AI Copilot Development
A copilot is not a chatbot in the corner. It is an assistant that knows what the user is looking at, what they are allowed to see, and what the next useful action would be — which makes context and permissions the real work.
Discuss your copilot
What We Build
Assistants embedded in the products people already use.
In-Product Assistants
A copilot inside your application that sees the current context and can act on it.
Grounded Q&A
Answers from your documentation, tickets and data, with citations and a clear "not found" state.
Action Execution
Creating records, drafting responses and triggering workflows with confirmation before anything commits.
Developer Copilots
Internal assistants over your codebase, API docs and runbooks for engineering teams.
Domain Copilots
Assistants for sales, support, legal or finance that understand that domain's language and constraints.
Knowledge Integration
Connectors to the systems where the knowledge actually lives, kept in sync rather than exported once.
What Makes A Copilot Useful
Context awareness
The copilot knows the record, page or selection the user is on without being told.
Suggested actions
Relevant next steps offered rather than waiting for the user to think of the right prompt.
Confirm before acting
Any change is previewed and approved, so trust survives the first mistake.
Memory
Conversation and preference memory scoped per user, cleared on request.
Usage analytics
What people ask, what fails and where the knowledge base has gaps.
Feedback loop
Thumbs and corrections captured and fed into evaluation, so the copilot measurably improves.
Where We Put AI To Work
We take on AI projects where the outcome can be measured — a cost that falls, a queue that clears, a decision that gets more accurate.
Talk to our teamAdvanced AI Engineering Capabilities
The difference between a demo that impresses and a system you can rely on.
Evaluation harness
A scored test set built from your real data, run on every prompt, model or retrieval change.
Guardrails
Input and output filtering, prompt-injection defences and refusal behaviour you have chosen.
Cost engineering
Caching, model tiering and context trimming so unit economics work at real volume.
Provider abstraction
One interface across providers, so switching model is configuration rather than a rewrite.
Self-hosted options
Open-weight models served in your own infrastructure where data cannot leave your estate.
Full request logging
Prompt, context, response and score retained for debugging and audit.
How We Deliver
One high-value question type first, expanded from evidence.
Find the questions
What users actually ask today, gathered from support tickets and interviews.
Ground and evaluate
Retrieval built with permissions, scored against a real question set.
Embed in the product
Context wiring, actions and the interface where the work happens.
Measure and expand
Usage and failure analysis drives what the copilot learns to do next.
Put An Assistant In Your Product
Tell us what your users ask and what they need to do. We will scope a copilot that respects your permissions.
Talk to our AI teamCopilot Questions
Everything you need to know about our ai development work.