Demos and benchmarks rarely match real production data. A PoC tests on your actual files – drawings, PDFs, contracts, whatever you handle daily.
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.
Custom workflows mean custom risk. Better to discover the edge cases in 8 weeks than 8 months into an MVP.
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”.
Most AI projects fail on data quality, not algorithms. A PoC surfaces this in weeks, not after a six-figure spend.
The first design choices – data pipeline, model type, integration approach – determine cost and speed at scale. Hard to fix later. Cheap to fix now.
Most demos run on cherry-picked data. A PoC on your files shows you what’s actually possible vs. what’s marketing.
Some AI approaches look cheap until you scale. A PoC gives a real cost model – inference, storage, retraining – before you commit.
Without a PoC, MVP estimates are guesses. With one, you have measured accuracy, real processing times, and a defensible budget.
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
A step by step process led by senior AI engineers and architects.
From understanding your system to a ready to use action plan.
Discovery call
Free tech review
PoC Build
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*
Ideal for:
Team:
Deliverables:
A working prototype on your data, validated against your real workflow. You get accuracy numbers, architecture, and a budget for what comes next.
Ideal for:
Team:
Deliverables:
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.
An AI prototype that read real architectural drawings and detected 14 cabinet types at 90% accuracy. Result greenlit the production build Stevens runs today.
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.
A slice of real data
Your accuracy standard
A domain expert's time
Tell us a bit about your project or challenge, and we'll get back to you shortly.
Reach out directly
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