InnoRate: AI commercialization analysis with an evidence ledger

The situation

Technology commercialization analysis is expensive, slow, and inconsistent. A technology transfer office or a VC analyst reads a disclosure, googles around, reads a few market reports, writes a memo. The memo takes a week and it's stale the day it's filed. Different analysts produce different memos on the same technology, because the inputs and the reasoning aren't captured anywhere.

We wanted to know whether an evaluation pipeline could produce a report of the same shape and quality every time, with a record of how it got there that a human can audit.

What we built

InnoRate is a commercialization-analysis platform with an API. A user uploads documents or pastes in a description. The system detects the innovations, then runs the generation pipeline through six steps.

1. Research. The system plans every web query for every section in advance, deduplicates them, and runs them as a single batch so nothing is repeated and nothing is missed.
2. Parallel section generation across eleven section types (technology overview, development stage, IP status, commercialization strategy, competitive picture, market analysis, regulatory compliance, ESG impact, potential licensees, risk assessment, and an investment memo). Sections are written concurrently.
3. Self-refine. Each section runs its own self-refine loop with up to three iterations and early exit on quality.
4. Inline evidence. Evidence records are produced alongside each claim, in the same generation pass. Every claim is typed (web research, document-stated fact, analytical estimate, industry knowledge, logical inference, unsupported) and classified (quantitative, qualitative, comparative, causal, predictive).
5. Chain-of-Verification. The system runs verification on high-risk claims. Analytical estimates, industry-knowledge claims, and low-confidence quantitatives get re-researched against the live web. The system marks the result as verified, corrected, contradicted, or unverified.
6. Evidence ledger. The report ships with a structured ledger capturing every claim, its type, its sources, and its verification status.

How a claim earns its place

Inline evidence is the core move. Most AI report generators produce a report and then ask a second pass to "add citations." That second pass is a hallucination pipeline in a hat. The model invents citations that look plausible. InnoRate produces the evidence alongside the claim, from the same context, with the discipline that a claim without evidence is a structural error. The system refuses to ship those.

Chain-of-Verification catches the claims the model might have been confident about for the wrong reasons. An industry-knowledge claim ("this market is growing at 12% annually") is exactly the kind of thing an LLM will state with false confidence. Verification sends those claims back to the web, checks them, and flags the ones that come back corrected or contradicted. The user sees those flags in the report.

Every section has to satisfy a defined structure or it doesn't ship. There is no quiet fallback to templated prose to paper over a section that didn't generate cleanly.

What it replaced

A manual process that took a week per disclosure and produced inconsistent memos. Or a generic LLM chat that produced confident prose with invented citations.

What a similar engagement looks like

10 to 14 weeks to deploy InnoRate (or an InnoRate-shaped product) for a new domain. We need the domain's section taxonomy, reference examples of the reports you want to produce, access to any domain-specific data sources, and subject-matter-expert review time. You get the deployed platform, the API, the section logic tuned for your vertical, and the evidence ledger.

Reports of the same shape and quality arrive in hours, with every claim typed and verified. The people who used to spend a week writing one report can spend that week deciding what to do about ten. The partner discussion shifts from whether to evaluate to what to do about the evaluation.

It's a fit for tech transfer offices, VC evaluation teams, due-diligence shops, and any organization that produces standardized analytical reports at volume and needs to stand behind the numbers. It's the wrong product when every report is its own one-off and no two share a section taxonomy.