Noetis Due Diligence · Business engine

Due Diligence Document Analysis API

Examines a company data room for an acquisition or partnership review.

Answers: "Can an engine build a findings log from company records we supply?" For corporate development and investment teams.

Works on supplied records. It does not connect to a data room, and findings need professional review.

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Implemented

Implemented: runs on the shared runtime with supplied material. One bounded development check on 2026-10-12 took 161.4s with 16 model calls and ended "incomplete". That is a development signal, not an evaluation. Customer access is by approved account.

What you send

Question
Supplied text material
Text material up to 150,000 characters per run

What comes back

Diligence report with material findings and evidence requests.
Findings with the located quote and a support status from claim review
Release status: released, incomplete with reasons, or failed
Executed roles, usage and measured cost

Sample

Authored illustration. Written by Encevra to show the workflow. Not customer data and not a recorded model output.

Summarize material findings from this data room extract.

CIM: ARR $4.0M, top customer 18% of revenue. Customer contract: top customer may terminate on change of control. Financials: ARR $3.6M at year end. Tech note: single engineer maintains billing system.

The real response shape is documented in the API reference.

How the workflow operates

Each role is a responsibility in the workflow, served by a third party language model. More roles are not automatically better; depth and routing decide which run.

Built with

Models this engine uses: OpenAI (GPT), Anthropic (Claude), NVIDIA (NVIDIA). Specialists run on OpenAI models and the claim review and final brief on Anthropic models. If a provider has a temporary failure, the step can fall back to an OpenAI model, and the run records that.

Data Room Mapper

Inventory documents and identify gaps.

Financial Reviewer

Analyze supplied performance and financial inconsistencies.

Commercial Reviewer

Examine customers, concentration and market assumptions.

Contract Reviewer

Identify material obligations and change of control issues.

Technical and Operational Reviewer

Examine documented systems and dependencies.

Cross Document Investigator

Connect contradictions and produce unresolved questions.

Evidence and deployment

Evidence

This engine reads the text you send in the request. Each released finding carries the exact quote and its character span in that text, located mechanically before release. Quotes that cannot be located are labelled Insufficient evidence and never shown as source verified. Interpretation is reviewed as a separate step, and gaps such as a missing clause describe what was searched instead of citing text that is not there.

Deployment

Runs on Encevra hosted infrastructure using the third party models listed above. Every model call and fallback is checked against the deployment policy first. Private cloud and air-gapped deployment are planned; discuss deployment requirements.

What it does not handle

Recursive full team launches without explicit cost and depth limits
Valuation opinions
Uses only supplied material. No freshness guarantee beyond the documents provided.
Required for every output during trials.

How to integrate

Send "engine": "due-diligence" with your question and material from your server. Keys stay server side.

How to evaluate it

No published result for this workflow yet. Build a small answer key from your own work and compare against a strong single model baseline. See the evaluation checklist and method.

Cost and rights: pricing and licensing. Showing outputs to your own customers needs a written agreement.

Schedule an engine setup call

Related engines

Content reviewed 2026-10-12 by the Encevra engine team. Engine version 0.1.0 on noetis-core-1.