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AI Integration

We connect AI to the systems that actually run your business — CRMs, ERPs, data warehouses, legacy platforms. No standalone demos. Production systems that ship.

Published

November 17, 2025

The gap between an AI demo and a production system is almost always an integration problem

Running a model is easy. Connecting it to your CRM, your ERP, your data warehouse — so AI shows up where your people already work — that’s the hard part.

We’ve built integration layers for real estate platforms, automotive marketplaces, and employee ownership software. We connect LLMs to existing data, design function-calling APIs, manage conversation state across systems, and ship security controls that respect your existing access policies.

How it works

  • Weeks 1-4 — Foundation: Map existing systems and data architecture. Design integration patterns and API structure. Build the first integration point. Deploy to internal testing.
  • Weeks 5-10 — Build: Ship remaining integrations. Stand up RAG pipelines if needed. Build monitoring and logging. Expand to broader internal users. Iterate on feedback.
  • Weeks 11-14 — Harden: Error handling, edge cases, performance tuning, caching, admin interfaces, security review.
  • Weeks 15-18 — Rollout: Deploy to 5-10% of external users. Monitor metrics. Iterate on UX. Expand gradually. Measure business impact.

What you get

  • Integration architecture designed around your existing systems
  • Function-calling APIs that let LLMs invoke your business logic predictably
  • RAG pipelines grounded in your organisation’s actual knowledge
  • Monitoring and logging for every AI decision
  • Knowledge transfer so your team owns it long-term

Integration patterns we use

Function-calling architecture: Define a bounded set of functions the LLM can invoke — property search, valuations, agent lookup, vector DB queries. The model interprets intent and picks the right function, but stays within well-defined rails. Predictable. Transparent. Maintainable.

RAG pipelines: Ingest documents, videos, URLs, structured data. Chunk, embed, store in production vector databases (Pinecone, Chroma, pgvector). Semantic search with relevance tuning. Grounded responses, not hallucinations.

Legacy system adapters: Wrap SOAP services with modern APIs. Transform fixed-width files into JSON. Build event-driven interfaces around batch systems. Strangler fig patterns let AI coexist with legacy while gradually expanding scope.

Who this is for

You’ve seen what AI can do in demos. Now you need it connected to your actual data and workflows. Especially relevant if you’re working with legacy systems, have strict security requirements, or need AI embedded in existing interfaces — not bolted on as a standalone tool.

Results

At REA Group, we built systems that queried property databases, valuation engines, and knowledge bases within millisecond response windows. For a European automotive marketplace, we shipped conversational AI using function-calling architecture with six well-defined capabilities. Bounded integration that delivers value without unpredictable agent behaviour.

See it in practice: the Matterport at REA Group integration and the Unity AR Capture Pipeline rescue under a four-month deadline.

Book a free call

Schedule an integration assessment — a 90-minute session where we review your systems, discuss your goals, and map out a practical path forward.