Case study · Big 4 advisory · Technology & AI due diligence
AI Claims, Verified in Code.
Code-level evidence behind an AI valuation premium before IC approval.
- AI-Native score
45/100
AI as Product tier, evidenced repository by repository
- Codebase reviewed
2.5MLoC
across 12 repositories, 5 carrying genuine AI signal
- AI integrated
27%
of code with real model integration; the rest carries no AI signal
- Evidenced
100%
of AI claims mapped to source code, not management interviews alone
The challenge
Positive interviews. No code-level evidence.
The advisory team was supporting a private equity client on a significant minority investment in a mature B2B software platform. Newly launched AI capabilities were a core part of the equity story, and the investment committee needed those claims verified before the deal closed.
Management interviews supported the AI narrative, but the advisory team declined to rest the IC's comfort on conversations alone. The claims needed evidence across the software stack and the codebase itself.
- 01
Where does AI actually sit in the platform, and is it genuine integration or a thin wrapper?
- 02
Does the depth of AI investment match what management described to the IC?
- 03
Which parts of the platform carry no AI signal at all, and does that match the stated rollout scope?
The solution
Every AI claim mapped against the codebase.
Working alongside the advisory team, CodeDD's AI-Native assessment located where AI sits in the platform, classified genuine integration versus wrapper use, and assessed platform support for the roadmap.
- 01
Scored AI-Native maturity
Every repository scored across product embedding, model integration, retrieval and data plane, adoption velocity, MLOps and evaluation, and platform readiness.
- 02
Classified integration depth
AI technologies fingerprinted per repository, from LLM frameworks down to ownership of models versus wrapper use, tiered by IP depth.
- 03
Isolated the AI-integrated code
Machine-learning lines of code measured against total lines of code, repository by repository, to size the real AI footprint.
- 04
Matched claims to rollout scope
AI signal mapped against management's stated rollout, confirming where the platform matched the story and where it did not.
codedd.ai / AI-native assessment
Product screen
2.5M LoC · 12 repos analysed · 5 with AI signals45/100
AI as Productcodedd.ai / repository signals
Repository signals
12 repositories analysed · sorted by AI-Native score| Repository | LoC | ML LoC | AI technologies | Tier | Usage | IP depth | Score |
|---|---|---|---|---|---|---|---|
| Repo 01 | 341K | 74K · 22% | LLM Framework · langchain | T1 | AI as Product | Own models | 52/100 |
| Repo 02 | 198K | 31K · 16% | Vector DB · pgvector | T1 | Agentic system | Integrator | 44/100 |
| Repo 03 | 126K | 9K · 7% | LLM API · OpenAI | T2 | Wrapper | Wrapper | 21/100 |
+9 additional repositories
The results
AI claims substantiated, and bounded, in time for the IC.
AI claims substantiated own models and real integration confirmed in the core modules of the platform.
Rest of platform: no AI signal matching the rollout scope management described, no gap between story and code.
27% of the codebase carries genuine AI integration; the remainder shows no AI signal at all.
Findings evidenced at code level not interviews alone, mapped repository by repository against the equity story.
IC comfortable the minority investment completed on code-evidenced findings.
“Interviews gave us a story. CodeDD gave us the evidence to put that story in front of the investment committee.”
Director, Technology & AI Due Diligence, Big 4 advisory firm