An enterprise services firm · Enterprise AI
Turning a document backlog into minutes, not weeks
A team of specialists spent their days keying data out of contracts and forms. We built an AI pipeline that reads the documents and drafts the entries — with a human confirming, not transcribing.
Written by Dinesh Khire
- Processing time per document
- Days → minutes
- Manual keying removed
- ~70%
- Exceptions caught earlier
- +3x
The challenge
Our client’s business ran on documents — contracts, forms, statements arriving in every format a counterparty could invent. A skilled team spent most of their week doing something no one enjoys and everyone acknowledges is waste: reading each document and typing the important fields into the systems downstream. It was slow, it didn’t scale without hiring, and the tedium bred exactly the errors it was meant to prevent.
They’d seen the AI demos. What they hadn’t seen was a way to trust an AI with work that fed real decisions and real records.
The approach
We were blunt about where AI belongs and where it doesn’t. Reading a document and proposing the fields is a good fit for a model. Being accountable for what lands in the system of record is not — that stays with a person. So we designed the AI as a component inside a workflow, not a replacement for the team.
- We built an extraction pipeline that reads each document, pulls the fields that matter, and drafts the entry — handling the messy formats that break rigid, template-based tools.
- We kept a human in the loop by design. The specialist confirms or corrects a pre-filled draft instead of transcribing from scratch, so the work shifts from typing to judgement — faster, and less error-prone.
- We engineered for trust: every field traces back to where it was found, low confidence is flagged for a closer look, and we measure accuracy continuously, so quality drift shows up as a number rather than a surprise.
Because the same team builds and runs the pipeline, the accuracy, traceability and monitoring weren’t afterthoughts. They were the point.
The impact
What took days now takes minutes, and roughly seventy per cent of the manual keying simply went away — the team’s time moved from transcription to the judgement calls that actually need a person. Because the model surfaces uncertainty instead of hiding it, genuine exceptions are caught earlier, not discovered downstream. The AI never made a decision on its own. It just took the tedium out of the ones people were already making.
