Transactions & Investment
If the thesis depends on AI, test the AI.
Independent AI judgment when an investment thesis, acquisition case, or strategic partnership depends on claims about technology and enterprise adoption.
The questions we own
Six answers that move a valuation.
- Is the claimed AI capability real?
- Does it materially affect the business model?
- Are the adoption assumptions credible?
- Is the AI architecture economically sustainable at scale?
- Does the organization have the operating capability to execute the thesis?
- Is AI a real source of value here, or a narrative laid over the top?
Who brings us in
Where AI claims are material to price.
- Private equity
- Strategic acquirers
- Boards
- Investment committees
- Corporate development
- Venture and growth investors
- Lenders and partners
Scope
Strategic and operational diligence.
- AI strategy diligence
- Product and technology claim review
- Adoption assumptions and customer evidence
- AI economics, including inference cost at the modeled volume
- Business-model dependence on AI
- Integration risk
- Operating-model implications after close
- Post-transaction AI thesis review
This is strategic and operational diligence. It is not a source-code audit, and it does not replace technical, legal, or financial diligence performed by your existing providers.
Why it matters now
Almost every book has an AI story in it.
A demo runs. A pipeline looks strong. A model is genuinely good at one narrow thing. None of that tells an investment committee whether the capability is defensible, whether customers will change how they work to use it, or whether unit economics survive the volume in the model.
Eldris answers those questions from the position of someone who builds AI products and has operated inside large enterprises where adoption either happens or quietly does not.