From the engineering desk
The workflow layer you were about to be asked to build.
Somebody in the next staff meeting is going to propose an internal discovery tool, and it lands on your backlog. Audity is that layer, already built, with a REST API and an MCP server, so you drive it from your own stack instead of maintaining it.
Your week right now
You know how this build actually goes.
The unscoped internal tool
It starts as “just a GPT wrapper for our audits” and becomes a permanent internal product with one maintainer: you. Nobody budgeted for version two.
The model-drift treadmill
Every model release breaks prompts, formats, and assumptions. Internal builds get rebuilt every few months, and that rebuild always outranks your roadmap.
Client work loses the sprint
Every hour on internal plumbing is an hour off the engagements clients actually pay the firm for. The opportunity cost is invisible until the quarter ends.
What changes
Drive it from your stack. Skip the maintenance.
01
API and MCP server, included
Every seat includes the REST API and the Model Context Protocol server. Run the readiness diagnostic programmatically, or let an agent drive the audit, including from Claude Code, Cursor, or Codex.
02
The workflow layer is the product
Discovery agendas, interview kits, document analysis, contradiction detection, scoring, deliverable assembly: the entire engagement pipeline is already built and maintained by someone whose whole product it is.
03
It stays current so you don’t
Audity continuously ingests the latest models, tools, and use cases. When the frontier moves, the workflow updates upstream, no internal rebuild sprint.
04
Clean boundaries, exportable data
Engagement data is isolated per firm and per engagement, findings are source-linked and structured, and outputs export as PDF and DOCX. No lock-in shaped like a black box.
Numbers you can say out loud
95%
of internal builds never reach repeatable production use (MIT NANDA)
3–4 mo
rebuild cycle for the 5% that do, as models drift
5
rebuilds it took Notion’s own team to settle their AI stack
1 seat
is all the API and MCP access costs: it’s included
MIT NANDA research on internal custom-GPT builds at consulting firms; a16z's analysis says the difference between an AI feature and an AI product is the workflow around it. Notion, a $10B company with a full engineering org, rebuilt its internal AI tooling five times.
Make the case
Walk into the next staff meeting with this.
01
“MIT NANDA found 95% of internal builds like this never reach repeatable production use within a year, and the ones that do get rebuilt every three to four months as models drift.”
02
“It ships a REST API and an MCP server with the seat. We can drive audits from Claude Code, Cursor, Codex, or our own agents instead of building and owning the workflow layer ourselves.”
03
“Our sprint capacity should ship what clients pay us for. Notion took five rebuilds to get their internal stack right, with a much bigger team than ours.”
The objection you'll hear
“We could build this in a quarter.”
The first build, probably. The tax is everything after: prompt drift with every model release, format changes, evaluation, the deliverable pipeline, and an internal tool with no product team behind it. Buying the workflow layer and scripting against its API keeps the interesting engineering (agents, integrations, client work) and outsources the treadmill.
Keep the interesting engineering. Skip the treadmill.
See the API and MCP server drive a full audit before anyone commits your backlog to building one.
The same case, from the other desks
The sales desk
Turn free discovery into a paid product that opens six-figure engagements.
→The associate desk
Ship engagement work that survives senior review the first time.
→The principal's desk
Your methodology, running consistently without you in the room.
→Looking for the firm-level view instead? Audity for boutique firms →