Find out if it can be built — before you bet on it.
A time-boxed experiment that tests your riskiest assumption against measurable criteria — so you get a clear build, pivot, or stop decision before committing budget.
A testable hypothesis
We pin down the single technical question worth answering and write it as a measurable, pass-or-fail hypothesis — before a line of code is written.
Deliberately narrow scope
We build only the minimum needed to test the core assumption — no polish, no extra features, no over-engineering the experiment to look like a product.
Measurable success criteria
Concrete thresholds agreed up front — response time, accuracy, throughput, cost — so "did it work?" is settled by data, never by opinion.
Resources mapped early
A clear picture of the technologies, data, and skills a full build would need — surfaced while it is still cheap to change course.
Documented results & findings
Evidence from the experiment: what performed, what broke, what surprised us, and the technical hurdles a production build would have to clear.
An actionable recommendation
A direct call — greenlight, pivot, or stop — with the reasoning and the data behind it, so your next decision is grounded rather than guessed.
Reaching a verdict
One question in. A clear verdict out.
A proof of concept narrows a fuzzy bet down to a single measured answer — and the answer is allowed to be no. That honesty is the whole point.
The riskiest assumption
The one thing that, if it fails, sinks the project.
A minimal experiment
Only enough is built to test that one thing — nothing more.
Measured against success criteria
Pass/fail thresholds agreed up front — settled by data, not opinion.
What you receive
You receive a validated approach, a mapped tech stack, and a full-build effort estimate — so development starts with the hard questions already answered.
Fixed scope · success criteria agreed up front · the verdict is yours to act on
De-risk before you spend
Test the assumption that could sink the whole project for a fraction of a full build, while the budget at stake is still small.
Decide on evidence, not optimism
Replace "we think it will work" with test data your whole team — and your board — can stand behind when the budget conversation happens.
A "no" is a win too
An honest stop recommendation saves you months of building the wrong thing. Telling you when not to build is the entire point of a proof of concept.
A clean line to the real build
If the verdict is go, you walk into full development with a validated approach, a mapped tech stack, and the hard questions already answered.
Key Capabilities
- Riskiest-assumption hypothesis design
- Minimal end-to-end vertical slice (not a full application)
- Measurable success-criteria definition (latency, accuracy, throughput, cost)
- Technical feasibility & performance benchmarking
- AI/ML model and prompt feasibility spikes
- Third-party API and data-source feasibility checks
- Architecture and scalability assessment
- Findings report with a go / pivot / stop recommendation
- Effort, cost, and resource estimate for the full build
- Throwaway-by-design prototype code, documented and handed over
Technologies
Engagement Models
Feasibility Spike
- One sharply defined hypothesis
- A minimal experiment to test it
- Pass / fail against agreed criteria
- A short findings summary with a recommendation
Proof of Concept
- Hypothesis and measurable success-criteria workshop
- A minimal end-to-end vertical slice
- Performance and feasibility benchmarking
- Documented results and findings
- A clear go / pivot / stop recommendation
- Effort and cost estimate for the full build
Prototype & Roadmap
- Everything in Proof of Concept
- A more complete, demonstrable prototype
- A production architecture recommendation
- A phased build roadmap and risk register
- A stakeholder demo and decision workshop
Frequently Asked Questions
What's the difference between a proof of concept and an MVP?
A proof of concept answers one question — "can this actually be built and work?" — with the minimum experiment needed to find out. It is disposable by design. An MVP is the first real, shippable version of a product that users can pay for. You do a PoC to decide whether to build; you do an MVP to start building. Doing them in the wrong order is how budgets get burned on ideas that were never feasible.
How long does a proof of concept take, and how do you keep it from sprawling?
Most proofs of concept run two to four weeks. We keep them bounded by agreeing the single hypothesis and the pass/fail success criteria before we start — and by building only what is needed to test that hypothesis. When something tempting but out of scope appears, it goes on the recommendations list for the full build, not into the experiment. Fixed scope and a defined end date are the whole discipline of a PoC.
What do I actually receive at the end?
You receive a documented findings report: the hypothesis, the success criteria, the measured results, the technical hurdles we hit, and a direct recommendation — go, pivot, or stop — with the reasoning behind it. You also receive an effort and cost estimate for the full build and the prototype code itself. The code is a feasibility experiment, documented and handed over, not a production system.
Will you tell us honestly if the idea is not feasible?
Yes — that is the value you are paying for. A proof of concept that always concludes "build it" is worthless. If the evidence says the assumption does not hold, we recommend stopping or pivoting, and we show you the data behind that call. An honest no, delivered in week three, is far cheaper than discovering the same thing after a six-month build.
Can the proof-of-concept code be used in production?
No, and we are deliberate about that. PoC code is built to answer a feasibility question quickly, not to meet the security, scalability, and maintainability standards a production system needs. Hardening throwaway code into production is a common and expensive mistake. If the verdict is go, we use what we learned to build the real thing properly — with the validated approach carried forward, but the code rebuilt to production standards.
Do we own the code and findings?
Yes. The findings report, the success-criteria definitions, the benchmark data, and the prototype code are all yours. You can take the recommendation to another team, build it in-house, or continue with us. There is no lock-in and no proprietary tooling you depend on to read your own results.
Have a high-risk technical bet? Prove it before you build it.
Book a 30-minute call. We will pin down the riskiest assumption, define what "success" measurably looks like, and scope a proof of concept that answers it.