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Document intake & classification
Identify document types, route files to the appropriate workflow and handle incomplete, unsupported or unreadable inputs explicitly.
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Put AI to work on the information your business already holds. Extract document data, find relevant knowledge and support everyday decisions, with source evidence, evaluation and human review built in.
Classify inputs, extract relevant information and keep source references available for inspection.
Retrieve permitted knowledge, compare related records and evaluate results against representative examples.
Present uncertainty, conflicts and failed checks to a reviewer before approved results update another system.
Choose a specific task, a representative set of inputs and a clear acceptance process. Connect model capabilities to business rules and decisions that people can inspect.
COMMON STARTING POINTS
Identify document types, route files to the appropriate workflow and handle incomplete, unsupported or unreadable inputs explicitly.
Extract relevant values from PDFs, scans and forms. Retain source references so reviewers can inspect the evidence behind each result.
Check required fields, apply business rules and compare related records. Separate a model’s interpretation from fixed business checks and approval decisions.
Retrieve permitted information and generate source-linked answers or drafts. Define how the assistant handles missing, conflicting or out-of-date evidence.
Route exceptions to the right person with the supporting context. Connect approved results to existing systems through agreed interfaces.
Measure quality on representative samples. Track failures, reviewer effort, latency and usage cost as the workflow moves from pilot to wider use.
Illustrative scenario: an accounts team receives an invoice and purchase order. The workflow extracts their values, highlights discrepancies and retains the evidence needed for approval.
Classify the files, extract relevant fields and retain links to source pages. Flag unreadable or incomplete information for review.
Compare identifiers, quantities and totals against business rules and related records. Keep mismatches visible instead of silently accepting them.
Show the reviewer the extracted value, its source and the failed check. Record the correction or approval with the relevant context.
Send the approved record through the agreed interface. Track acknowledgements, duplicate handling and any failed updates that need attention.
A pilot should establish where the approach works, where it fails and what it needs to operate. Acceptance criteria and review requirements are defined before expansion.
The task, available inputs, permissions, review requirements and a representative set of evaluation cases.
A focused flow covering intake, model output, business checks and the agreed reviewer or system handoff.
Quality results, error categories, limitations and the operating considerations that inform a rollout decision.
Access boundaries, monitoring, feedback handling, usage controls and responsibilities for maintaining the workflow.
Clarify the scope, responsibilities and decisions behind a useful engagement.
We agree the task and success criteria, then compare results with labeled, representative examples. We examine important error types separately, including missing fields, unsupported answers and incorrectly accepted records, rather than relying on a single headline score.
That depends on the selected service and its configuration and data terms. These are reviewed before connecting your information. We agree permitted data, retention, access and hosting requirements for the proposed solution.
Not necessarily. Existing models combined with retrieval, structured extraction and business rules may suit the task. The pilot helps determine whether configuration is sufficient or whether further model work is justified.
The workflow needs explicit fallback and review paths. Unreadable inputs, missing evidence, conflicting records and failed checks can be routed to a person, with the original context available for correction.
Input volume and size, the selected services, retrieval, integrations and human review all matter. A pilot can measure usage, latency and reviewer effort to inform a realistic operating estimate before wider rollout.