AI Agents: Grounded in Your Data
AI agent development.
Assistants that answer from your own systems instead of guessing, with retrieval, real tool access, guardrails, and a clean handoff to a human when they should not answer.
What we build
Answers you can act on.
Client-facing assistants
A chat interface your clients can ask "where is my project?" or "what did we pay last quarter?", answered from live data, at 11pm, without anyone on your team replying.
Grounded in your data
Retrieval over your own systems and documents, so answers cite what they came from. An assistant that invents a number is worse than no assistant at all.
Tool access, not just chat
Agents that actually do things: look up a ticket, draft an invoice, schedule a job, update a record. Chat is the interface, not the product.
Internal copilots
Assistants for your team: summarising an account before a call, drafting the status update, surfacing what changed since last week across the tools you run.
Guardrails and escalation
Scoped permissions, refusal on out-of-scope questions, and a clean handoff to a human when confidence is low. The agent knows what it is not allowed to answer.
Evaluated, not vibes
A test set of real questions with expected answers, run on every change, so you can see accuracy move rather than trusting that it feels better.
Our stack
How it works
From first call to launch.
Discover
A free discovery call about your business, your goals, and what needs to be built. You get a concrete scope and a fixed quote.
Design
Structure, interfaces, and technical approach you review and approve before a line of production code is written.
Build
Built in short sprints you can see: working software every week, tested as it grows, no black-box development.
Launch & support
We deploy it and set up monitoring, then hand over. After launch we stay available for changes and new features.
Published work
Agents in production.
A connected health platform, built for Seniorsoft, runs AI-assisted medication scheduling inside a live connected-health platform, with a human operator deliberately kept in the escalation path, because in that domain a confident wrong answer has consequences.
Read the Case StudiesFAQ
Common questions
A generic chatbot answers from a script or from whatever it was trained on. An agent grounded in your systems answers from your data (this client’s actual invoice, this project’s actual status) and can take an action rather than only replying. The difference is whether it can be trusted with a real question.
That is the main engineering problem, and the answer is architectural rather than hopeful. Answers are retrieved from your systems and cited back to their source, the agent is scoped to a defined set of tools and topics, and anything outside that scope is refused and handed to a person. We also keep an evaluation set of real questions so accuracy is measured on every change.
Your data stays in your systems and is retrieved at query time rather than loaded into a model. For the model itself we select commercial APIs whose published terms exclude training on customer inputs, and we name the provider and the terms in the agreement before any build starts, so it is yours to check rather than ours to assert. Where a third party’s terms are not acceptable to you at all, we can run against a self-hosted model instead.
The systems you already run: CRM, accounting, ticketing, document storage, and whatever else holds the answers. If it has an API, an agent can read from it and, where you allow, write to it. The integration work is the same as described on our backend page.
We scope a first production agent at 6 to 10 weeks: scoping the questions it must answer, wiring retrieval to your data, building the interface, then evaluating and tightening against real usage. You see it answering real questions from early in the build. The range is what we plan against and quote from, and the scope we agree before starting is what fixes it.
Then it should not have answered. We design the refusal and escalation path before the happy path: low confidence, missing data, or an out-of-scope question routes to a human with the context attached. An agent that says "let me get someone" is doing its job.
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