A conversational, action-taking AI layer over your own product, APIs and systems - grounded in your data, running what-if simulations and executing multi-step workflows, with approvals where they matter. Governed and audit-ready from the first line, built to clear the rules wherever you operate.
Every product now needs an AI interface, and MCP has made agent-interoperability a standard the major vendors back. But a chatbot bolted on the side doesn't change how anyone works - and an AI that takes action without governance stalls the moment it hits a security review. The copilot people actually want reasons over your data, takes multi-step action across your systems, and can prove every step.
A chatbot stapled to the side of your product, disconnected from your real data and the workflows people actually use.
It retrieves a response and stops. No work actually gets done inside the product, so it never changes behaviour.
It demos beautifully, then can't produce an audit trail or model-risk evidence the moment someone asks for it.
No oversight and no documentation, so it dies in your customers' security and procurement process.
Four capabilities, one layer over your product. Grounded in your data, connected to your systems, and never acting outside the rails you set.
Users ask in plain language and get answers grounded in your own data - not a chatbot bolted on the side.
Run forward-looking what-if scenarios in-product - safely, without changing the underlying data - so users explore outcomes, not just look up records.
It doesn't just answer - it executes multi-step actions across your APIs, with approvals where they matter.
Audit trails, human oversight and model-risk evidence, built in from day one - not bolted on after.
Every request follows the same disciplined path - understand, ground, act, and prove it. Tap through each stage, or let it play.
An orchestrator reads the request, works out what it really needs, and routes it to the right specialist agent - instead of one generalist prompt trying to do everything at once.
A generic view of how we build these - the shape holds whatever your product is. A user asks in plain language, in real time; an orchestrator routes each request to a purpose-built specialist agent; those agents reach your systems through tools - over MCP where it fits and direct integration where it doesn't - and the answer streams back live, every action logged. Guardrails, scoped access and observability wrap the whole thing.
Grounded in your product, data and engines - every answer is computed, never guessed.
Answers are computed from your systems - not estimated by the model.
The point of the shape is trust: the model reasons, but every answer is computed from your own systems - not estimated. It runs as independently deployable services, so one slow part never takes the rest down - and every step is traced, so autonomy is something you can actually sign off on.
The tools we build with and the strong alternatives at each layer, shown together - we pick per project rather than forcing one house style.
What we've run in production and the strong alternatives are shown together, no split - we pick the right tool per project and aren't locked to any single vendor.
Not pilots or prototypes - agentic copilots running on real products, in the verticals where AI has to be trusted.
A multi-agent copilot over the platform's live APIs lets advisors and clients run retirement and what-if scenarios in plain language - every answer computed by the platform's own engines and streamed back in real time. A system of record becomes a system of advice.
Read the case study →Payments agents that handle multi-step payment workflows, reasoning over the client's own data and taking action across their APIs - not just answering questions.
An agentic copilot for insurance software, built over their product and APIs with auditability and oversight designed in from the first line.
Audit trails, oversight and model-risk evidence ship with the copilot, not as a later phase - so it clears reviews instead of stalling in them.
The copilot and the agentic engineering underneath, from one team - no stitching three vendors together to go AI-native.
The delivery muscle behind the AI work is battle-tested, not experimental.
Connected through MCP where it fits and direct tool integration where it doesn't, on the open standards major vendors back - so your copilot interoperates and you're never locked to one model.
ISO 27001 processes and a partner stack to match - Member of the Claude Partner Network, Snowflake and Databricks - for the data-and-AI foundation underneath.
Usually a CTO, VP of Engineering, Head of Product or technical founder - and on the enterprise side, a Head of Data or AI.
A copilot that works isn't enough if it can't satisfy a regulator or pass a security review - and the bar is rising everywhere: US frameworks like the NIST AI RMF, the EU AI Act, and the rules arriving behind them. Yours has to be explainable, controllable and provable, or it gets stuck in procurement. We build that in from the first line - so your launch ships on time and your deals don't wait on a risk questionnaire.
Already have AI in production? We assess, document and harden your existing agents and copilots so what you've shipped holds up to scrutiny.
Show exactly what the copilot did, and why.
Approvals and guardrails that keep your team in command.
Aligned to US frameworks like the NIST AI RMF, the EU AI Act and the rules in your markets - ready the day a regulator or customer asks.
Built in, not scrambled together after an audit request.
Most work starts with a single, governed copilot on a high-value workflow, proven in production, then expands to more agents and deeper into your roadmap.
We find the highest-value first copilot and where it moves the needle in your product or your operations.
A fixed-scope first build, governed from day one and designed to be easy to say yes to.
We design, build and harden it with you - audit trails, oversight and security baked in from the first line.
Extend to more workflows and more agents, deeper into your roadmap, as the value compounds.
It executes multi-step actions across your APIs, with approvals where they matter. Acting on your systems is the point — answering is the easy part.
Yes. The copilot sits over your product, APIs and data — grounded in your data through retrieval. We don't replace your stack.
It's grounded in your data with retrieval and memory, fenced with guardrails, and keeps a human in the loop where it matters. Every action is auditable.
Governance is built in from the first line — audit trails, oversight, model-risk evidence, and documentation aligned to US frameworks like the NIST AI RMF, the EU AI Act, and the rules in your markets.
Yes. We assess, document and harden existing copilots and agents so what you've shipped holds up to scrutiny.
Model-flexible by design. We connect through MCP and tool integrations on open standards and route to the right model for each job, so you're never tied to one vendor.
The scoped first build is designed to prove value fast — weeks, not quarters — then expands from there.
Book a 30-minute discovery call. We'll map the highest-value first copilot - and the governance to ship it with confidence.