Head-to-Head
askOdin vs. Probabilistic AI for Due Diligence
// A COMPILER, NOT A WRAPPER
The short answer
A generic LLM wrapper generates its verdict, so the answer can change between runs and cannot be traced to a fixed standard. askOdin uses a model only to extract claims, then evaluates them in a deterministic compiler: the same recorded variables return the same Clarity Score, with a Defensible Audit Log anchored to fingerprints of what was read.
Most "AI due diligence" tools shipping today are a prompt wrapped around someone else's general-purpose LLM. They read a data room competently and write back fluent, confident prose. For a capital decision, that is the wrong tool aimed at the wrong target.
A wrapper generates a narrative. It does not compute a verdict, it cannot reproduce one, and it cannot prove what it evaluated. askOdin is built the other way around: a deterministic compiler that issues a score with its record — the variables, the Framework version, the file fingerprints — that you can hand to an LP.
Here is the head-to-head, dimension by dimension.
The Doctrine
LLMs optimize for persuasion.
askOdin compiles for physics.
// THE HEAD-TO-HEAD MATRIX
Six dimensions that decide capital
A deterministic compiler on the left. A generic probabilistic LLM wrapper on the right. The gap is not a matter of model quality — it is a matter of architecture.
| Dimension | askOdin — Deterministic Compiler | Probabilistic LLM Wrapper |
|---|---|---|
| Data Retention // RETENTION | askOdin — Deterministic Compiler 30-DAY CEILING Your documents are never used to train a model, and raw documents are held no longer than 30 days under a documented ceiling. | Probabilistic LLM Wrapper MAY TRAIN "May train on submitted content." Under consumer terms, your confidential cap table can become one more training row. |
| Execution // EXECUTION | askOdin — Deterministic Compiler DETERMINISTIC Deterministic, statically-typed Go compiler. The math evaluates outside the neural network — the same logic path runs every time. | Probabilistic LLM Wrapper PROBABILISTIC Probabilistic next-token prediction. The verdict is sampled, not computed; temperature and model version move the answer. |
| Output // OUTPUT | askOdin — Deterministic Compiler AUDIT LOG Hash-anchored, time-stamped, IC-ready memo. A Defensible Audit Log™ an LP can open and verify two years from now. | Probabilistic LLM Wrapper UNVERIFIABLE Unverifiable chatbot text. Fluent prose with no provenance, no signature, no way to prove what was actually evaluated. |
| Role of the model // MODEL | askOdin — Deterministic Compiler EXTRACTS ONLY Extraction only. Documents go to Google's Gemini API under a paid-tier agreement that prohibits training; the verdict is computed by askOdin's own engine, outside the model. | Probabilistic LLM Wrapper MODEL DECIDES The model writes the verdict. The answer depends on how a general-purpose model reads your documents that day, under the vendor's terms. |
| Auditability // REPRODUCIBILITY | askOdin — Deterministic Compiler REPRODUCIBLE Same recorded variables, same Framework version → same score. A past result is checked from its record; the arithmetic never leaves Go. | Probabilistic LLM Wrapper NONDETERMINISTIC Nondeterministic. Ask twice, get two answers. There is no fixed standard to re-run the question against next quarter. |
| Failure Mode // FAILURE-MODE | askOdin — Deterministic Compiler PRESERVES CONFLICT Preserves contradictions. RAVEN Protocol™ cross-document triangulation surfaces the conflict between the deck and the data room. | Probabilistic LLM Wrapper HALLUCINATED RECON Smooths contradictions. The model hallucinates a tidy reconciliation, papering over exactly the discrepancy you needed to see. |
// Naming the general-purpose LLM category (ChatGPT, Claude, and their wrappers) maps the market. The contrast is architectural, not a model-quality claim.
// THE TEST THAT SETTLES IT
Ask the same question twice
The cleanest test of a diligence tool is the dullest one: ask it the same question twice. A probabilistic wrapper drifts — different prose, sometimes a different conclusion, and no record of why. A deterministic engine scores the variables it extracted, and the same recorded variables under the same Framework version return the same score. Extraction still runs on a model that can change, so a past result is checked from its record, not by hoping a fresh run matches. That is not a nicety. That is what lets an investment committee rely on the number.
$ odin score --record audit-0412.json
+ Clarity Score: 41 / 100 framework: stamped files sha256:9f3a…c1d7
$ odin score --record audit-0412.json
+ Clarity Score: 41 / 100 framework: stamped files sha256:9f3a…c1d7
// same recorded variables → same version → same score
- probabilistic wrapper, run 1: "Strong, fundable team."
- probabilistic wrapper, run 2: "Some concerns on retention."
// no record, no standard, nothing to audit
// Benchmark corpus built on public deal data. Every askOdin verdict is scored against this standard.
// FAILURE MODE
A wrapper smooths the contradiction. askOdin keeps it.
The most dangerous thing a probabilistic model does in diligence is reconcile. Hand it a deck claiming 140% net revenue retention and a data room showing churn that says otherwise, and a generative model will write you a confident paragraph that quietly splits the difference. The discrepancy — the single most important signal in the room — gets hallucinated away.
RAVEN Protocol™ does the opposite. Its cross-document triangulation is a verification layer for heterogeneous data rooms: it holds the deck and the data room side by side and preserves the conflict instead of resolving it for you. The contradiction is the output.
// source A — investor_deck.pptx, slide 14
CLAIM: net revenue retention = 140%
// source B — finance_export.xlsx, cohort tab
DERIVED: net revenue retention = 88%
! CONTRADICTION PRESERVED — delta 52 pts, not reconciled
// flagged for IC review, both sources cited
The architectural mechanics of RAVEN's triangulation engine are protected under U.S. Provisional Patent No. 63/994,876 and are not publicly disclosed.
A wrapper gives you a paragraph you have to trust.
askOdin gives you a verdict you can audit.
// COMPILER VS. WRAPPER
Frequently Asked Questions
Can ChatGPT do due diligence?
It can do parts of it: read documents, summarize them and draft a memo. It cannot give you a verdict you can defend, because the verdict is sampled rather than computed, so it can change between runs and points to no fixed standard. In our Theranos backtest, ChatGPT declined the deal because the narrative was vague, not because the numbers contradicted each other. Use it for triage; use a deterministic evaluation for the decision.
What is an LLM wrapper?
A product built as a prompt around someone else's general-purpose language model. It reads documents competently and writes fluent answers, but the verdict is sampled by the model rather than computed, so it can change between runs and cannot show a fixed standard it was measured against.
Does askOdin use a large language model at all?
Yes, for extraction only. A model reads the documents and pulls out typed claims. It does not evaluate them. The evaluation runs in a deterministic Go engine outside the model, which is why the same recorded variables return the same Clarity Score under the same Framework version. The model reads; the compiler judges. The architecture is covered in Deterministic vs. Probabilistic AI.
Why does reproducibility matter for an investment decision?
Because the question an LP, a regulator or a board eventually asks is what the basis for the decision was. A verdict that comes out differently when you ask twice cannot answer that. A deterministic one keeps a record of the variables it scored and the Framework version it used, so months later the basis is read from that record. Extraction runs on a model that can change, so the record, not a fresh run, is the evidence.
Can a wrapper catch a contradiction between the deck and the financial model?
Sometimes, but it cannot guarantee it, and it will often reconcile the two into one plausible figure instead. RAVEN Protocol cross-document triangulation holds the conflict and cites both sources, so the discrepancy reaches the investment committee instead of being written away.
For Allocators
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