AI policy you can hand to a client, an insurer or a regulator
We write the acceptable-use policy, map where data flows, review model and vendor risk, and put the controls in place, so the answer to “how do you use AI” is a document rather than a shrug.
A week in ai policy and governance, before and after
- Mon 9:00A client's security questionnaire asks how AI is used. Nobody knows what the team actually uses.The acceptable-use policy and the data-flow map answer it, and the tool inventory is current.
- Tue 11:00Customer data is pasted into a public chatbot by someone trying to be helpful.The policy says what may go where, the approved tools are listed, and the team was trained on it.
- Wed 14:00The insurer asks about AI risk. The answer is improvised.The model and vendor risk review is on file with its review date.
- Thu 10:00A regulated deal needs data to stay in Canada and the stack cannot promise that.The residency map shows what stays in Canada today and the in-house option for what does not.
- Fri 15:00An AI feature goes wrong and there is no procedure.The incident procedure names who decides, what is switched off and who is told.
What we build for ai policy and governance teams
6 capabilities, each with its own page: the deliverables, the steps, and what is not promised.
AI policy and governance advisory
Acceptable-use, model-risk and data-handling policy for a team facing client, insurer or regulator questions.
See the deliverablesPII protection and data handling
Redaction, minimisation and handling rules for personal data in transcripts, tickets and documents before any model sees them, with residency where your obligations require it.
See the deliverablesAI red teaming
Adversarial testing of an AI feature — jailbreaks, prompt injection, data exfiltration, tool abuse — with findings and mitigations you can act on.
See the deliverablesCustom hosted and on-premise AI
AI deployments designed for where your data has to live — a dedicated hosted environment or your own hardware — on open-weight models and self-hostable components.
See the deliverablesAI enablement workshops
Hands-on sessions that get a function from “tried the chatbot” to a working habit on its own material, with the guardrails included.
See the deliverablesAI context readiness audit
A transparent check of which company context an AI could use today, what is missing, and what to write down first.
See the deliverablesWhat stays under your control
The promises that hold on every engagement, and the ones we do not make on this one.
On every engagement
- Outputs cite the document or conversation they came from, so a person can check before acting.
- A person approves anything that reaches a customer or writes to a system of record.
- Delivery is Slack, email or a document you own. Other channels are not offered.
- Fixed price, agreed before we start. Nothing numeric is published on this site.
The work runs on the TurnkeyGTM platform where it fits, and on plain software where it does not.
Not promised here
- This is advisory; legal review stays with your counsel and we do not certify compliance.
- Policies are written for your team's actual tools and data, not from a template.
- Red teaming of your own AI features is scoped and authorised in writing before any test.
How an engagement runs
One call to scope it, one fixed price to approve, one build you can trust before it goes live, then a handover.
The full process- 1
Scope the work
A thirty-minute call on what your team does by hand every week, where the information lives, and who needs the output.
- 2
Fixed price, ROI up front
You get a written proposal with a fixed price and an honest estimate of the hours it gives back.
- 3
Build beside your process
We design and build the automation and run it next to the manual version until the outputs match and you trust them.
- 4
Hand over, keep it running
Documentation and runbooks so your team owns it.
Questions we get asked
Teams ai policy and governance works with
Engineering & leadership: Engineering, founders and compliance owners: the teams that decide how AI runs and where.
Software engineering
Put AI to work on the parts of engineering that eat the week, with evaluation gates so the output earns its place in the codebase.
Learn moreRevenue operations
Connect the record across CRM, calls and tickets so a figure in a report can be traced to the record it came from.
Learn moreCustomer support
Answer from your own documentation, tag what customers ask, and surface the themes before they show up as churn.
Learn moreTell us what your ai policy and governance team does by hand.
A thirty-minute call is enough to know whether it is worth automating. Fixed price, agreed before we start.