Your data. A more useful customer experience.
Useful AI experiences need reliable product information, relevant customer context, and access to approved systems. We connect the data your business already has so bespoke applications can support the people using them.
Your business is not generic. Your AI should have the context.
Your business context lives across CRM, commerce systems, warehouses, and documents. When those sources are disconnected, an AI application has an incomplete view. Connecting them gives each role the relevant context to make a decision, serve a customer, or take the next action.
We build reliable pipelines, integrations, and retrieval around your approved systems. A connection created for one application can support the next. As new sources and model capabilities become useful, we extend that foundation with your team. The infrastructure and custom code remain yours.
Connect the data. Make the experience useful.
Every engagement ships working infrastructure, not a recommendations deck. You own the code, the pipelines, and the documentation.
Pipelines, retrieval, and knowledge systems
The core build. We map where your data lives, connect it, clean it, and make it retrievable, so agents, assistants, and RAG systems answer from your records instead of the model's memory. When a system you depend on has no usable API, we write one. Scoped in phases, each ending with something running in production.
Scope your build →- Data pipelines and integrations across your core systems
- Custom APIs into systems that do not offer one
- Migrations out of legacy stores, planned and executed
- Search and retrieval systems tuned for AI workloads
- Knowledge systems that keep AI answers current
- Documentation your next hire can use
Built on your stack
We work alongside IT inside your existing warehouse, tools, and cloud, connecting approved sources before proposing new systems. Architecture, permissions, and monitoring are designed with your team, so they can operate and extend the result.
Talk through your stack →Own it. Keep improving it.
Quality checks, monitoring, and documentation make the system maintainable. Continue with us to add sources, improve retrieval, and support new applications, or take it forward internally. Ongoing work is agreed around your priorities.
Ask about handover →Where missing context gets in the way.
Start with the experience or decision you want to improve. These are some of the data problems that can stand in the way.
Where this fits in the stack.
Data engineering is usually the foundation. These are the services that build on it.
Put your data to work for your customers.
Tell us about the experience you want to improve and the systems behind it. We can assess what needs connecting and define a considered scope.
