AI with a job to do.
Everyone's talking about AI. Few know where it actually helps.
Three integrations we design and ship to production end to end — the ones that quietly pay for themselves from the very first week.
Assistants that actually respond
An assistant grounded in your knowledge base handles questions around the clock — it grasps intent, finds the right information, and hands the conversation to a person the moment it's needed. Shorter queues, better answers, privacy intact.
Documents that read themselves
Invoices, contracts and scans become structured data the moment they arrive. The system recognizes what it's looking at, extracts the fields that matter and flags anything unusual — hours of manual checking gone, and the errors with them.
Problems you see coming
Models trained on your history forecast demand, spot anomalies and raise the alarm before a swing turns into a bottleneck. You get the warning with enough lead time to act — clearer data, faster decisions.
Six ways to get started.
CapabilitiesProcess automation
The repetitive work handed off to software: intake, triage, routing, follow-up. Your team keeps the judgment calls; the machine keeps the queue empty.
Data and forecasting
Analytics and forecasting on live data — demand, churn, inventory — brought to where decisions are made, not buried in a dashboard.
Assistants and chatbots
Customer-facing or internal, grounded in your content, with a clean handoff to human operators and a full record of every conversation.
RAG pipelines
Retrieval-augmented generation over your documents: answers that cite their sources, respect permissions, and don't make anything up.
MCP integrations
We connect the models to your real systems — databases, CRM, internal tools — through Model Context Protocol servers with narrow, auditable scopes.
Multi-agent systems
Specialised agents that plan, execute and review each other's work under a single orchestrator — a pipeline you can trace from start to finish.
AI in production
Questions? Answers.
If yours isn't here, get in touch.
Agents handle the repetitive, low-judgment work that eats up your team's time: answering customer questions from your knowledge base, reading and extracting data from documents, triaging requests, and flagging anomalies before they become problems. The goal is production systems that earn their keep, not chatbots that impress in a demo.
We build with guardrails. Responses are grounded in your content through retrieval (RAG), so they cite their sources and respect permissions, and integrations run through traceable, tightly scoped connections. Your data stays yours, and every action is auditable.
We're model-agnostic and pick whatever fits the task — frontier models like Claude and GPT, open-weight models you can self-host, plus RAG pipelines, vector search, and MCP integrations into your real systems. We optimise for reliability, cost, and privacy instead of chasing the hype.
No. Many of the most useful agents work with the documents and knowledge you already have. When predictions or forecasting come into play, we assess your historical data first and tell you honestly what's feasible before you commit.
Agents escalate to a person the moment they need to, and you get a complete log of every conversation and decision. Your team keeps the judgment calls; the system keeps the queue empty and the audit trail complete.
