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AI Document Intake & Archive Automation

An auditable document workflow for intake, tagging, collaboration routing and archive/retrieval.

Request a fit check

Built as a bounded proposal framework for a real published buyer need. Final scope depends on approved systems, access and compliance.

Capture

Collect incoming files from approved folders, inboxes or upload points.

Classify

Apply rules plus AI-assisted tags/categories with confidence thresholds.

Route

Send documents to the correct team or collaboration location.

Archive

Preserve metadata, searchable indexes and retention rules for fast retrieval.

Commercial structure

$2,500–$7,500 pilot

Start with one measurable pilot. Expand only after acceptance criteria pass. No long contract is required for the first scope.

Fit check

Best for small offices with recurring document intake and an existing storage/collaboration platform. Final permissions and retention rules stay under buyer control.


Why this is easier to evaluate than a generic AI automation engagement

We keep the first engagement bounded: a defined workflow, explicit deliverables, acceptance checks, human-review boundaries, and documented handoff. Expansion is considered only after the pilot produces evidence worth scaling.

Price logic: the final pilot price depends on integration count, data handling, workflow complexity, review requirements, and support. If a smaller scope can answer the business question, we prefer the smaller scope.

What the pilot delivers

FAQ

Will documents be auto-filed without review?
Only where agreed confidence and policy rules permit it; uncertain cases go to review.

Can we begin without confidential files?
Yes. Sanitized samples are enough for initial design and proof-of-concept work.

How do we judge quality?
We define test cases and acceptance thresholds before recommending production rollout.

Control and accountability

Human review remains in the loop for uncertain classification, retention exceptions and consequential filing decisions. The system does not guarantee perfect classification; unsupported or low-confidence cases are routed for review.

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