Prove the AI idea works before you fund the full build
An AI proof of concept is a short, structured test of whether a specific idea — automating quoting, extracting documents, answering from your knowledge base — works on your data, at your accuracy bar, at a cost that makes sense. It removes the most expensive failure mode in AI adoption: funding a six-month build on the strength of a demo that never met your real inputs.
Willowark timeboxes these engagements and defines success before writing code: which task, which data, what accuracy threshold, what cost per unit makes the economics work. Then we build the thinnest end-to-end slice that can be honestly measured — real documents, real tickets, real calls — and score it. The deliverable is evidence, not enthusiasm.
AI & Intelligent AutomationHow the work gets done
The same way every time: scope, build, hand over.
A typical proof of concept runs a few weeks: assemble a representative sample of your data, build the pipeline or agent slice, and evaluate against a labeled test set held out from development. We measure accuracy where it matters — per field, per case type — alongside latency and per-unit model cost, and we document failure modes specifically, because 'it struggles with handwritten change orders' is a finding you can plan around.
The engagement ends in a decision, and both outcomes are wins. If the numbers clear the bar, you get a working prototype, an eval harness that carries into production, and a scoped roadmap with realistic costs. If they don't, you get a clear account of why, what would have to change, and the money you did not spend on a doomed build.
Scoping a proof of concept is mostly about choosing what to leave out. We pick one task, one data source, and one accuracy measure, and we resist adding a second use case even when it seems close, because the point is a clean answer rather than a broad one. The main trade-off is realism against speed. A prototype wired into your live systems is more convincing but slower to build; one running on exported data proves the model can do the job without proving the integration can. We usually choose the exported-data path for the model question and reserve integration for the production build, unless integration is the risk.
The way proofs of concept usually go wrong is by being graded on the data they were built with. A pipeline tuned on the same documents it is scored against will look better than it is, which is why we hold out a test set and only score against it once. The other failure is a demo that works on a laptop and nobody can run afterward. Everything we build runs from a repository with a documented setup, the eval harness is a script your team can rerun, and the findings are written for the person who has to decide, not for the people who did the work.
Scope it in writing
What we agree before work starts
- Written success criteria agreed before the build starts
- Working end-to-end prototype running on your real data
Build with checkpoints
Working results, not slide decks
- Evaluation report with accuracy, latency, and cost-per-unit numbers
- Go or no-go recommendation with a scoped production roadmap
Hand over something you own
Documentation, source, and training
- Labeled test set, held out from development, with the scoring script that produced the numbers
- Repository with documented setup so your team can rerun the prototype and the evaluation
Sound familiar?
Where ai prototyping & proof of concept earns its keep.
A manufacturer testing whether AI can quote from their drawing packages before reorganizing the estimating team
An ops leader with three competing AI ideas and budget for one
A team burned by a vendor demo that collapsed on real documents
A founder who needs a working AI prototype in front of investors or a pilot customer
Ask about AI Prototyping & Proof of Concept
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Related work
AI operating software for training operations
Components:
- Schedulers (training ops): The people running the training operation.
- Operating software (AI assistance): The operating software, with AI assistance built in.
- Cloud services (distributed): Distributed cloud services behind the application.
- Training game (learning): The training game the software connects to.
Connections:
- Schedulers to Operating software
- Operating software to Cloud services
- Operating software to Training game
A global automotive manufacturer's training operation · Automotive
AI operating software for training operations
AI operating software for the manufacturer's training schedulers — removing manual scheduling labor and logistics tracking, saving hundreds of hours per year — plus a training video game built to help trainers perform better. Used across the manufacturer's training organization.
Read the case study →Common questions
Asked before every ai prototyping & proof of concept project.
How long does a proof of concept take?
Most run two to six weeks, depending on scope and data preparation. The timebox is deliberate: a proof of concept that drags past that is usually a production build wearing the wrong name, and we would rather scope it honestly.
What if the result is that it doesn't work?
Then the engagement did its job. You will know precisely why — data quality, accuracy ceiling, economics — and what would need to change for the answer to flip, which sometimes becomes a cheaper fix worth doing anyway. A clear no after a few weeks beats discovering it eighteen months into a build.
Do we own what gets built?
Yes. The prototype code, the evaluation harness, the labeled test set, and the findings are yours, whether or not you continue with Willowark. The eval suite in particular keeps paying off — it becomes the acceptance test for whoever builds the production system.
What do you need from us to run a proof of concept?
A representative sample of the real data, a person who knows the task well enough to label a test set and judge outputs, and a decision-maker who agrees to the success criteria before we start. Data preparation is usually the longest lead item, so getting the export moving early matters more than anything else. We handle the rest, and we keep the time we ask of your people to a few short sessions.
Can the prototype go straight into production if it works?
Some of it. The eval harness, the test set, the schema, and the prompt work carry forward intact, and they are typically the most valuable parts. The pipeline code itself is built for speed of learning rather than reliability, so production usually means rebuilding it with queues, retries, monitoring, and proper integration into your systems. We are explicit about which parts are throwaway when we scope the production roadmap.
Where this sits
AI Prototyping & Proof of Concept, inside a ai & intelligent automation system.
The lit component is the part of the system this service delivers; the rest is what it has to work with.
Hover or focus a component to see what it is and what it talks to. Arrow keys move between them.
Inbound documents and messages are ingested, an agent reasons with company knowledge and acts through the systems of record, and a person reviews the cases that need judgment.
Components:
- Inbound (email, PDFs, forms): The unstructured work arriving every day.
- Ingestion (extract, classify): Turns documents into structured fields with confidence scores.
- Agent (reasons, uses tools): A model with tools: it looks things up, decides, and acts — within limits you set.
- Knowledge (your docs): Company procedures and history, retrieved on demand.
- Systems of record (ERP, CRM): Where the work actually lands.
- Reviewer (exceptions): The person who sees what the agent was unsure about.
Connections:
- Inbound to Ingestion over email
- Ingestion to Agent over events
- Agent to Knowledge over REST, both directions
- Agent to Systems of record over REST
- Agent to Reviewer over handoff
Strategy. Software. Systems.
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