inAi builds document-processing applications that move from source files to structured outputs, with review and exception handling built into the workflow.
The objective is not simply to extract some text. It is to produce information that fits the next operational step: a reviewed record, an import file, a completed form or an update to an existing system.
Define the output before selecting the extraction method
Start with the fields the destination needs, the rules they must satisfy and the evidence required to accept them. Then inspect representative inputs: clean documents, missing information, contradictory versions, unfamiliar layouts and unreadable content.
The agreed scope specifies supported file types and document families. OCR, layout processing and model-based extraction are selected where useful; support for every possible file or scan is not assumed.
A reviewable processing chain
Read and classify.
Extract and connect evidence.
Validate.
Review exceptions.
Export or integrate.
What we can build around extraction
The application can include batch queues, document previews, an editable field table, source highlighting, reviewer roles, retry controls, run reports and structured exports. Integration can be included where the destination system and access are agreed.
For example, a procurement team may need to compare fictional supplier quotations containing different quantities and delivery terms. The system can assemble comparable fields while flagging a missing currency or an inconsistent total for review.
Measure what matters to the operation
An evaluation should separate valid formatting from correct information. A JSON object can have the right shape and still contain an unsupported value.
Agree field-level correctness checks, evidence validity, exception detection and the human corrections needed to accept an output. Measure processing and review effort on stated examples; do not treat model confidence as a calibrated probability unless that calibration has actually been evaluated.
Prepare representative examples
Provide sample document families, the target schema, an explanation of required fields and examples of accepted outputs. Include difficult cases, not only the cleanest documents. Use an agreed data-sharing route for non-public material.
We can begin with a bounded pilot before connecting the workflow to production systems. The pilot should answer whether the process is useful, what remains manual and which document categories need further work.