All use cases
// AI Development

AI document pipeline

Documents in, structured data out, checked.

Engagement that fits
Sprint, Fixed-scope build, Product partnership
Stack
LangGraphLangChainClaude APIOpenAI APIPostgreSQLBullMQ

// Overview

Where this product fits

// Audience

Who it is for

Teams that read the same kinds of documents over and over, such as contracts, invoices, reports or filings, and copy what they find into another system by hand.

// Problem

The problem

Reading and retyping documents is slow and error-prone, the work does not scale with volume, and a model dropped in without checks returns answers nobody can trust or trace.

// Deliverables

What we would build

  • Intake from email, upload or the web

    Documents reach the pipeline however they arrive today: forwarded by email, uploaded by your team or fetched from the websites that publish them.

  • Extraction into your own schema

    A language model reads each document and fills the fields your systems expect, in your own format, instead of someone copying values by hand.

  • Translation of foreign-language text

    Documents in other languages are translated as they are read, so your team works in one language while the original text stays on record.

  • Confidence scores and review queues

    Each extracted field carries a confidence score, and anything below your threshold waits in a queue for a person to check before it moves on.

  • An evaluation set for every field

    Documents with known answers test every field, so a change of model or prompt is measured before it ever touches your data.

  • Cost tracking on every run

    Each run records what it spent on models, so you see the cost of every document and can tune the pipeline where it matters most.

// Process

How it works

  1. 01.

    Documents arrive and join a queue

    Each new document waits its turn in a queue, so a busy day slows nothing down and a failed step is retried.

  2. 02.

    A model extracts each field to a schema

    The model returns structured data that matches your schema, and anything that does not match is rejected before it is stored.

  3. 03.

    Checks flag low-confidence results

    Rules and scores flag the fields the model is unsure of, and those go to a reviewer instead of straight into your records.

  4. 04.

    Approved data flows into your system

    Checked data is written to your database or sent to the tools you use, with a link back to the source document.

What's next

Let's build
your product.

Tell us what you're building and where it stands. We'll scope it with you on a call and give you a straight answer on fit.

  1. 01

    Book a call

    Pick a time on our calendar and tell us what you want to build.

  2. 02

    Shape the scope

    Together we name the users, the first release and what success looks like.

  3. 03

    Get a proposal

    We send a written plan with the scope, the steps and the engagement that fits.

  4. 04

    Start the work

    We begin with Discovery and show you working software early and often.