AI Integration & Agentic Workflows

Your business has valuable knowledge locked in documents, databases, wikis, and emails that nobody can query in real time. RAG systems change that. I build custom retrieval pipelines that connect your proprietary data to large language models — so your AI answers questions using your knowledge, not just its training data. This includes vector database architecture, document ingestion and chunking pipelines, embedding strategies, hybrid search systems, and retrieval quality optimisation.

• ✓Custom RAG pipeline design and implementation
• ✓Vector database setup — Pinecone, Weaviate, pgvector, Chroma
• ✓Document ingestion pipelines — PDF, Notion, Google Drive, Confluence, web scraping
• ✓Embedding model selection and optimisation (OpenAI, Cohere, open-source)
• ✓Hybrid search — semantic + keyword for precision retrieval
• ✓Retrieval evaluation and accuracy tuning
• ✓Internal knowledge bases, AI search systems, document Q&A products

An AI agent is not a chatbot. It's a system that perceives a goal, reasons about the steps to reach it, uses tools to take action, and adapts when things don't go as expected. I design multi-step agentic workflows that replace manual, repetitive, or coordination-heavy processes — including custom AI voice agents for operations that run on phone-based communication.

• ✓AI agent design using LangChain, CrewAI, LangGraph, AutoGen
• ✓Multi-agent orchestration — planner, executor, critic architectures
• ✓Tool use and API integration — agents that take real actions in real systems
• ✓Custom AI voice agents — low-latency voice pipelines using Twilio, Deepgram, Whisper for customer service and internal operations
• ✓Process automation — data extraction, classification, routing, report generation
• ✓Human-in-the-loop workflows — approval gates, escalation logic
• ✓n8n and low-code agent orchestration for faster deployment

You have a product that works. Your users are asking for AI. Your board is asking about your AI strategy. But your codebase was never designed for any of this. I consult with software companies and product teams to identify exactly where AI creates real value in their product, design the integration architecture that doesn't break what already works, and implement it. This is not about adding a chatbot to a dashboard — it's about embedding intelligence where it changes user outcomes.

• ✓AI feature audit — identifying where AI adds genuine value vs. where it's noise
• ✓Integration architecture design for existing codebases
• ✓LLM API integration — OpenAI, Anthropic, Gemini, open-source models
• ✓Prompt engineering and system prompt architecture for production reliability
• ✓AI feature design — smart search, auto-classification, content generation, recommendations
• ✓Cost and latency optimisation — model selection, caching, batching strategies
• ✓AI reliability and safety — output validation, guardrails, fallback logic

Not all AI problems are text-in, text-out. Some require processing images, audio, video, or documents alongside language. I design and build specialised AI pipelines for use cases that standard LLM integrations don't cover — combining the right models, the right retrieval strategies, and the right infrastructure for the task.

• ✓Real-time audio processing — speech-to-text, speaker diarisation, live transcription pipelines
• ✓Speech → reasoning → response pipelines for voice AI applications
• ✓Document intelligence — invoice processing, contract analysis, form extraction using vision models
• ✓Image and video understanding — classification, captioning, content moderation pipelines
• ✓Multi-modal RAG — retrieval across text, images, and documents simultaneously
• ✓Whisper, GPT-4 Vision, Claude Vision, Gemini multi-modal integrations
• ✓Custom fine-tuning and model adaptation for specialised domains