AI Integration & LLM Engineering
Production-grade AI embedded directly into your product — not a chatbot wrapper. RAG pipelines over your own data, intelligent automation workflows, voice transcription, and LLM-powered decision layers built into your backend.
AI That Works in Production
The gap between an AI demo and a production AI feature is enormous. Demos use hardcoded examples, ignore edge cases, and have no cost controls. Production AI integration means: retrieval-augmented generation so the model answers from your actual data, structured JSON output fed directly into business logic, robust error handling for rate limits and content filters, token usage tracking per user, and observability on every LLM call. That's the bar I build to.
I use the Vercel AI SDK as a provider-agnostic abstraction, with OpenAI GPT-4o and Anthropic Claude 3.5 Sonnet as primary models depending on the use case. Model selection is driven by task characteristics — Claude for long-context document analysis, GPT-4o for multimodal inputs, smaller models for high-volume classification tasks where cost matters. The architecture makes switching models a config change, not a refactor.
Real-world AI features I have built include: RAG knowledge bases where the system answers questions from uploaded documents, voice-to-text consultation transcription in a healthcare platform (Rooks), automated content generation pipelines, AI-powered classification and tagging systems, and conversational interfaces with multi-turn memory. Each one is instrumented, cost-controlled, and degrades gracefully when the AI service is unavailable.
GPT-4o
Latest frontier models integrated — OpenAI, Anthropic Claude, and on-premise Ollama.
RAG
Production RAG pipelines grounding LLM responses in your proprietary data.
Core Capabilities
Proven engineering solutions for complex, real-world business problems.
RAG & Knowledge Bases
Document ingestion, chunking, vector embeddings, pgvector / Pinecone storage, semantic retrieval, and grounded generation with source citations. Ask questions of your own data.
LLM Orchestration & Agents
Multi-step agentic workflows with tool use (web search, database queries, API calls), memory management, and retry logic for reliable autonomous task completion.
Voice & Transcription
Whisper-based voice transcription with speaker diarisation, punctuation restoration, and structured output extraction — proven in a live healthcare platform.
Content Generation Pipelines
Automated content creation with brand voice enforcement, human-in-the-loop review gates, structured output schemas, and CMS integration for publish workflows.
Classification & Extraction
Document classification, entity extraction, sentiment analysis, and structured data extraction from unstructured text — with confidence scoring and fallback handling.
Cost & Observability
Per-user token tracking, budget alerts, semantic response caching, model tier routing, and dashboards showing AI cost as a function of business metrics.
The Engagement Process
Use Case Scoping
Define exactly what the AI feature does, what data it needs, what the output format is, and what happens when the AI fails or returns low-confidence results.
Data Architecture
Design the data pipeline: ingestion, chunking, embedding strategy, vector store selection, retrieval ranking, and context window budget allocation.
Prompt Engineering & Evaluation
Iterative prompt development with an evaluation dataset measuring accuracy, hallucination rate, and latency. No production deployment without passing eval benchmarks.
Integration & API Build
Build the API layer connecting the AI pipeline to your product. Streaming responses, structured output parsing, error handling, and rate limit management.
Production & Monitoring
Deploy with token usage tracking, cost dashboards, response quality logging, and alerting on degraded model performance or elevated error rates.
Primary Technology Stack
Pricing & Investment
AI integration cost depends on the complexity of the use case, the models involved, and whether you need custom fine-tuning or retrieval infrastructure. Most projects start with a scoped proof-of-concept before committing to a full build.
AI Integration
£3,000 – £10,000
Connect your product to an LLM API (OpenAI, Anthropic, Gemini) with prompt engineering, output validation, streaming responses, and cost monitoring. Delivered in 2–4 weeks.
Ideal for: Products adding AI-powered features, content generation, summarisation tools
RAG / Knowledge System
£10,000 – £28,000
Full retrieval-augmented generation pipeline: document ingestion, chunking, embedding, vector search, context assembly, and an LLM response layer with citation support.
Ideal for: Internal knowledge bases, customer support bots, document Q&A platforms
Custom AI System
£28,000+
Agentic workflows, multi-step reasoning chains, fine-tuned models, multi-modal pipelines, or deeply integrated AI features that require custom infrastructure and ongoing optimisation.
Ideal for: AI-native products, vertical SaaS with intelligent automation, enterprise AI tooling
All AI projects include cost forecasting — you will know your estimated OpenAI / Anthropic API spend before launch, not after. Prompt engineering and evaluation are included in every engagement.
Frequently Asked Questions
Related Services
API Development
Backend APIs that expose your AI features to web and mobile clients.
SaaS Development
SaaS platforms with AI as a first-class product feature.
Web Development
Full-stack Next.js applications with AI-powered UI features.
Cloud & DevOps
GPU-optimised cloud infrastructure for AI model serving.
Ready to Add AI to Your Product?
Let's talk about what AI can genuinely improve in your product — and build it properly, with the engineering rigour that makes it reliable, cost-controlled, and actually valuable to your users.