Evidence-led applications · Generative AI Applications

AI Document Intelligence Application Development

Document intelligence supports casework only while every generated conclusion keeps its exact passage, source version, uncertainty, conflicting evidence, analyst correction, and review status visible until the case owner decides.

We build a workspace where analysts compare documents, open the passage and version behind a conclusion, and see conflicts or uncertainty before deciding what belongs in a case.

Analysts and the case owner take over a workspace, document-passage evidence model, contradictory-document findings, and source-update plan that separate generated output from reviewed case decisions.

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Illustration of AI Document Intelligence Application Development: a team assembling a generative AI application from its components

Some of the 500+ brands we've worked with

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  • PepsiCo
  • Lexus
  • Sporx
  • HangiKredi
  • Grandvision
  • Quick Sigorta

Steps, gates, and who decides

How we work

The evidence trail comes before the interface. Once that path is clear, the workspace can be shaped around the points where analysts compare, challenge, correct, and approve a conclusion.

  1. Follow documents into decisions

    We sample document families and versions alongside analyst decisions and the case flow around them. This shows where passages conflict, where context gets lost, and which conclusions need a visible evidence trail.

    AI assist
    The model groups sampled documents by family and flags version conflicts for an analyst to inspect.
    Human gate
    Are the sources permitted, representative, and versioned? Your case owner confirms which document sources are permitted to use.

  2. Put review around the analyst's task

    Comparison, citations, uncertainty, correction, and escalation are designed around the casework itself. Analysts can challenge a conclusion without dropping the context they need to decide.

    AI assist
    Sampled workflows give the model material for candidate comparison and citation layouts. The lead analyst decides what belongs together.
    Human gate
    Can a reviewer challenge a conclusion without leaving the case? Your lead analyst approves which review actions stay inside one screen.

  3. Connect evidence to the case flow

    We link the evidence model to relevant case states, permissions, and feedback. Generated output stays visibly separate from a decision an analyst has reviewed and approved.

    AI assist
    Existing workflow material lets the model draft possible case-state transitions and permission boundaries for the system owner to check.
    Human gate
    Does each action retain its source and reviewer context? Your system owner approves which case states the workspace may touch.

  4. Test what happens when sources disagree

    Representative documents and deliberately conflicting versions run through the complete workflow. Reviewers inspect unsupported conclusions, stale citations, hidden uncertainty, and the corrections analysts make.

    AI assist
    Across the test set, the model finds unsupported conclusions and citations pointing at old versions for a reviewer to verify.
    Human gate
    Do the agreed evidence and review thresholds hold for critical cases? Your review owner decides whether a flagged conclusion blocks release.

Named artifacts you keep

What you get

The handoff covers the workspace, the evidence relationships beneath it, and the checks that keep each conclusion open to review.

  • Architecture document

    Document-passage evidence and reasoning model

    The relationships among document, passage, version, conclusion, uncertainty, conflict, and completed review.

  • Dashboard

    Document comparison and analyst-review workspace

    A working interface for comparing documents, opening citations, correcting generated output, and recording the analyst's decision.

  • Test evidence

    Contradictory-document case findings

    Representative, contradictory, and failure-oriented cases paired with review thresholds and escalation rules.

  • Playbook

    Case workflow and source-update operations pack

    Operating guidance for case intake, analyst feedback, source updates, exception handling, and audit review.

Scope and honest limits

When to bring us in

This work fits a document-heavy review process where every useful conclusion still needs its passage, version, and assumptions close by.

A good fit when

  • Your analysts spend hours matching versions and citations, but passages that disagree still reach the case without a resolved source trail.
  • A summary is useful only when its reviewer can open the exact passage and version, yet the current workflow drops that context before the case decision.
  • The case owner is asked to approve release, but evidence, correction, and escalation thresholds have not been agreed for unsupported conclusions.
  • Documents are compared and summarized, but conclusions reach reviewers without links to the supporting passage and source version.
  • The analyst workspace shows generated output, yet uncertainty, corrections, review status, and the final decision are not kept visibly separate.
  • The case workflow accepts generated conclusions, but evidence-grounded tests and reviewer feedback do not follow them into monitoring.
  • A conclusion enters the case record, though nobody can trace it back to the source version and evidence the analyst accepted.

Better handled as other work when

  • You need Zeo to interpret the documents as legal or regulatory advice. We expose the passages and the conflicts, and your counsel decides what they oblige.
  • You want generated summaries entered as verified facts before analyst review. The workspace keeps them provisional until a reviewer accepts the evidence.
  • You need document rights acquired, production run, or adjacent case systems changed. The application covers this workflow, while those tasks need separate scopes.

If one of these is closer to your situation, start here instead: Review the application development service

Engineers who ship production AI

This is the part of Zeo that writes and ships code. Our senior engineers build agents, chatbots, and RAG pipelines, along with the automation and data work around them, and they keep operating those systems once they're live. We've worked with more than 500 brands since 2011.

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Tools we use

Tools behind this work

  • Mistral AI

    turns scanned or image-based filings into machine-readable text first

  • Anthropic

    compares multiple full documents in one pass without losing the citation trail

  • Unstructured

    partitions filings into clean text, table, and layout elements before indexing

  • LlamaIndex

    the index that keeps every passage linked to its source document and version

  • Chroma

    the passage-level index an analyst's query searches during a live comparison

  • Confident AI / DeepEval

    checks that a stated conclusion is actually supported by its cited passage

  • Label Studio

    the second human read a disputed passage-to-conclusion link gets routed to

Next step

Bring one real document review case

Share representative documents and their versions with the person who decides what counts as acceptable evidence. We'll map the trail from passage to reviewed conclusion.
Plan the workspace

Before you decide

Questions we hear a lot

Representative document families and versions, examples of analyst decisions, the required source links, the case workflow, privacy constraints, reviewer capacity, and evidence thresholds. A named owner must also be able to authorize source use and settle conflicts between documents or policies.