AI is great at one-off ROI math. It’s unreliable at consistency, governance, and CFO defensibility across hundreds of deals a quarter.
It’s working on the deals you can see. The problem is the deals where the rep built a business case with a fabricated benchmark, or the deal where the champion couldn’t defend the numbers and went quiet, or the 4 hours a week each rep is spending rebuilding what someone else already built last month. Those failures are invisible until you realize your value motion doesn’t scale — and by then you’ve lost quarters, not just deals.
Yes — and that’s the point. The model is the engine, not the car. ValueNova wraps the LLM in evals, reusable blueprints, guardrails, a curated benchmark library, and an audit trail. Without that wrapper, you get a different answer every time.
You’ll have a calculator. Then you need someone to maintain it, govern it, version it, and secure it. When that engineer leaves — or when the CFO asks why the numbers don’t match the last deal — you’re back to square one. That’s the build-vs-buy conversation — see our full analysis at /compare/build-vs-buy.
ValueNova supports configurable model providers for enterprise customers, including private deployments. The governance layer is what matters, not which underlying model produces the draft.
Benchmarks come from a maintained, source-cited library — not from the model. The AI proposes; the library disposes. If a number isn’t backed by a source we can show the buyer, it doesn’t make it into the deliverable.
Compare ValueNova to traditional spreadsheet-based ROI calculators and business cases. See why leading sales teams are moving beyond Excel for value engineering.
Should you build custom ROI software or buy a platform like ValueNova? A comprehensive analysis of costs, timelines, and trade-offs.
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