AI context readiness audit
A transparent check of which company context an AI could use today, what is missing, and what to write down first.
What you get
Each deliverable names the format it arrives in. These are the formats the stack produces today.
Readiness checklist
Scored
Business context pack
Markdown or JSON
Prioritised gaps
Document
How it works
- 1
Inventory what exists
Documents, decisions, product facts, customer evidence.
- 2
Score readiness
Against a transparent rubric: findable, current, consistent, permitted.
- 3
Pack and prioritise
A first context pack is assembled and the gaps are ranked by what they would unlock.
What runs where
On the platform
- Ingestion and the context pack format run on the platform
Built for you
- Rubric scoring and the gap list
What 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
- An assessment, not a build.
- Says what is missing; does not write the missing material unless scoped.
Try a piece of it
Free tools from the TurnkeyGTM ecosystem that do a small part of this job in the browser. No account needed.
OKF Utility
Generate, import, convert, and validate Open Knowledge Format bundles in the browser. Free, no account required.
Knowledge Graph Builder
Research a topic in chat and watch it become an interactive knowledge graph. Nodes, edges, and sources you approve before they land.
Questions we get asked
More in automation & engineering
AI workflow automation
The workflow your team repeats every week, automated with AI where it helps and plain software where it does not, run beside your current process until you trust it.
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 deliverablesModel selection and cost optimization
Which model, at which size, for which step — chosen by measured quality and cost on your own tasks rather than by the leaderboard.
See the deliverablesHow 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.
Tell us where this fits in your week.
A thirty-minute call is enough to scope it. Fixed price, agreed before we start.