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Production AI systems for decision-critical work

Turn complex knowledge workflows into reliable AI products.

Mopa Labs designs and ships AI workflow systems for teams working with dense documents, fragmented data, and high-stakes decisions, from first validated use case to secure production software.

Discuss a workflowSee the offers →
mopa · eval

$ mopa eval run --workflow agreement-review

ingesting batch: 38 documents…

extracting 12 fields per document

evaluating against baseline v3…

✓ 33 documents pass: all fields within tolerance

⚠ 4 low-confidence extractions → human review queue

✗ 1 flagged: scanned amendment, table split across pages

✓ run complete: 33 pass · 4 in review · 1 flagged · 0 silent failures

Illustrative run on synthetic documents. Your evaluation runs on your workflow.

// the situation

  • The documents arrive faster than your team can read them.
  • The answer exists, split across five systems and one person’s memory.
  • Every week, expensive people extract the same fields from the same documents by hand.
  • There is a prototype that impresses in demos, and no one trusts it with real work.
  • One wrong number in a client report costs more than a year of any tooling.

These are workflow problems. They yield to systems that are measured, reviewed by humans where it matters, and engineered to operate, not to another demo.

// three ways to start

The offers

Every engagement is fixed-scope, with a written proposal after a diagnostic conversation.

// 01 · prove the use case

AI Workflow Value Sprint

One use case proven, or killed, in two to three weeks.

for
Operators with many candidate AI ideas and no defensible priority.
duration
2–3 weeks
  • Workflow map: how the work actually moves, where it stalls, and who touches it
  • Baseline economics: what the workflow costs today in time, money, and error
  • Risk and evaluation plan: what failure looks like and how the system will be measured
  • Interactive proof on representative documents
  • + 2 more on the offer page

A fit when

  • You have more candidate AI ideas than capacity and no defensible way to rank them
  • The workflow runs on documents or decisions with a real cost of error
  • You want evidence before committing to a build
Read the full offer →

// 02 · build the system

Production Agent System

A measurable operating workflow, not an AI demo.

for
Teams with a proven document or decision workflow ready to run on production software.
duration
6–12 weeks
  • Production application your team works in
  • Integrations with the systems where your documents and data live
  • Retrieval and tool layer grounded in your documents
  • Evaluation suite that runs on every change
  • + 4 more on the offer page

A fit when

  • The workflow is proven, by a Value Sprint or by evidence you already have
  • Your organization controls the documents and data the system needs
  • A workflow owner can commit review time during the build
Read the full offer →

// 03 · make it hold

AI Product Engineering / Prototype Rescue

From promising prototype to software real users can rely on.

for
Founders and innovation teams with a working prototype that cannot yet support real users.
duration
4–10 weeks
  • Architecture audit: a scored report across security, reliability, data, testing, and deployability
  • Your top risks in plain language, each with its blast radius
  • A prioritized fix roadmap
  • Product simplification down to the workflow that matters
  • + 4 more on the offer page

A fit when

  • The prototype works in demos but you would not put it in front of paying users yet
  • You need to know what is fragile before you commit more money to features
  • Real users, a launch, or a diligence process is on the calendar
Read the full offer →

Reliability & Improvement Retainer, available only after a Mopa build.

// also

Capabilities

The three offers are how most engagements begin. Around them, the studio builds the rest of the software a production system needs.

Build
Full Stack Development · Mobile App Development · MVP Building
Intelligence
AI Agents & Integration · Data & Analytics
Design & Quality
UI/UX Design · Testing & Experimentation
Advisory
Consulting
See capabilities →

// real artifacts

How we prove it

Client work is published only with written permission. Until then, the method shows itself: real artifacts, honest about what they are.

Sample evaluation: synthetic data

Evaluation report: document-extraction workflow

A complete evaluation report run on a synthetic document corpus built for this purpose: baseline versus system accuracy per field, a failure-mode table, and the human-review control assigned to each failure class. The data is synthetic; the method is exactly what your workflow gets.

Reference architecture

Reference architecture: production document workflow

The architecture a Mopa build follows for a document workflow: ingestion, extraction, retrieval and tool layer, evaluation harness, human review queue, observability, and access controls, drawn the way we draw it for a real engagement.

Deliverable specimen: worked example

Value Sprint report specimen

A specimen of the report a Value Sprint produces (workflow map, baseline economics, risk and evaluation plan, and the build-or-stop recommendation), filled in with a worked example so you can judge the deliverable before buying it.

// method

Discover. Prove. Build. Operate.

  1. // 01

    Discover

    Map the workflow as it actually runs: the documents, the systems, the people, and where time and errors concentrate. Establish what the work costs today.

    evaluation gate
    A measurable baseline (time, cost, error rate) agreed in writing before anything is built.
    human control
    The workflow owner sets scope and priority; nothing enters the plan without their sign-off.
  2. // 02

    Prove

    Take the narrowest valuable slice of the workflow and build an interactive proof on representative documents, measured against the baseline.

    evaluation gate
    The proof beats the baseline on an agreed evaluation set, or the use case is killed and the reasons documented.
    human control
    Your domain experts judge the outputs and define which failure modes are acceptable, which need review, and which are disqualifying.
  3. // 03

    Build

    Engineer the production system: integrations, retrieval and tool layer, review queues, observability, and access controls, built around the proven workflow, not around a demo.

    evaluation gate
    The evaluation suite runs on every change; a regression against the baseline blocks the release.
    human control
    Low-confidence output routes to human review by design, and reviewers can correct the system where it matters.
  4. // 04

    Operate

    Run the system on real work, measure it in production, and hand it off: documentation, walkthroughs, and a team that can maintain it without us.

    evaluation gate
    Production metrics tracked against the baseline; drift is detected and reported, not discovered.
    human control
    Your team owns the system and the escalation paths; people decide what happens when the system is unsure.

Evaluation and human control are not a stage; they run through all four.

// how we work

Operating principles

  1. // 01

    Measurable baseline

    Before we build, we measure how the work happens today: time, cost, error rate. Progress is a comparison against that baseline, not an impression.

  2. // 02

    Narrow first workflow

    The first release does one workflow well. Breadth comes after the system has earned trust on real work.

  3. // 03

    Visible failure modes

    Every system fails somewhere. We document where, how often, and what happens next, so failure is a known, controlled quantity instead of a surprise.

  4. // 04

    Secure by design

    Access controls, data boundaries, and audit trails are part of the architecture from the first commit, not a hardening pass at the end.

  5. // 05

    Handoff without hostage-taking

    You own the code, the infrastructure, and the documentation. We build so your team can run the system without us, and we say so in the contract.

// fit

Who this is for

Mopa Labs is for

  • Teams whose work runs on dense documents and consequential decisions: asset managers, financial operators, research and reporting groups.
  • Energy, infrastructure, and engineering organizations with technical documents, models, and decision workflows.
  • Technical founders with a working prototype that has to grow up.
  • A workflow owner with the authority to change the process, representative documents to test against, and a real cost when the work goes wrong.

A fit is unlikely if

  • Staff augmentation: we scope and deliver outcomes rather than placing engineers on your team.
  • Model research without an operating workflow behind it.
  • Certified compliance guarantees: we engineer controls and evidence; we don’t issue certifications.
  • A build priced before the workflow is understood.

// questions

Straight answers

What is a production AI system?

An AI system real users depend on for real work, not a demo. It runs on your live documents and data, measures itself against a baseline on every change, routes low-confidence output to human review, and ships with observability, access controls, and audit trails. The test is simple: a workflow owner can rely on it, see what it did and why, and correct it when it is wrong.

How do you handle our documents and data?

Under NDA, signed before we see anything. During an engagement your data stays in environments you control or approve, access is limited to the people working on it, and nothing you share is used to train models.

Which AI models do you use?

The ones the evaluation supports. We select models per workflow (commercial APIs, or self-hosted where data requirements demand it), and we architect so that a model or provider change is a configuration decision, not a rebuild.

Who owns what you build?

You do. Code, prompts, evaluation suites, infrastructure, and documentation are yours at handoff. We retain only our own pre-existing internal tooling and methods.

Where does the system run?

In your cloud accounts wherever possible, behind your access controls. If we host anything during a build, the handoff plan moves it onto infrastructure you own. This applies to existing codebases too, including AI-built prototypes; we work from your exported code or a connected repo.

How long do engagements take?

A Value Sprint runs two to three weeks. A production build typically runs six to twelve; a prototype rescue, four to ten. Every engagement is fixed-scope, with a written proposal after a diagnostic conversation.

How do you price engagements?

Every engagement is fixed-scope. After a diagnostic conversation we send a written proposal with the scope, deliverables, and a fixed figure, so there is no open-ended meter running. Changes to scope are changes to the proposal, decided together.

How do we work together?

You work directly with the people building the system: one primary contact, a shared channel, and a review cadence agreed at kickoff. Scope is fixed in writing; changes to scope are changes to the proposal, decided together.

// intake

Discuss a workflow

Tell us about the workflow. Enough context here makes the first call a working session instead of a discovery call.

We reply from a mopalabs.com address.
Select an option
What the work is, roughly how often it happens, and where it slows down or goes wrong.
Where the documents and data live. Names are fine; credentials are not.
When does this need to move?
Select a range
Helps us propose the right first step. Nothing here is binding.

We'll use what you share here only to respond and prepare for the conversation: no mailing lists, no sharing with third parties (privacy). An NDA is available before any material changes hands.

We use analytics to improve the experience. Non-essential tracking only starts after you opt in.