Most businesses aren't starting from scratch. They have systems, data, and workflows that work. We integrate AI into what's already running — without breaking it.
Demos work in isolation. Production AI works in context — connected to your database, respecting your business rules, talking to your existing APIs, handling your data formats. Closing that gap is the hard part.
Your WIP system, your ERP, your customer database — they're running. They have schemas, quirks, undocumented dependencies, and years of production data. AI integration has to work around all of it. Ripping and replacing isn't an option for a live business.
The data in your existing systems is your competitive advantage. 20 years of production records, customer relationships, pricing history, operational patterns — an AI that can access and reason over that data is a fundamentally different tool than one that can't.
Connecting AI to production systems without understanding the business logic behind them is how you get incorrect automations, corrupted records, and expensive rollbacks. We've spent 25 years building production systems across multiple industries. We understand what's at stake.
We map your existing data flows, identify the AI integration points that deliver the most value, and build connections that are additive — not disruptive. Existing workflows keep running. AI adds capability alongside them.
We work at every layer of the stack — from the database up to the user-facing interface.
We've been writing production SQL since 2004. We know how to read existing schemas, understand undocumented relationships, and build AI access layers that work with your data as it actually is — not as it ideally would be. No schema migration required to get started.
REST APIs built API-first, where the endpoint is the source of truth and AI adapters wrap it cleanly. JWT authentication, RBAC, multi-tenant isolation where required. Consistent patterns that make AI integration straightforward and future-proof.
Getting data from your systems into your AI in the right shape. Extraction, transformation, vectorisation for semantic search, hybrid search pipelines that combine full-text and vector similarity. We design for the query patterns AI actually uses, not just generic CRUD.
Courier systems, accounting platforms, CRMs, agency portals, e-commerce platforms — we've connected all of these to production systems in the past and understand the integration patterns involved. AI integrations into existing third-party ecosystems are a natural extension of that work.
A manufacturing operation had been running a bespoke production management platform since 2004 — WIP tracking, agency portals, multi-system invoice export, courier integration. The platform worked. The data it contained was extraordinarily rich: every job, every technique, every substrate, every outcome. None of it was accessible to AI.
The integration challenge was unusual: the developer proposing the AI integration was also the developer who'd originally written the platform. Every schema relationship, every edge case, every undocumented behaviour was already understood. No discovery phase. No reverse engineering. No risk of misreading a critical dependency.
Rather than building a generic integration layer, the approach was native: AI access points built directly into the existing schema, data extraction designed around the actual query patterns the models needed, routing logic that understood the production workflow rather than just the data structure.
The outcome was an AI that didn't just have access to the data — it understood the context the data lived in. The difference between reading a database and understanding a business.
"The best integration partner for a legacy system is the person who wrote it. The second best is someone experienced enough to reverse-engineer it properly. In either case, domain knowledge is the variable that determines whether integration delivers value or creates problems."
Tell us what you're running and what you're trying to connect. We'll scope the integration and tell you honestly what's feasible.
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