Copilots grounded in your data, not the internet's
A copilot is only useful if it knows your business, and only trusted if it shows its sources. We build assistants for sales, support and operations teams that answer from your systems — with citations, permission-aware retrieval and usage metrics that prove they're earning their keep.
Where copilots earn their keep
Teams that answer the same questions every day are where a copilot pays for itself.
Customer support
Suggested answers drafted from your docs, past tickets and policies — agents approve and send, handle time drops.
Sales & proposals
Account research, call prep and first-draft proposals assembled from your CRM and knowledge base.
Operations & knowledge
The "ask the veteran" questions — procedures, specs, precedents — answered with citations instead of interrupting the veteran.
Analytics & data
Questions in plain language, answers from your actual warehouse — with the query shown, so trust is checkable.
How a copilot becomes trusted
Trust is engineered: grounding, citations, evaluation — and the honesty to say "I don't know."
Ground it in your data
Retrieval over your documents and systems — inheriting your permissions, so people only see what they're cleared to see.
Citations on every answer
Each response links to its sources. "I don't know" is an acceptable answer; a confident guess is not.
Evaluate against real questions
A test set built from your team's actual questions, scored before launch and continuously after.
Measure adoption, then expand
Usage, deflection and time saved tracked per team — the copilot expands where the evidence says it works.
What ships with every copilot
An assistant your security team approves and your finance team can measure.
Common questions
How do you stop it from making things up?
Grounding, citations and refusal: answers come from retrieved sources, every claim links to one, and when retrieval comes back thin the copilot says so instead of improvising. The evaluation set keeps it honest over time.
Will it leak data between departments?
No — retrieval inherits your permission model. A salesperson's question is answered only from what that salesperson could open by hand.
Which model does it use?
Whichever wins on your task, cost and privacy constraints — kept swappable behind an interface, so when better models arrive your eval set decides the upgrade, not the hype cycle.
How do we know it's actually helping?
A named metric before we build — handle time, deflection rate, hours saved — and a dashboard tracking it after. If the number doesn't move, that's a finding, not a secret.
Complete AI workloads migrated from cloud APIs onto custom on-prem hardware
For teams whose data can't leave the building — assistants and models running on infrastructure they own.
Read the case study →More AI solutions
Document intelligence →
Contracts, claims, invoices and filings — extracted and reviewed at machine speed.
Agentic workflows →
Multi-step processes executed end-to-end — with human checkpoints and full audit logs.
Forecasting & decisions →
Demand, pricing and risk models embedded where the decision is actually made.
The full practice →
Everything we do in AI solutions.
Which team asks the same questions every day?
That's where a copilot starts. Thirty minutes with an engineer to scope it.