For AI-native consulting teams
Senior-grade AI engagements. At team-scale. Without the drift.
Audity is the operating system for AI transformation teams — from boutique consulting firms to enterprise transformation offices — that need to deliver senior-grade AI roadmaps: sourced, scored, and run by their team. Built on the frameworks your clients already recognize, with your firm's lens, voice, and question library applied to every engagement.
The Drift
Your team can run the work. They each run it differently.
You are not founder-trapped. You are drift-trapped. The methodology is in your team, but it shows up differently in every engagement, and senior review becomes a rewrite tax instead of a quality bar.
Inconsistent scoring across associates
Two associates score the same readiness signal three different ways. The deliverable shape changes engagement to engagement. Your senior team is the only thing holding the brand promise.
Evidence lives in seven tools
Interview notes in one place. Document analysis in another. Scoring rubric in a spreadsheet. Deliverable in slides. Nothing traces back to its source in one click.
Quality is senior review burden
Your senior consultants spend more time rewriting associate work than pressure-testing findings. Senior judgment becomes a production line, not a quality bar.
The Output
Four named deliverables. Same shape. Every associate.
Consistency is the asset. When every engagement produces the same four deliverables, in the same shape, scored on the same rubric, your firm’s output becomes recognizable. That is what scaling senior-grade work actually looks like.
Current-state AI readiness audit
A structured assessment of the client today: data, tooling, process, governance, people. Scored on Cisco / Gartner / 7-pillar, flavored by your firm’s vertical lens. Same shape, every engagement.
Prioritized AI use-case portfolio
Every opportunity scored on impact and feasibility, ranked into a portfolio the client can act on. Two associates produce the same shape of portfolio. That is the point.
90-day implementation roadmap
A sequenced plan: what to build first, who owns it, what evidence supports the call. Source-linked end to end.
Tool and partner recommendations
Specific tools, vendors, build-vs-buy calls, with rationale your client can pressure-test. The senior reviews the call, not the formatting.
How It Runs
Three steps per engagement. Every associate runs all three. The same way.
Your firm’s flavor (vertical lens, voice, deliverable template, custom question library) is captured once during a short onboarding with our team. After that, every associate runs the same three steps on every engagement.
STEP 01
Methodology library
Your firm’s question library, scoring rubric, and deliverable template live in one place. Every associate reaches for the same playbook. The library is your moat, not the individual senior in the room.
STEP 02
Parallel engagements
Multiple associates run separate engagements simultaneously. Each engagement isolated. Each using the same firm flavor and framework. Throughput goes up without quality going down.
STEP 03
Evidence chain end-to-end
Every finding in every deliverable traces back to the document, interview, or rubric that produced it. Senior review becomes pressure-testing the findings, not rewriting the work.
Evidence
Source-linked end to end. Auditable per engagement. Pattern data across the firm.
The evidence chain is the part that lets an AI-native team scale without losing what made the senior work senior in the first place.
Source-linked findings
Every claim in every deliverable points back to the document quote, interview, or rubric that produced it. Senior review presses on the finding, not the citation.
Audit trail per engagement
Every step taken in an engagement is logged: who ran the interview, who scored the rubric, who changed a finding. Defensible if the client ever asks.
Cross-engagement patterns
After ten engagements in a vertical, your firm sees patterns no individual senior consultant could hold in their head. Pattern data is your moat.
Build vs. Buy
Your engineers should be building what your clients pay for. Not the discovery workflow.
MIT NANDA research: 95% of internal “custom GPT” builds at consulting firms fail to reach repeatable production use within 12 months. The other 5% rebuild every 3 to 4 months as the models drift.
a16z analysis: the difference between an AI feature and an AI product is the workflow around it. AI-native teams are the most likely to underestimate the workflow tax.
Notion’s own case: rebuilt their internal AI tooling five times before settling on a productized stack. A $10B company with a full engineering org still took five tries.
Your engineering capacity should ship what your clients pay you for. The discovery OS is already built.
The Big-4 already built this
McKinsey, BCG, Deloitte, EY, PwC all run productized AI discovery internally.
They named it. They funded it. They will not sell it to you. Audity is what their tooling looks like, packaged so your team can run the same play at your scale.
McKinsey
Lilli
BCG
Deckster
Deloitte
Sage
EY
Zora
PwC
Agent OS
Run multiple engagements in parallel. Same shape. Every time.
A 30-minute demo. We walk you through the methodology library, the parallel-engagement view, and the evidence chain your senior team will press on. If Audity will not move your associate quality bar, we will tell you on the call.
FAQ
Questions AI-native teams ask first.
Your team can deliver senior-grade engagements. Audity is what makes them consistent.
Book a walkthrough. We will show you the methodology library, the parallel-engagement view, and the evidence chain. Bring your senior reviewer and your most experienced associate, they will see different things.