Add AI to Your Existing Software the Right Way
Clean, production-grade AI integrations that embed LLM capabilities into your current stack — without rebuilding everything from scratch.
What We Build
AI Integration Capabilities
Six integration patterns we deliver into existing production systems.
LLM API Integration
Production-ready integrations with OpenAI, Anthropic Claude, Google Gemini, and Mistral — with prompt management, token optimisation, retry logic, and cost controls.
Custom Model Serving
Deploying fine-tuned or open-source models (Llama, Mistral, Falcon) to your own infrastructure — full data privacy with no reliance on third-party API availability.
Retrieval-Augmented Generation
RAG pipelines using vector databases (Pinecone, Weaviate, pgvector) to ground LLM responses in your proprietary data — accurate answers without hallucination.
AI-Powered Search
Semantic search layers added to existing applications — replacing keyword matching with meaning-aware retrieval that surfaces the right result even with imprecise queries.
Agentic Workflows
Multi-step AI agents that plan, use tools, call APIs, and execute tasks autonomously — built on LangChain, LlamaIndex, or custom orchestration for complex workflow automation.
Streaming & Real-Time Responses
Server-sent events and WebSocket integrations that stream LLM responses token-by-token — delivering the fast, responsive experience users expect from modern AI products.
Project Deliverables
What's Included in Every AI Integration
- AI integration architecture design and API specification
- Production integration code with error handling and retry logic
- Prompt library with versioning and testing harness
- Vector database setup and embedding pipeline (if RAG)
- Cost monitoring and rate-limit management
- Security review: data handling, PII redaction, zero-retention config
- Integration documentation and maintenance guide
AI Integration FAQ