Proof of Concept

8-12 weeks with our senior team. You leave with a working prototype on your real data - measured against your own accuracy standard- plus a technical plan and a budget for the full build.
Internet Archive
Tempest
Xperi
Foba
DTS
Stevens

When You Need a PoC

  • [1]

    You have an AI idea but no proof it works on your data

    Demos and benchmarks rarely match real production data. A PoC tests on your actual files – drawings, PDFs, contracts, whatever you handle daily.

  • [2]

    Off-the-shelf tools failed on your real workflows

    You tried existing solutions. They worked on demos but broke on your documents. A PoC tells you whether custom is worth it – and what it would take.

  • [3]

    Your data is messy, fragmented, or one-of-a-kind

    Custom workflows mean custom risk. Better to discover the edge cases in 8 weeks than 8 months into an MVP.

  • [4]

    Internal stakeholders need proof before signing the MVP budget

    Hard to greenlight a 6-figure build on a slide deck. A working prototype on real data turns the conversation from “if” to “how big”.

What a POC saves you from

Building an MVP that won't work on real data

Most AI projects fail on data quality, not algorithms. A PoC surfaces this in weeks, not after a six-figure spend.

Architecture decisions that scale badly

The first design choices – data pipeline, model type, integration approach – determine cost and speed at scale. Hard to fix later. Cheap to fix now.

Vendor demos that hide the hard parts

Most demos run on cherry-picked data. A PoC on your files shows you what’s actually possible vs. what’s marketing.

Hidden compute costs

Some AI approaches look cheap until you scale. A PoC gives a real cost model – inference, storage, retraining – before you commit.

Wrong scoping for the MVP

Without a PoC, MVP estimates are guesses. With one, you have measured accuracy, real processing times, and a defensible budget.

Buying generic AI when you need custom

A PoC tells you if a $20/month tool would do the job – or if your workflow genuinely needs a custom build. Either answer saves money.

How it works

3 steps of the PoC

A step by step process led by senior AI engineers and architects.
From understanding your system to a ready to use action plan.

[1]

Discovery call

A 30-minute conversation about your workflow, data, and goal.
[2]

Free tech review

We assess real samples of your data and tell you straight whether AI can do the job.
[3]

PoC Build

Our team works on your data: annotation, model selection, training, validation. You leave with a working prototype.

PoC Pricing

Tech Verification

Have hypothesis how AI can boost your operations?

We’ll check what is technically possible to do. Just a clear ‘yes’ or better alternatives from our core tech team.

Always free*

0 / week

Ideal for:

  • Any project idea, big or small
  • Non-technical founders or executives who need tech validation
  • Businesses that are looking into AI, and how it can boost their operations

Team:

Deliverables:

  • A clear "green light" to proceed, or suggestions for alternatives
  • A deliverables list for the PoC

PoC Package

A working prototype on your data, validated against your real workflow. You get accuracy numbers, architecture, and a budget for what comes next.

$20k-60k / 8-12 weeks

Ideal for:

  • You want AI in production but need proof it works on your data
  • You need a working prototype to greenlight the MVP budget
  • Off-the-shelf tools didn't fit your workflow
  • You're comparing vendors and want apples-to-apples on real data

Team:

Deliverables:

  • Working prototype trained on your data
  • Accuracy report - recall, precision, and real-world performance measured against your own benchmark
  • Architecture plan for the MVP
  • Cost and timeline estimate for production build

Stevens Industries: PoC in 12 weeks

An AI prototype trained on their actual production drawings. 90% accuracy in 12 weeks – enough for Stevens to greenlight the MVP now in daily use.

AI/ML Software Development

Takeoff Tool Proof of Concept for Stevens Industries

Read case
[1]

An AI prototype that read real architectural drawings and detected 14 cabinet types at 90% accuracy. Result greenlit the production build Stevens runs today.

Frequently Asked Questions

  • A working prototype trained on your real data, plus a written report covering accuracy metrics (recall, precision, F1), architecture decisions and why we made them, infrastructure cost estimates for production, edge cases we found, and a prioritized MVP plan with budget and timeline.

    The prototype isn’t a slide deck demo – it’s a runnable system you can show internal stakeholders and use to validate the business case before committing to a full MVP build.

  • You do. Default is full IP transfer – all code, models, and training data stay yours. We retain methodology and general domain knowledge only, nothing client-specific.

    This matters if you want to hand the PoC to another team for the MVP, or take what we built in-house. It’s written into the contract before any work starts.

  • For the free technical review: a small sample, usually 5–20 documents. For the full PoC: a larger labeled or labelable dataset – exact volume depends on the problem and gets scoped in the discovery call.

    NDA before any access. Data stays in your environment when possible; when we need transfers, we use your secure channel of choice. Same protections we use on long-term builds for Stevens Industries and Internet Archive.

  • That’s still a successful PoC. Better to find out in 6 weeks than 6 months into an MVP. Roughly a third of these engagements end with “don’t build this” or “build a simpler version” – and clients tell us that answer alone paid for the work.

    You still walk away with the accuracy report, the edge cases we documented, and a clear understanding of what would need to change for AI to work – useful even if you go a different direction.

  • Three factors: data complexity (clean structured PDFs sit at the low end, mixed-format legacy documents at the high end), how much annotation we need (existing labeled data shortens the timeline), and whether we’re testing one model approach or comparing several.

  • The report gives you a scope, budget, and timeline for production. From there: a short proposal, contract, and we either continue with the same team or scale up for the MVP build (typically 3-6 months, $80–150k depending on integrations).

    We’ve done this trajectory end-to-end with Stevens Industries – PoC in late 2023, MVP launched in 2024, system now in daily production use. You’re not obligated to continue with us though; the PoC deliverables are vendor-neutral by design.

    • [1]

      A slice of real data

      Ideally examples you've already processed, so we can measure the AI against known-good answers.
    • [2]

      Your accuracy standard

      The threshold you already hold your team to. We measure against yours, not ours.
    • [3]

      A domain expert's time

      a few hours a week for the first month, to walk us through the manual workflow and settle edge cases. The first couple of weeks go to understanding how the work is done today, before we write model code.

Let's collaborate

Tell us a bit about your project or challenge, and we'll get back to you shortly.

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