Free Fit Review
Define the firm, use case, transaction context, document types, current workflow, desired output, users, timeline, and data-handling requirements. No confidential employee-level files are needed at this stage.
Engagement process
Start with the smallest useful layer. Expand without rebuilding.
The engagement begins with the decision, document type, evidence standard, and acceptance criteria—not a generic automation promise. Each successful layer can become the structured input to the next.
Select the entry layer
Choose a focused parser pilot, a managed full-document diligence review, a Diligence OS workflow, or a client-dashboard use case. The recommendation is based on the smallest scope that can produce a useful result.
Define acceptance criteria
Agree on field definitions, JSON schema, evidence traceability, exception handling, reviewer workflow, report requirements, DRL behavior, and what qualifies as a successful pilot.
Run and validate the pilot
Process a bounded document population, compare outputs to source pages, correct mappings, classify failure modes, and measure whether the result improves speed without reducing professional review quality.
Expand into managed diligence
Add document types, full data-room classification, cross-document reconciliation, buyer information, formal report generation, and living DRL updates inside the same evidence model.
Embed operating and client layers
Move validated diligence into the Diligence OS for control, ownership, readiness, and handoff; then expose the appropriate live status and outcomes through client-facing dashboards.
Founding parser pilot
A limited license can be earned through useful validation—not vague feedback.
For qualified early firms, Nautilus may offer a limited no-cost or reduced-cost SBC or OE/New Hire Guide parser license. The pilot is bounded by a defined document type, testing population, validation method, and feedback schedule.
- Representative, lawfully shareable test documents
- Named reviewers who understand the source material
- Field-by-field confirmation, correction, rejection, or missing status
- Permission to retain de-identified correction patterns where agreed
- Clear deployment, confidentiality, and license terms
Acceptance criteria
Measure usefulness at the point of work.
- Document classification accuracy
- Required-field completeness
- Value normalization and schema stability
- Source-page and evidence traceability
- Exception and conflicting-evidence handling
- Reviewer correction time
- Repeatability across document variants
- Usable downstream JSON or report input
Where the approach adds value
Good-fit indicators.
Repetitive document work
Expert teams repeatedly extract the same fields from SBCs, guides, invoices, rates, or plan documents and need stable structured data.
Knowledge is lost at handoff
Evidence, open items, assumptions, seller responses, and implementation decisions become separated when work moves between teams.
Clients need live visibility
Static reports cannot show readiness, aging issues, owners, decisions, and post-close progress across an active transaction or portfolio.