Answers with sources attached.
Permission-aware search and Q&A over SharePoint, Teams, and approved documents. Every answer cites where it came from, and the agent says so when it doesn't know.
Enterprise AI · Microsoft 365 Copilot and beyond
Copilots your security team signs off on. And your people actually use.
We build governed AI agents inside your own Microsoft 365 tenant: knowledge agents that cite their sources, briefs that write themselves, and automations with a human in the loop. Phased, gated, and owned by you.
Built in your tenant · You own every agent · A go/no-go gate at every phase
Why most pilots stall
Licences are the easy part.
The model is rarely the problem. Enterprise copilots stall on the same four things, and we plan for all of them before the first agent is configured.
01
Oversharing.
Copilot respects existing permissions, so it surfaces anything a user can already reach, including the folder nobody remembers sharing. We map and tighten access before an agent goes live, not after the first incident.
02
Messy knowledge.
An agent is only as good as what it reads. We set up taxonomy, metadata, and quality gates so it answers from the approved version, not draft seven in someone's personal folder.
03
No proof it works.
Without a test set, "it seems good" is the only metric. We build evaluation sets with your subject-matter experts and score every release against them, with citations checked, not assumed.
04
Nobody owns adoption.
Pilots fade when the novelty does. Champions, training, and benefit tracking run from week one, so the pilot group is still using it in month three.
What we build
Agents for the work your teams repeat every week.
One business function first, with named pilot users. Then scale what worked, with evidence.
The weekly brief, written for you.
Recurring briefs and KPI context synthesised across plans, trackers, and action logs. The analyst reviews and sends, instead of assembling it from scratch.
Routine work, with a human sign-off.
Agent flows and Power Automate for recurring synthesis and task routing. Anything that leaves the team passes a human review step first.
The part auditors ask about.
Permission review, taxonomy, alignment with your Purview labels and DLP policies, evaluation test sets, and monitoring that stays on after we leave.
Custom agents when you outgrow it.
When a use case needs more than low-code, we build custom agents on Azure OpenAI or other models inside your cloud, with the same guardrails and evals.
People who actually use it.
Champion networks, role-based training, and a benefit baseline set in discovery, so the business case is measured rather than claimed.
How an engagement runs
You can stop at any gate with working software.
A typical shape for one business function. Each phase is fixed-fee and ends in a go/no-go decision, so you never commit to the whole programme on faith.
00 ~3 weeks
Discover & prepare
Confirmed use case, source inventory, permission map, success metrics, and a firm quote for the build.
Go / no-go 01 ~6 weeks
Knowledge agent
Configured knowledge base, citation-backed agent, evaluation test set, and UAT with your pilot group.
Go / no-go 02 ~4 weeks
Insight agent
Connectors to plans, KPIs, and action logs. Recurring briefing flows and context-rich summaries.
Go / no-go 03 ~4 weeks
Automate & govern
Agent flows with human review, monitoring and evals live, champions trained, operating model and scale plan.
MVP closeThen scale: more functions and data domains, with benefits measured against the discovery baseline.
Security & ownership
Your tenant. Your data. Your IP.
Built in your tenant
Every agent, flow, and configuration lives in your environment and belongs to you. Nothing runs on our infrastructure.
No training on your data
We configure and ground models on your content. We don't fine-tune or train models on it, and we don't keep copies.
Least-privilege access
Named consultants on your accounts, scoped to the engagement, and revoked at close. No standing connection to your systems.
Your controls, your checkpoints
Designed around your Purview labels, DLP policies, and GDPR and Swiss data protection obligations, with security review built into each phase.
Who we build with
AI teams already trust us with the hard parts.
From agent-to-agent coordination to LLM analytics and defence engineering, the same senior team that ships for them would work on your pilot.
See all our work →Straight answers
What IT and procurement ask first.
Do you only work with Microsoft 365 Copilot?
No. Copilot Studio is usually the fastest route when you already run Microsoft 365, and we start there. When a use case needs more control, we build custom agents on Azure OpenAI or other models inside your own cloud, with the same governance.
Can we start smaller than a full MVP?
Yes. Discovery stands on its own: a fixed fee, a confirmed use case, a permission map, and a firm quote for the build. You can stop at that gate with a clear plan in hand.
Who owns what you build?
You do. All agents, flows, prompts, and documentation are built in your tenant and handed over with runbooks, so your team can run and extend them without us.
How do you measure whether it worked?
Success metrics are agreed in discovery, before anything is built. Typically answer accuracy on the evaluation set, time saved on a named recurring task, and weekly active use in the pilot group.
What do you need from our side?
An executive sponsor for gate decisions, a product owner from the pilot function, two or three subject-matter experts for a few hours a week, and tenant admin support early on. We keep the ask small and scheduled.
The first step
One workshop. A plan you can take to your sponsor.
Bring your digital workplace or IT lead and one business owner. Within a week you get an opportunity map, a prioritised backlog, a governance checklist, and a firm quote for discovery. Yours to keep, whether or not you go ahead.