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.
Select a pilot with a clear evaluation method
A promising pilot has repeated inputs, a person who owns the outcome, and a result that can be checked. High volume alone is not enough. If nobody can define a correct or acceptable output, it will be difficult to know whether the AI integration is improving the process.
| Candidate task | Useful test | Human control |
|---|---|---|
| Document data extraction | Field accuracy and review time across varied scans and layouts | Send missing or uncertain fields to a review queue before saving. |
| Internal knowledge search | Answer correctness, citation quality, and appropriate refusal | Restrict sources by access and show the supporting passage. |
| Report drafting | Factual consistency with approved records and editing time | Keep the output as a draft with a named approver. |
| Request classification | Correct routing, false positives, and time to resolution | Allow staff to correct the category and escalate uncertain cases. |
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.
Plan data handling, failure, and change
List the information the system will send to each model, storage service, or integration. Remove unnecessary personal or confidential data, verify provider settings and retention terms, and enforce the same access boundaries that apply in the source system. Involve the people responsible for organizational policy and risk where appropriate.
Models and providers change, so keep an evaluation set and rerun it when prompts, models, retrieval settings, or source documents change. Log enough context to investigate a poor result without exposing sensitive material to people who should not see it. Decide how staff can report errors and how the team can pause the feature if quality drops.