Choose one task and establish the baseline
Record how the task works today: inputs, responsible staff, processing time, and the mistakes that matter. Define what success would look like. An extraction tool might need to identify certain fields and send ambiguous documents to a reviewer.
Avoid starting with a general assistant expected to answer everything. A bounded task makes it easier to choose source material, check permissions, and decide when the system should stop and ask a person.
Test document extraction on difficult examples
For PDF data extraction, build a sample set that includes different layouts, scans, missing fields, and conflicting information. Define the expected output for each example. Keep a separate evaluation set so improvements are not judged only on documents used during development.
Check field accuracy and review time separately. A tool that returns convincing but incorrect values may create more work than one that clearly flags uncertainty. Keep references to the original material so staff can inspect the source.
Connect approved knowledge and retain review
For internal search, define which documents each user may access. Retrieval-augmented generation connects a model to selected material, but source retrieval alone does not guarantee a correct answer. Evaluate citations, missing information, and questions that should not be answered.
Decide where AI output enters the existing process. A draft can remain a draft until a responsible person approves it. Extracted fields can stay in a review queue before updating a system of record. Design the correction path alongside the successful result.
Budget for operation as well as development
AI integration cost depends on the document volume, model or API usage, retrieval storage, evaluation, and support. Access controls and integration with existing software also take work. Review the handling of your data with the providers involved before sending production material.
Compare the pilot with the baseline: processing time, review effort, errors, and operating cost. Expand only when the result is useful enough to maintain. Kabeli can help scope and build this kind of applied AI workflow for organizations in Nepal.