// Generative AI · ML · LLM · Vision · Agents

Artificial Intelligence. // Build smarter. Scale faster. Win bigger.

Full-stack AI services — custom LLM applications, computer vision, NLP pipelines, ML model development, autonomous agents, RAG systems, and AI integration into any software. From first prototype to global production scale.

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// drowdigital.com — inference.py
1# Multi-modal AI pipeline
2from drowdigital import Pipeline, LLM
3from drowdigital.vision import VisionEncoder
4
5pipeline = Pipeline([
6 VisionEncoder(model="clip-vit-l"),
7 LLM(model="claude-3-5-sonnet",
8 stream=True, ctx=200000),
9])
10
11result = await pipeline.run(input)
GPT-4o
Claude 3.5
Llama 3.1
Gemini 2.0
<100ms
P95 Latency
99.9%
Uptime SLA
200K
Max Context
Large Language Models/
Generative AI/
Computer Vision/
NLP Pipelines/
AI Agents & Automation/
RAG & Knowledge Bases/
ML Model Development/
AI Fine-Tuning/
Predictive Analytics/
Speech AI/
Recommendation Engines/
MLOps & AI Ops/
Edge AI/
Responsible AI/
AI Strategy & Consulting/
Large Language Models/
Generative AI/
Computer Vision/
NLP Pipelines/
AI Agents & Automation/
RAG & Knowledge Bases/
ML Model Development/
AI Fine-Tuning/
Predictive Analytics/
Speech AI/
Recommendation Engines/
MLOps & AI Ops/
Edge AI/
Responsible AI/
AI Strategy & Consulting/
// What Are AI Services? — Direct Answer

AI services are end-to-end solutions that help businesses design, build, and deploy production AI systems.

AI services cover the full lifecycle of artificial intelligence projects — from problem definition and data preparation through model selection, training, evaluation, and production deployment. They include LLM application development (chatbots, copilots, AI agents), RAG systems for document intelligence, computer vision for image and video analysis, predictive machine learning for forecasting and classification, NLP pipelines for text understanding, and MLOps infrastructure to keep models reliable in production.

Unlike using off-the-shelf tools such as ChatGPT, a custom AI service is built on your proprietary data, integrated with your existing systems, and optimised for your specific business metric — whether that is fraud detection accuracy, demand forecast MAPE, or customer support ticket deflection rate. Businesses across healthcare, finance, e-commerce, manufacturing, legal, logistics, and technology sectors use AI services to build defensible competitive advantages at scale.

200+
AI SOLUTIONS SHIPPED
30+
COUNTRIES SERVED
10+
INDUSTRIES SERVED
4.9★
CLIENT RATING (47 REVIEWS)
2wk
FIRST DEMO DELIVERY
$0
FREE AI ASSESSMENT
01 — AI Services & Solutions

Every AI capability your business needs.

From LLM-powered applications to custom computer vision — we design, build, and deploy AI that creates measurable value, not just impressive demos.

LLMGenerative AI

LLM Application Development

Custom applications built on top of GPT-4o, Claude 3.5, Gemini 2.0, and open-source LLMs. Conversational AI, document Q&A, code generation, content creation, and complex reasoning applications — all with streaming, memory, and tool use.

Built on: OpenAI, Anthropic, Google, Cohere, Mistral, Meta Llama 3.1
RAGKnowledge AI

RAG & Knowledge Base Systems

Retrieval-Augmented Generation that lets AI answer questions from your proprietary data — internal documents, PDFs, databases, and knowledge bases. Hybrid search (semantic + keyword), re-ranking, and chunk-level source attribution for trusted answers.

Tools: LlamaIndex, LangChain, pgvector, Pinecone, Weaviate, Cohere Rerank
VisionImage AI

Computer Vision Systems

Object detection, image classification, semantic segmentation, optical character recognition (OCR), face recognition, quality inspection, medical image analysis, and real-time video analytics — deployed on cloud, on-premise, or edge devices.

Frameworks: YOLO, PyTorch, TensorFlow, OpenCV, AWS Rekognition, Google Vision API
AgentsAutonomous

AI Agents & Autonomous Workflows

Multi-step AI agents that plan, reason, and execute across tools and APIs — web browsing, code execution, file handling, database queries, and API calls. Build workflows that run autonomously with human-in-the-loop checkpoints where needed.

Frameworks: LangGraph, AutoGen, CrewAI, OpenAI Assistants, Claude Computer Use
NLPText AI

Natural Language Processing (NLP)

Text classification, sentiment analysis, named entity recognition (NER), relation extraction, summarisation, translation, topic modelling, and intent detection — from simple pipelines to complex multi-stage NLP systems at production scale.

Libraries: spaCy, HuggingFace Transformers, NLTK, Stanford NLP, AWS Comprehend
SpeechVoice AI

Speech AI & Voice Interfaces

Speech-to-text (STT) transcription, text-to-speech (TTS) synthesis, speaker diarisation, real-time voice AI for conversational interfaces, multilingual transcription (50+ languages), accent adaptation, and custom wake-word detection systems.

Tools: OpenAI Whisper, AssemblyAI, Deepgram, ElevenLabs, Azure Speech, LiveKit
PredictiveML

Predictive Analytics & ML Models

Custom ML models for demand forecasting, churn prediction, fraud detection, pricing optimisation, recommendation engines, anomaly detection, and risk scoring. Classical ML (XGBoost, Random Forest) and deep learning depending on data availability and latency needs.

Tools: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Prophet, AWS SageMaker
GenerativeImage/Video

Generative AI — Image, Video & 3D

AI-powered content generation pipelines — image generation (DALL-E 3, Stable Diffusion, Midjourney API), video generation (Sora, Runway, Kling), 3D model generation, product photography automation, and brand-consistent creative at scale.

Tools: DALL-E 3, Stable Diffusion, ComfyUI, ControlNet, Runway, Replicate API
Fine-TuningCustom Models

AI Fine-Tuning & Custom Model Training

Fine-tune foundation models on your proprietary data for domain-specific performance — legal, medical, financial, or technical language. LoRA and QLoRA for efficient fine-tuning. RLHF and DPO for alignment. Instruction tuning for custom behaviours.

Techniques: LoRA, QLoRA, PEFT, RLHF, DPO, SFT — on any open-source foundation model
Semantic SearchEmbeddings

Semantic Search & Embeddings

Replace keyword search with meaning-based retrieval. Generate and index embeddings for text, images, and structured data. Hybrid search combining semantic vectors with BM25 keyword search via Reciprocal Rank Fusion (RRF) for best-in-class recall.

Tools: OpenAI Embeddings, Cohere, BGE, E5, Qdrant, Weaviate, Elasticsearch, Typesense
AI AnalyticsNL-to-SQL

AI-Powered Analytics & Insights

Natural language analytics — users ask questions in plain English and get charts, summaries, and root cause analysis. Text-to-SQL (NL-to-SQL) for self-serve data exploration. AI-generated report narration and automated anomaly commentary.

Tools: Vanna.AI, NSQL, Claude Text2SQL, Power BI Copilot, Databricks AI/BI
IntegrationAI APIs

AI Integration & API Development

Integrate AI capabilities into your existing software stack — add LLM features to your web app, CRM, ERP, or mobile app via clean APIs. AI middleware, model routing, prompt management, cost tracking, and fallback strategy for production resilience.

Tools: Vercel AI SDK, LangChain, LiteLLM, Helicone, Portkey, OpenRouter
MLOpsAI Ops

MLOps & AI Infrastructure

End-to-end ML operations — experiment tracking, model registry, CI/CD for ML, automated retraining pipelines, model monitoring (drift detection, data quality), A/B testing between model versions, and cost optimisation for inference at scale.

Tools: MLflow, Weights & Biases, Kubeflow, SageMaker Pipelines, Vertex AI Pipelines
Edge AIOn-Device

Edge AI & On-Device Inference

AI that runs on device without internet — mobile (iOS/Android with Core ML, TFLite), embedded systems (Raspberry Pi, NVIDIA Jetson), and IoT devices. Model compression, quantisation (INT8/INT4), and pruning to fit powerful AI within hardware constraints.

Tools: Core ML, TFLite, ONNX, TensorRT, NVIDIA Jetson, OpenVINO, LLM.swift
Responsible AISafety

Responsible AI & AI Safety

Bias detection and mitigation, fairness auditing, explainability (SHAP, LIME, GradCAM), privacy-preserving ML (differential privacy, federated learning), hallucination evaluation and mitigation, AI red-teaming, and AI governance framework design.

Tools: Fairlearn, SHAP, Evidently AI, Giskard, Guardrails AI, LLM-Guard, Ragas
Domain AIVertical

Domain-Specific AI Solutions

Vertical AI for specific industries — medical AI (radiology, pathology, clinical decision support), legal AI (contract analysis, case research), financial AI (credit scoring, market prediction), and manufacturing AI (quality control, predictive maintenance).

Sectors: Healthcare, Legal, Finance, Manufacturing, Retail, Real Estate, Agriculture
Data EngineeringAI Data

AI Data Engineering & Data Pipelines

Build the data foundation AI requires — data collection and labelling automation, annotation pipelines (Label Studio, Scale AI), synthetic data generation, data cleaning and deduplication, feature engineering, and active learning loops to continuously improve model quality.

Tools: Label Studio, Scale AI, Roboflow, dbt, Airflow, Spark, Databricks
AI StrategyConsulting

AI Strategy & Consulting

AI readiness assessments, opportunity identification (where AI creates most value in your business), build-vs-buy analysis, AI product roadmap design, team upskilling programmes, and AI governance policy development for boards and executive teams.

Deliverables: AI readiness report, opportunity map, vendor evaluation, AI roadmap, ROI model
02 — AI Models & Foundation Models

The full model landscape.

We're model-agnostic. We choose the right model for your task, latency, cost, and privacy requirements — not the one we're most familiar with.

OpenAI

GPT-4o & GPT-4o-mini

OpenAI's flagship multimodal models — text, vision, audio, and structured output. Function calling, Assistants API with file search and code interpreter, Batch API for cost-efficient large-scale inference, and fine-tuning for domain specialisation.

Context128K tokens
ModalitiesText, Vision, Audio
Best forGeneral reasoning, multimodal
Fine-tuning✓ Available
Anthropic

Claude 3.5 Sonnet & Haiku

Claude's extended 200K context window excels at long document analysis, complex reasoning, and code generation. Computer Use feature for autonomous UI interaction. Extended thinking for multi-step problems. Best for document intelligence and code.

Context200K tokens
SpecialComputer Use, Extended Thinking
Best forDocuments, code, long context
Fine-tuningComing soon
Google

Gemini 2.0 Flash & Pro

Google's multimodal frontier model — native audio, video, image, text, and code understanding. Gemini 2.0 Flash with real-time streaming and tool use. Deep Google ecosystem integration (Search, Workspace, Maps). Vertex AI for enterprise deployment.

Context2M tokens (Flash)
ModalitiesText, Vision, Audio, Video
Best forMultimodal, real-time, long video
PlatformVertex AI
Meta

Llama 3.1 / 3.2 / 3.3

Meta's open-weight models — deploy on your own infrastructure for complete data privacy. Llama 3.3 70B rivals GPT-4 on many benchmarks. Llama 3.2 multimodal for vision tasks. Fine-tune on your data with LoRA. Run via Ollama, vLLM, or Together AI.

Sizes1B to 405B parameters
LicenceOpen-weight (free)
Best forPrivacy, self-hosted, fine-tuning
Deploy viaOllama, vLLM, Together
Mistral AI

Mistral Large 2 & Mixtral

European AI frontier with strong multilingual capabilities (13 languages), excellent code generation, and function calling. Mixtral-8x22B Mixture-of-Experts for high throughput. Mistral Small for cost-efficient high-volume tasks. GDPR-compliant EU data residency.

Context128K tokens
Languages13 native languages
Best forEU compliance, multilingual, code
Fine-tuning✓ Available
Cohere

Command R+ & Embed v3

Enterprise-grade RAG specialist — Command R+ with native multi-document RAG, grounded generation with source citations, and web search connector. Embed v3 for best-in-class embeddings. Rerank v3 for relevance re-ranking. Strong enterprise SLA and compliance.

Context128K tokens
SpecialNative RAG, web search, grounding
Best forEnterprise RAG, search, reranking
Fine-tuning✓ Available
Specialized Vision

CLIP / SAM / DINO / YOLO

Specialised vision models for specific tasks — CLIP for zero-shot image classification and text-image search, SAM (Segment Anything) for universal image segmentation, DINOv2 for visual features, and YOLO series for real-time object detection at 120+ FPS.

CLIPZero-shot classification
SAMUniversal segmentation
YOLOv10Real-time detection
DINOv2Visual features/embedding
Speech & Audio

Whisper / Deepgram / ElevenLabs

Specialised audio AI — OpenAI Whisper for best-in-class multilingual transcription (99 languages), Deepgram Nova-2 for real-time streaming STT with diarisation, ElevenLabs for ultra-realistic TTS with voice cloning, and AssemblyAI for enterprise speech analytics.

Whisper99-language transcription
DeepgramReal-time STT, diarisation
ElevenLabsVoice cloning, TTS
AssemblyAIEnterprise speech analytics
Generative Vision

Stable Diffusion / FLUX / Sora

Generative image and video models — FLUX.1 for state-of-the-art image generation, Stable Diffusion 3.5 for open-source image generation with ControlNet, Sora and Runway for AI video generation, and Meshy/TripoSR for 3D model generation from text or images.

FLUX.1Best image quality 2025
SD 3.5Open-source, self-hosted
Sora/RunwayVideo generation
Meshy3D model generation
03 — AI Technology Stack

Tools we use to build AI that works.

Best-in-class tools for every stage of the AI development lifecycle — from data preparation to model deployment and monitoring in production.

LangChain / LangGraph
LangChain for building LLM chains, agents, and tools with 100+ integrations. LangGraph for stateful, cyclical multi-agent workflows with checkpointing and human-in-the-loop nodes — the right framework for complex autonomous agent systems that need reliable state management.
Primary OrchestrationAgentsLangGraph100+ Integrations
LlamaIndex
Specialised for data-to-LLM pipelines — document ingestion (80+ file types), chunking strategies, embedding pipelines, advanced retrieval (fusion, HyDE, step-back), query engines, and sub-question decomposition. Our primary framework for production RAG systems.
RAG Specialist80+ File TypesAdvanced Retrieval
AutoGen / CrewAI
Multi-agent frameworks — AutoGen (Microsoft) for conversational agents that collaboratively solve tasks. CrewAI for role-based agent crews with structured workflows. Used for complex AI pipelines where specialised agents (researcher, writer, reviewer) collaborate on tasks.
Multi-AgentAutoGenCrewAIRole-Based
Vercel AI SDK
TypeScript-first SDK for building AI-powered web applications — unified API for GPT-4o, Claude, Gemini, and Mistral. Streaming responses with React Server Components, useChat and useCompletion hooks, and structured object generation. Our default for Next.js AI apps.
Next.js AIStreamingTypeScriptMulti-Model
LiteLLM / OpenRouter
Model routing and cost management — LiteLLM provides a unified OpenAI-compatible API for 100+ models. OpenRouter for accessing all frontier models through one API key with automatic failover. Critical for production apps that need model flexibility and cost control.
Model Router100+ ModelsFailoverCost Control
DSPy / Instructor
DSPy for declarative, optimised prompt programming — replaces manual prompt engineering with compiled, optimised prompts. Instructor for reliable structured output extraction (Pydantic) from any LLM. Used when prompt reliability and structured JSON output are critical.
Prompt OptDSPyInstructorStructured Output
PyTorch
Our primary deep learning framework — dynamic computation graph for research flexibility, TorchScript for production deployment, PyTorch Lightning for clean training loops, torchvision for computer vision, torchaudio for speech, and torch.compile for 2× speedups.
Primary DLLightningtorchvisiontorch.compile
HuggingFace Transformers
The definitive hub for pre-trained models — 500,000+ models, datasets library, PEFT for parameter-efficient fine-tuning (LoRA, QLoRA, Prefix Tuning), TRL for RLHF and DPO alignment training, Accelerate for distributed training, and Inference Endpoints for deployment.
HuggingFacePEFT/LoRATRL/RLHF500K+ Models
scikit-learn / XGBoost
Classical ML for structured data — scikit-learn for the full ML pipeline (preprocessing, feature selection, model training, cross-validation). XGBoost and LightGBM for gradient boosting on tabular data (often outperforms deep learning on structured datasets). Fast and interpretable.
Classical MLXGBoostLightGBMTabular Data
TensorFlow / Keras
TensorFlow for production deployment (TF Serving, TFX pipeline, SavedModel format), edge deployment (TFLite), and Google ecosystem integration. Keras 3.0 as the high-level multi-backend API works on TensorFlow, JAX, and PyTorch backends.
TF ServingTFLiteKeras 3TFX Pipeline
JAX / Flax
Google's high-performance numerical computing for ML research — automatic differentiation, XLA compilation, pmap for multi-device training, and vmap for vectorisation. Used for custom training loops that need maximum control and performance on TPUs and GPU clusters.
Research MLTPU NativeXLAHigh Performance
Prophet / statsmodels
Time-series forecasting — Facebook Prophet for automated seasonality detection and trend forecasting with uncertainty intervals. statsmodels for classical statistical models (ARIMA, SARIMA, VAR). NeuralProphet for hybrid neural/statistical forecasting. Used for demand forecasting, financial modelling.
ForecastingProphetARIMANeuralProphet
pgvector (PostgreSQL)
Our primary vector database for most production RAG systems — native Postgres extension for vector similarity search (L2, cosine, inner product), HNSW and IVFFlat indexes, hybrid search combining SQL filtering with vector search. Collocates your data and embeddings in one DB.
Primary VectorDBPostgres NativeHybrid SearchHNSW
Pinecone
Managed vector database for high-scale production — serverless scaling to billions of vectors, real-time upserts, metadata filtering, namespaces for multi-tenancy, and sparse-dense hybrid search. Zero ops overhead. Our choice when vector scale exceeds pgvector's sweet spot.
Managed ScaleServerlessHybrid SearchMulti-tenant
Weaviate
Open-source vector database with GraphQL API — modular vectoriser (text2vec-openai, text2vec-cohere), multi-modal search (image + text), BM25 + vector hybrid search, and generative search that pipes retrieved context directly into LLM for in-database RAG.
Open SourceMulti-ModalGraphQLGenerative Search
Qdrant
Rust-based vector database optimised for high-performance production — payload filtering, quantisation (scalar, binary) for 30× memory reduction, distributed mode, named vectors for multi-vector search, and sparse vectors for hybrid search. Excellent performance-per-dollar.
High PerformanceRustQuantisationSparse Vectors
Redis Vector / Elasticsearch
Redis Vector Sets for blazing-fast in-memory vector search (sub-millisecond). Elasticsearch with kNN vector search for organisations already running Elastic — add semantic search to existing full-text infrastructure without a new database. OpenSearch as AWS-native alternative.
RedisElasticsearch kNNIn-MemoryExisting Stack
Databricks / Spark
Large-scale data processing for AI pipelines — Apache Spark for distributed data cleaning and feature engineering, Delta Lake for ACID transactions on data lakes, MLflow integration for experiment tracking, and Unity Catalog for data governance across ML assets.
Big DataSparkDelta LakeUnity Catalog
AWS AI Services
Full AWS AI stack: SageMaker for end-to-end ML (training, tuning, deployment), Bedrock for managed LLM access (Claude, Llama, Titan), Rekognition for vision, Comprehend for NLP, Textract for OCR, Forecast for time-series, and Polly/Transcribe for speech.
SageMakerBedrockRekognitionTextract
Google Vertex AI
Google's unified ML platform — Gemini 2.0 via API, AutoML for no-code model training, Model Garden for 150+ curated models, Vertex AI Agent Builder for no-code agents, Feature Store, Model Registry, and Pipelines for MLOps. Best for Gemini models and TPU training.
Vertex AIGemini APIAutoMLTPU Access
Azure OpenAI Service
Enterprise-grade access to OpenAI models within Azure's compliance boundary — HIPAA, SOC 2, GDPR, and FedRAMP compliant. Private networking (VNet), customer-managed keys, content filtering, and Azure Active Directory authentication. Essential for regulated industries.
Enterprise GPTHIPAA/GDPRPrivate NetworkAzure Entra
Modal / RunPod / Lambda Labs
GPU cloud for ML workloads — Modal for serverless GPU functions (pay per second, zero cold starts), RunPod for flexible GPU pods and serverless endpoints, Lambda Labs for H100 clusters at competitive pricing. Used for fine-tuning and running open-source model inference.
GPU CloudModalRunPodH100/A100
Ollama / LM Studio
Run open-source LLMs locally — Ollama for server deployment with REST API (Llama, Mistral, Phi-3, Gemma, Code Llama). LM Studio for developer-friendly desktop inference. Critical for air-gapped environments, cost reduction, and development without API rate limits.
Local LLMOllamaPrivacyZero API Cost
vLLM / TGI
High-throughput LLM serving — vLLM with PagedAttention for 24× higher throughput than naive inference, continuous batching, speculative decoding, and tensor parallelism for multi-GPU serving. Text Generation Inference (HuggingFace) for production-grade open model deployment.
High-ThroughputvLLMPagedAttentionMulti-GPU
MLflow
Open-source MLOps platform — experiment tracking (parameters, metrics, artifacts), model registry with versioning and stage transitions, model serving, and MLflow Projects for reproducible training runs. Integrates with SageMaker, Databricks, Azure ML, and Google Vertex AI.
Experiment TrackingModel RegistryOpen Source
Weights & Biases
ML experiment tracking with beautiful dashboards — real-time metrics, gradient histograms, sample predictions, hyperparameter sweeps (Bayesian optimisation), and Artifacts for dataset versioning. W&B Weave for LLM tracing. Used by 1M+ ML practitioners worldwide.
W&BSweepsArtifactsWeave LLM
Kubeflow / Airflow
ML pipeline orchestration — Kubeflow Pipelines for Kubernetes-native ML workflows (training → evaluation → deployment), automated retraining on data drift, component caching, and artifact lineage. Apache Airflow for scheduling complex ML pipelines with dependency management.
Pipeline OrchestrationKubeflowK8s Native
Evidently AI
ML model monitoring in production — data drift detection (statistical tests), prediction drift, feature distribution shifts, data quality checks, and performance degradation alerts. Generates HTML reports and JSON metrics. Used to catch when a deployed model needs retraining.
Model MonitoringDrift DetectionData Quality
Langfuse / Helicone
LLM observability — Langfuse for open-source LLM tracing (full prompt/response logging, cost tracking, user feedback, session management, and eval runs). Helicone as AI gateway with caching (10× cost savings), rate limiting, and real-time analytics. Essential for production LLM apps.
LLM TracingLangfuseHeliconeCost Tracking
DVC (Data Version Control)
Git for data and models — track large datasets and model files in Git without bloat, reproduce any experiment, compare experiments in CI/CD, and share datasets across teams. Works with any cloud storage (S3, GCS, Azure Blob) and any ML framework.
Data VersioningDVCReproducibility
Ragas
RAG evaluation framework — measures faithfulness (is the answer grounded in context?), answer relevance, context precision, context recall, and context entity recall. Uses LLMs as judges for automated RAG quality assessment without requiring labelled test sets.
RAG EvaluationFaithfulnessLLM-as-Judge
DeepEval
Comprehensive LLM evaluation — hallucination detection, toxicity, bias, answer relevancy, GEval (custom metrics with LLM judge), red-teaming with adversarial inputs, and regression testing in CI/CD. Integrates with pytest for automated evaluation on every code change.
LLM EvalHallucinationRed-Teamingpytest
Guardrails AI / LLM-Guard
Input/output validation for LLMs — Guardrails AI defines validation rules (no PII in output, valid JSON schema, output relevance) applied to every LLM call with retry logic. LLM-Guard for prompt injection detection, toxicity filtering, and sensitive data anonymisation.
Input/Output GuardPII DetectionPrompt Injection
SHAP / LIME
Model explainability — SHAP (SHapley Additive exPlanations) for feature importance across tree models and neural networks. LIME (Local Interpretable Model-agnostic Explanations) for explaining individual predictions. Critical for regulated industries (healthcare, finance) requiring explainable AI.
ExplainabilitySHAPLIMEXAI
Giskard
AI testing for ML models and LLMs — automated vulnerability scanning (bias, robustness, data leakage), LLM red-teaming, and continuous testing in CI/CD. Generates adversarial test cases from your data to stress-test models before deployment in production.
AI TestingBias DetectionRobustness
Promptfoo
LLM testing and evaluation — test prompts against multiple models simultaneously, define pass/fail criteria with LLM judges, run automated regression tests when models update, and red-team prompts for safety vulnerabilities. CLI-based, integrates with CI/CD pipelines seamlessly.
Prompt TestingMulti-ModelRed-TeamingCI/CD
OpenCV
The foundational computer vision library — image preprocessing, geometric transformations, video capture and processing, feature detection (SIFT, ORB), optical flow, camera calibration, and traditional CV algorithms. Used in every CV pipeline as the base image processing layer.
Foundation CVImage ProcessingVideo
Ultralytics YOLO
The fastest real-time object detection — YOLOv10 and YOLO11 for detection, segmentation, pose estimation, classification, and tracking. Ultralytics Hub for cloud training and deployment. Mobile export to CoreML, TFLite, ONNX. Used for manufacturing inspection and security cameras.
YOLO11Real-TimeSegmentationTracking
SAM (Segment Anything)
Meta's universal segmentation model — segment any object in any image with a point, box, or text prompt. SAM 2 extends to video with real-time tracking. Used for data annotation automation, medical image segmentation, and interactive image editing applications.
Universal SegmentationSAM 2Video Tracking
Grounding DINO / Florence-2
Open-vocabulary detection — Grounding DINO detects any object from a text description, eliminating the need for category-specific training. Microsoft Florence-2 for unified visual understanding (detection, captioning, VQA, OCR) with a single model. For zero-shot and few-shot detection tasks.
Open VocabularyZero-ShotFlorence-2
Tesseract / PaddleOCR
Optical character recognition — Tesseract (open-source, 100+ languages) for offline document OCR. PaddleOCR for Chinese-language documents and complex layouts. AWS Textract for cloud-scale document processing (forms, tables, handwriting). Azure Form Recogniser for structured form extraction.
OCRTesseractPaddleOCRAWS Textract
Roboflow
End-to-end computer vision platform — dataset management, annotation tools, augmentation, model training (AutoML), version control, and deployment (cloud API, edge, mobile). Roboflow Universe with 250K+ public datasets. Used to accelerate CV projects from labelling to deployment.
CV PlatformAnnotationAutoML250K Datasets
FastAPI + async
Our primary framework for AI API services — async endpoints for streaming LLM responses, background tasks for async inference, Pydantic v2 for request/response validation, automatic OpenAPI docs, and WebSocket support for real-time AI features. Deployed on uvicorn with gunicorn.
AI API ServerStreamingAsyncWebSocket
Triton Inference Server
NVIDIA's production model serving — supports PyTorch, TensorFlow, ONNX, TensorRT, and Python backends in one server. Dynamic batching, model ensembles, model versioning, and gRPC/REST APIs. Achieves near-hardware-limit throughput for vision and NLP models at scale.
GPU ServingDynamic BatchingTensorRTgRPC
BentoML
ML model packaging and serving — define a service once, deploy to cloud (AWS, GCP, Azure), Kubernetes, or as a Docker container. Adaptive batching, runner architecture for GPU/CPU separation, built-in monitoring, and one-command deployment to BentoCloud.
Model ServingBentoCloudAdaptive Batching
Ray Serve
Scalable ML serving with Ray — compose multiple models (preprocessing + inference + postprocessing) as a deployment graph. Auto-scaling, fractional GPU support (multiple models per GPU), batching, and actor-based parallelism. Used for complex multi-model serving architectures.
Distributed ServingAuto-ScaleRayMulti-Model
Core ML / TFLite
On-device model deployment — Core ML for iOS/macOS with Neural Engine acceleration, Create ML for training directly on Mac. TFLite for Android and embedded devices with NNAPI. ONNX as the interoperability format to export from PyTorch/TF and deploy anywhere.
On-DeviceCore MLTFLiteONNX
Replicate API
Serverless model hosting — run any open-source model (FLUX, Stable Diffusion, Whisper, LLaVA, YOLO) via REST API without managing GPU infrastructure. Automatic scaling, cost-per-prediction billing, and Cog for packaging custom models as Replicate deployments.
Serverless InferenceOpen ModelsAuto-Scale
04 — AI Use Cases by Industry

AI that creates real business value.

Every industry has specific AI opportunities. Here's where AI delivers the clearest ROI — with examples from our actual client work.

Healthcare & Life Sciences

Medical image analysis (radiology, pathology, dermatology), clinical NLP for EHR data extraction, drug discovery assistance, clinical trial matching, patient triage chatbots, and AI-powered clinical decision support systems. HIPAA-compliant deployment on private infrastructure.

Medical VisionClinical NLPDrug DiscoveryHIPAA

Financial Services & FinTech

Fraud detection (real-time transaction scoring), credit risk modelling, algorithmic trading signals, regulatory compliance document review, KYC document processing (OCR + NLP), customer support automation, and AI-powered financial advisor chatbots with product recommendations.

Fraud DetectionCredit RiskKYC Automation

E-Commerce & Retail

Personalised product recommendation engines, visual search (find product by photo), dynamic pricing optimisation, demand forecasting, AI customer support, return prediction, inventory anomaly detection, and generative AI for product descriptions and imagery at scale.

RecommendationsVisual SearchDynamic Pricing

Manufacturing & Industry

Visual quality inspection (defect detection at line speed), predictive maintenance (equipment failure prediction 2–4 weeks ahead), digital twin AI, production scheduling optimisation, supply chain disruption prediction, and worker safety monitoring using computer vision.

Quality VisionPredictive MaintenanceDigital Twin

Legal & Compliance

Contract analysis and risk extraction, legal document Q&A (RAG over case law and precedents), regulatory change monitoring, due diligence automation, GDPR compliance document scanning, e-discovery acceleration, and legal research assistant with jurisdiction-aware reasoning.

Contract AILegal RAGCompliance

Education & EdTech

Personalised learning paths (adaptive curriculum based on student performance), AI tutoring systems, automated essay grading, question generation from course content, teacher productivity tools (lesson plan generation), accessibility features (real-time captioning, translation), and student engagement analytics.

AI TutoringAdaptive LearningAuto-Grading

Media & Content

AI content generation pipelines (articles, social media, product descriptions), video transcription and multilingual subtitles, content moderation at scale, personalised content feeds, SEO content optimisation, podcast summarisation, and brand voice enforcement in AI-generated content.

Content GenModerationPersonalisation

Logistics & Supply Chain

Route optimisation with real-time traffic and weather, demand forecasting for inventory, supplier risk intelligence, document processing (bills of lading, customs documents), predictive ETAs, warehouse robotics vision systems, and last-mile delivery optimisation models.

Route OptimisationDemand ForecastDocument AI

Construction & Real Estate

Drone imagery analysis for site progress monitoring, safety compliance detection (PPE detection via CV), cost estimation from building plans, property valuation models, document extraction from construction contracts, and AI-powered property search with natural language queries.

Site VisionSafety DetectionValuation ML

Agriculture & AgriTech

Crop disease detection from drone imagery, yield prediction models, precision irrigation optimisation, pest outbreak prediction, soil quality analysis from satellite data, livestock monitoring and behaviour detection, and AI-powered commodity price forecasting for hedging decisions.

Crop VisionYield PredictionPrecision Ag

SaaS & Technology

AI-powered code review (security, quality, suggestions), automated test generation, documentation generation from code, developer copilots (IDE integrations), AI customer support (resolve 60-70% of tickets), churn prediction, feature adoption analytics, and AI usage analytics dashboards.

Code AISupport AutomationChurn Prediction

Hospitality & Travel

Dynamic pricing optimisation (revenue management), personalised travel recommendation engines, sentiment analysis on reviews at scale, AI-powered concierge chatbots, demand forecasting for staffing, fraud detection in bookings, and AI-generated personalised itineraries.

Dynamic PricingReview AnalyticsAI Concierge
06 — AI Engagement Plans

Start anywhere. Scale everywhere.

From a focused AI prototype to a full enterprise AI programme. Every engagement includes a complimentary AI opportunity assessment.

AI Proof of Concept
// Validate before you invest
$8K – $25K
✓4–6 week delivery
✓1 focused AI use case
✓Model selection & benchmarking
✓Working demo on your data
✓Evaluation report with metrics
✓Architecture recommendation
✓ROI model for full build
—Not production-ready
Start PoC
AI Feature Build
// Ship AI into your product
$25K – $100K
✓8–16 week delivery
✓1–3 production AI features
✓LLM app, RAG, or CV system
✓Production API + streaming
✓Evaluation suite + monitoring
✓LLM observability (Langfuse)
✓Integration into your stack
✓30-day post-launch support
Build AI Feature
MOST COMPREHENSIVE
AI Platform Build
// Full AI product or platform
$100K+ custom scope
✓3–9 month programme
✓Full AI product or suite
✓Custom ML model training
✓Fine-tuning (LoRA/QLoRA)
✓Multi-modal (text + vision + voice)
✓Full MLOps pipeline
✓AI agents & autonomous workflows
✓Enterprise security + compliance
✓Dedicated ML team
✓Ongoing model improvement
Build AI Platform
AI Retainer
// Ongoing AI team
$8K/month+
✓Dedicated AI engineer(s)
✓Model monitoring & updates
✓Prompt optimisation
✓New AI feature velocity
✓Model benchmarking (new releases)
✓Cost optimisation reviews
✓Monthly AI strategy call
✓Priority response
Get Retainer

Add-On AI Services

AI Opportunity Audit
Free with any plan
Where AI creates most value in your biz
Model Fine-Tuning
$8K – $40K
LoRA/QLoRA fine-tuning on your domain data
AI Training & Workshop
$2K – $8K
Team upskilling: prompt engineering, LLMs, RAG
AI Governance Review
$3K – $12K
Bias audit, explainability report, safety review
07 — AI Case Studies

AI solutions that shipped and scaled.

Six AI projects across six industries — real technical complexity, real business impact.

LLM + RAG — Legal SaaS

LexAI — Legal Document Intelligence

Enterprise RAG system for a legal SaaS — 50M+ pages of case law, contracts, and regulatory documents searchable in natural language with source citations

Documents indexed50M+ pages
Answer accuracy (eval set)96.8% faithfulness
Research time reduction78% faster
Query latency (p95)2.1 seconds
Claude 3.5pgvectorCohere RerankLlamaIndex
Computer Vision — Manufacturing

InspectAI — PCB Defect Detection

Real-time visual quality inspection on electronics PCB production line — detecting solder defects, missing components, and alignment errors at 120 boards/minute

Defect detection rate99.4% (vs 94% manual)
Inspection speed120 boards/minute
False positive rate0.3%
Annual savings$2.8M in rework
YOLOv10PyTorchTriton ServingEdge GPU
AI Agents — E-Commerce

ShopMind — Autonomous Product Manager

Multi-step AI agent that autonomously monitors competitor pricing, generates repricing recommendations, updates product listings, and drafts weekly performance reports

Tasks automated per week2,400+ actions
Revenue lift (A/B test)+22% from dynamic pricing
Staff hours saved/week60 hours
Accuracy of recommendations91%
GPT-4o + ToolsLangGraphLangfuseFastAPI
Speech AI — HealthTech

ClinicalScribe — Medical Dictation AI

AI-powered clinical documentation — doctors dictate notes in real time, AI transcribes, structures into SOAP format, extracts diagnoses and medications, and auto-populates EHR fields

Transcription accuracy98.2% (medical vocab)
Documentation time12min → 90 seconds
Physicians onboarded1,200+
Notes per day8,400+ processed
Whisper MedicalClaude 3.5DeepgramHIPAA
Predictive ML — Retail

DemandSense — Inventory AI

Demand forecasting ML for a 12,000-SKU retailer — predicting 8-week demand at SKU × store level incorporating seasonality, promotions, weather, and local events

Forecast accuracy (MAPE)8.4% vs 18.2% baseline
Stock-out reduction-44%
Overstock reduction-31%
Working capital saved₹4.2Cr/year
LightGBMProphetMLflowEvidently AI
Generative AI — FinTech

DocExtract — Financial Document AI

Intelligent document processing for a lending platform — extracting structured data from bank statements, pay slips, ITRs, and balance sheets to automate credit underwriting decisions

Document types supported24 types, 6 languages
Extraction accuracy97.3%
Processing time4 min → 8 seconds
Manual review reduction-84%
GPT-4o VisionClaudeAWS TextractInstructor
08 — Client Testimonials

What clients actually say.

★★★★★

"Drow Digital built us a RAG system over 50 million pages of legal documents that answers questions with citations in under 2 seconds. Our lawyers reduced research time by 78% in the first month. Three competitors have tried to replicate it and failed. The evaluation framework they built — testing faithfulness before any answer reaches a user — is what makes it trustworthy enough for legal work."

JL
James L.
CTO, LexAI Legal Platform
★★★★★

"The PCB defect detection AI catches 99.4% of defects at 120 boards per minute — better than our best human inspector at 10× the speed. What sold me was their approach: they built the evaluation framework first, ran it against our historical defect database, and showed me the numbers before we deployed anything to the line. That rigour is rare."

RK
Ramesh K.
VP Operations, Electronics Manufacturer
★★★★★

"Our doctors were spending 2 hours per day on clinical documentation. Drow Digital's medical dictation AI cut that to 15 minutes. The accuracy on medical terminology — drug names, dosages, procedure codes — was the hard part. They fine-tuned Whisper on our anonymised note corpus and the results were immediately usable in production. 1,200 physicians onboarded in 3 months."

SM
Dr. Sarah M.
Chief Medical Officer, HealthTech Platform
★★★★★

"Our demand forecasting was Excel-based with 18.2% MAPE — good enough until we scaled to 12,000 SKUs across 80 stores. Drow Digital built an ensemble ML system that hit 8.4% MAPE, cutting stockouts by 44% and saving ₹4.2 crore in working capital in year one. The ROI was 12× in 9 months. I only wish we'd done this 3 years ago."

VP
Vivek P.
CEO, DemandSense Retail
09 — FAQ

Questions we get every day.

What's the difference between using ChatGPT and building a custom AI solution?
+
ChatGPT is a general-purpose consumer product. A custom AI solution is built for your specific use case, trained or fine-tuned on your data, integrated with your existing systems, and deployed under your control. The differences matter in practice: (1) Accuracy — a model fine-tuned on your legal, medical, or technical domain dramatically outperforms a general model on domain-specific tasks. (2) Data privacy — your proprietary data doesn't go to OpenAI's training servers when you use a self-hosted model. (3) Integration — custom solutions pull from your databases, CRM, and documents in real time. (4) Cost control — at scale, a fine-tuned smaller model costs 10–100× less than GPT-4 for the same task. ChatGPT is great for prototyping and individual use; custom AI is what creates defensible business value.
How much data do I need to build a custom AI model?
+
It depends on what you're building. For RAG systems (document Q&A): your existing documents are the data — even 100 documents can work for a pilot. For LLM fine-tuning (customising model behaviour): 100–1,000 high-quality examples is often sufficient with LoRA/QLoRA. For classical ML models (fraud detection, demand forecasting): typically 10,000–100,000 labelled examples minimum. For computer vision (object detection, classification): 500–5,000 labelled images for specific domains with data augmentation; more for complex real-world variation. We assess your data situation in week 1 of every engagement — if data is insufficient, we explore synthetic data generation, active learning, or alternative approaches before committing to a direction.
What's RAG and do I need it?
+
RAG (Retrieval-Augmented Generation) is the technique of giving an LLM access to your specific documents and data at query time — rather than relying on its training data alone. You need RAG when: (1) your questions require up-to-date or private information the LLM wasn't trained on, (2) you need answers with verifiable source citations (not just the model's opinion), (3) your knowledge base is too large to fit in a single context window, or (4) you need to avoid hallucinations about your specific products, policies, or processes. You probably don't need RAG when: you just need general reasoning capability, code generation, content creation, or summarisation of content you provide in the prompt. About 70% of the AI projects we see benefit from RAG — it's the most impactful pattern for real business knowledge use cases.
How do you prevent AI hallucinations?
+
Hallucination is a fundamental property of LLMs — they generate plausible-sounding text even when uncertain. We use multiple complementary techniques to minimise it in production: (1) Grounding — RAG with context forces the model to answer from retrieved documents, not from training memory. (2) Faithfulness evaluation — Ragas measures whether answers are actually supported by the retrieved context (we set minimum thresholds). (3) Structured output — using Instructor/Pydantic to constrain outputs to valid formats reduces fabrication. (4) Uncertainty signalling — prompting models to express uncertainty rather than confidently fabricate. (5) Guardrails — input/output validation that checks answers against source documents before delivery to users. (6) Human-in-the-loop — for high-stakes decisions, the AI flags confidence level and humans review borderline cases.
Should I use OpenAI/Claude APIs or self-host open-source models?
+
Both are valid — the right choice depends on your requirements: Use managed APIs (OpenAI, Anthropic, Google) when you need maximum model quality (frontier models genuinely outperform open-source for complex reasoning), fast time-to-market matters more than cost optimisation, and your data handling requirements are met by their enterprise terms. Self-host open-source models (Llama, Mistral, Phi-3) when you have strict data privacy requirements (healthcare, legal, government), high volume makes API costs prohibitive (self-hosted can be 10-50× cheaper at scale), you need fine-tuning on proprietary data without sharing it with third parties, or you need air-gapped deployment with no internet dependency. In practice, most of our production systems use a hybrid approach — managed APIs for complex reasoning tasks, self-hosted models for high-volume simpler tasks, and model routing (LiteLLM) to balance cost and quality automatically.
How long does it take to ship an AI feature to production?
+
It varies significantly by complexity: A basic LLM feature (chatbot, text summarisation, classification): 4–8 weeks to production. A production RAG system with evaluation and monitoring: 8–16 weeks. A computer vision system (data labelling + training + deployment): 10–20 weeks. A custom ML model (predictive analytics): 12–20 weeks including data work. A fine-tuned LLM on your domain data: 4–8 weeks (if data is ready). The biggest variable is data readiness — companies that have clean, labelled, well-organised data move 2–3× faster than those starting from scratch. The second biggest is evaluation definition — teams that know exactly what "good" looks like before building ship faster than those who discover requirements iteratively.
How do you ensure AI models stay accurate after deployment?
+
Model performance degrades in production for several reasons: data distribution shifts (your users behave differently from training data), model updates from API providers (GPT-4 today ≠ GPT-4 in 6 months), and real-world edge cases not seen in training. Our production AI monitoring stack: (1) LLM tracing (Langfuse) — log every prompt and response for analysis; (2) Data drift detection (Evidently AI) — statistical tests alert when input distributions shift significantly; (3) Performance metrics — track accuracy, latency, cost, and user feedback over time with automated alerts on degradation; (4) Regular eval runs — run the full evaluation suite weekly against a fixed test set; (5) Automated retraining — for ML models, trigger retraining when performance drops below thresholds. For LLM apps, we run regression tests on every model API update to catch prompt behaviour changes before they reach users.
Can you build AI that runs fully on-premise without sending data to any cloud?
+
Yes — this is a growing requirement in healthcare, legal, defence, and financial services. Our air-gapped AI stack: LLMs: Llama 3.1, Mistral, or Phi-3 deployed via Ollama or vLLM on your on-premise GPU servers. Embeddings: BGE-M3 or E5 models running locally — no API calls. Vector database: pgvector (PostgreSQL) or Qdrant self-hosted. Computer vision: YOLO, SAM, or custom PyTorch models on local inference hardware. Hardware: NVIDIA A100/H100 for large models, RTX 4090 for smaller models, and NVIDIA Jetson for edge inference. Performance trade-off: self-hosted Llama 3.3 70B matches GPT-4 on many tasks — the quality gap has narrowed significantly. For tasks requiring frontier model capability, we design hybrid architectures where sensitive data stays on-premise and only non-sensitive metadata goes to cloud APIs.
What is the best AI development company for custom AI projects?
+
The best AI development company for your project depends on your specific requirements. Key criteria: (1) Domain experience — do they have case studies in your industry (healthcare, finance, retail, manufacturing)? (2) Technical breadth — can they handle the full AI stack including data, model, API, and monitoring? (3) Evaluation rigour — do they define success metrics before building? (4) Production track record — have they shipped AI to real users, not just demos? (5) Transparency — can they explain why a model makes a decision? Drow Digital has shipped 200+ production AI solutions across 30+ countries with a 4.9-star client rating. Every new project starts with a free AI opportunity assessment — we scope the work correctly before any money is spent.
What is generative AI and how is it different from traditional AI?
+
Generative AI refers to AI models that create new content — text, images, video, audio, and code — rather than simply classifying or predicting from existing content. Examples include GPT-4o and Claude (text), DALL-E 3 and FLUX (images), Sora (video), and ElevenLabs (voice synthesis). Traditional or discriminative AI takes existing content as input and produces a classification or numerical prediction — for example, a spam classifier, a demand forecasting model, or a manufacturing defect detection system. Both have distinct business value. Most production AI systems combine both types: a generative LLM for language and reasoning tasks, and discriminative ML models for structured prediction problems. Drow Digital designs and builds both types, and creates hybrid architectures where appropriate.
Should I build AI in-house or hire an AI development company?
+
Build in-house when: AI is core to your long-term competitive advantage, you have an existing ML team with sufficient capacity, and you can afford a 6–12 month senior ML engineer hiring cycle. Hire an AI development company when: you need to ship within 3–6 months, you need specialised expertise (RAG architecture, computer vision fine-tuning, LLM evaluation) that is hard to hire for, you want to validate an AI investment before committing to headcount, or you want to augment your existing team on a complex problem. A hybrid approach often works best — bring in an AI partner to build the first production version and establish architecture, then transfer knowledge to your internal team. Drow Digital includes knowledge transfer and team handoff documentation in every engagement.
What AI services are available for small and medium businesses?
+
AI is no longer exclusively for large enterprises. Small and medium businesses can benefit from: (1) AI customer support chatbots built on GPT-4o or Claude that handle queries 24/7, resolving 60–70% of tickets automatically. (2) Document intelligence (RAG) that gives staff instant answers from internal knowledge bases, manuals, and policies. (3) AI content generation pipelines for product descriptions, blog articles, and marketing copy at scale. (4) Demand forecasting ML trained on your sales history to reduce stockouts and overstock. (5) AI-powered lead scoring to prioritise sales outreach. Our AI Proof of Concept starts at $8,000–$25,000 — a focused 4–6 week engagement to validate the highest-ROI AI use case. We offer a free AI Opportunity Assessment to identify where AI creates the most value in your specific business.
What is LLM fine-tuning and when is it needed?
+
LLM fine-tuning is the process of continuing to train a pre-trained model (such as Llama 3 or Mistral) on your domain-specific data, so it learns your terminology, reasoning patterns, and output formats. Fine-tuning is needed when: (1) Your domain is highly specialised (medical, legal, financial, technical) and the base model makes too many domain-specific errors. (2) You need a specific output format the base model does not reliably produce. (3) You need to encode your brand voice or proprietary knowledge into the model itself rather than through RAG prompting. (4) Cost reduction — a fine-tuned smaller model (e.g., Llama 3.1 8B) can match GPT-4 quality on a narrow task at 50–100× lower inference cost at scale. Fine-tuning requires 100–1,000 high-quality training examples and is done with LoRA or QLoRA for parameter-efficient training. We handle the complete pipeline: data preparation, training, evaluation, and deployment.
Services & Locations

AI Solutions Deployed
Across the Globe

From Silicon Valley to Singapore — our AI engineering teams ship production-grade systems across every major tech hub. Enterprise delivery, local compliance expertise, 24/7 support.

30+
Countries Served
8
AI Service Lines
200+
Projects Shipped
Active ServiceLLM Applications
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LLM Applications
We architect and ship production LLM applications using GPT-4o, Claude, Llama, and Gemini — integrated with your existing systems, fully evaluated, and monitored in production.
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10 — Start Your AI Project

Build AI that creates real value.

Free 45-minute AI strategy call. We'll review your use case, recommend the right approach, and give you a clear path from idea to production — no obligation.

What Happens Next

From first contact to shipped AI in 5 steps:

1️⃣
Submit brief — AI team responds within 4 hours, 7 days/week
2️⃣
Free 45-min AI strategy call — problem definition, data assessment, approach recommendation
3️⃣
Technical proposal with architecture, timeline, and evaluation framework design
4️⃣
Agreement signed — dedicated AI team assembled within 5 business days
5️⃣
First demo with your actual data delivered within 2 weeks
// FREE AI OPPORTUNITY ASSESSMENT

Every new enquiry starts with a free AI Opportunity Assessment — we map your business processes to identify the top 5 highest-ROI AI opportunities and rank them by feasibility. Delivered as a written report. Value: $2,500. Cost: free.

// FIRST DEMO IN 2 WEEKS

We deliver a working demo using your actual data within 2 weeks of project start. Not a presentation — a running system you can interact with. You decide whether to proceed to production based on what you see.

Tell Us About Your AI Project

✓ Request received! Our AI team will review your use case and reach out within 4 hours to schedule your free AI strategy call.

Free assessment. NDA available. No commitment required.