Proof of Concept Development
You pull a proof before you commit the press.
A small, time-boxed build on your real data that answers one question honestly: is this worth doing for real? We set the bar before anything is built, measure against it, and hand you a clear press, amend, or kill.
Proof · pull 01
Job 4417
Support reply copilot
Pulled on 2,400 real tickets · 3 weeks · fixed scope
Sign-off
2–4 wks
Time-boxed
Your stock
Not a demo set
Set first
The bar
3 ways
It can end
The brief
Don't bet the project on a demo
AI demos are seductive and misleading. Polished on clean data, they promise things production quietly can't deliver, and the gap between the two only shows up after the budget is committed.
A proof flips it: a small, real build on your messy data, judged against a bar you set before you started. It costs a fraction of the full project and it's the only thing that tells you the truth about whether to commit.
A clear kill here saves the expensive one later.
What the pull is for
Proof before budget
A small, real build that answers whether this works for you, before you commit the press run. It costs a fraction of the full project and it's the only thing that tells you the truth about committing.
The bar is set before the pull
We agree the numbers that mean go before anything gets built, so the result is judged against your goal instead of moved afterward to fit whatever the build happened to produce.
It ends in a real decision
Press, amend, or kill, with the evidence behind it. No demo that dazzles in the room and disappoints in production three months later.
What a proof answers
Four questions, before you commit anything
Is the value real?
Whether the outcome actually shows up on your data and inside your workflow, instead of on a vendor's tidy demonstration set.
Is it feasible?
Whether your data, systems, and constraints can carry it in production. That's the part that quietly kills more projects than model quality ever does.
Where does it break?
What it struggles with, what breaks it, and what you'd have to handle before this could be trusted at scale.
Do the economics work?
A grounded read on the cost, the effort, and the return of building the real thing, estimated from something that exists instead of from a slide.
The schedule
Four weeks, and the first one has no code in it
Set the bar
We agree the specific, measurable criteria that would make this a go, and write them down. This is the week that decides whether the rest of it means anything.
Pull the proof
A focused build on your actual data: enough to test the hard part and nothing more. The goal is an answer, and the product comes later.
Read it and sign
We run it against real inputs, measure against the bar we set, and put a recommendation in front of you: press, amend, or kill.
The method
Set the bar, then clear it or don't
Define success
Together, up front. The specific bar that means this is worth building, agreed before anyone has an emotional stake in the answer.
Build a real slice
Tightly scoped to prove the core value on your real data. We build the risky part, never just the easy part that demos well.
Test honestly
Against real inputs, measured against the criteria we agreed, including the cases we already suspect it will struggle with.
Decide
Press, amend, or kill, with the numbers, the risks, and a costed path to production if it's a go.
Setting the bar
Most proofs fail because the bar was never real
A criterion you can't measure is a mood. When the bar is vague, the result gets argued instead of read, and the loudest opinion in the room decides a six-figure question. Here is the difference in practice.
≥ 85% draft accuracy across 2,400 real tickets
The drafts should be good
≥ 30% cut in median handle time against a control group
It should save the team time
≥ 7/10 agent trust, surveyed after two weeks of real use
The team should like it
Under 2s at p95 on our slowest ticket type
It should feel fast
What the proof changes
What four weeks buys you
A no-go stops being a failure
It's the product. A kill in week four costs a fraction of the full build that would have died in month nine, and you know exactly why it died.
The demo stops deciding
AI demos are seductive and misleading: polished on clean data, promising what production quietly can't deliver. A proof on your messy data is the only honest test.
The estimate gets real
Costing the full build from a slide is guesswork. Costing it from something that already runs on your data is arithmetic.
Nobody moves the goalposts
The bar was written down before the build existed, so the conversation at the end is about the evidence instead of what everyone meant in week one.
Selected work
Measured against a baseline, not a feeling
What comes back with the proof
Evidence, and a decision
A working proof, the measured results including the ones that missed, an honest recommendation, and, if it's a go, a costed path to the real thing.
- 01Agreed success criteria, written down before any build starts
- 02A working proof of concept running on your real data
- 03Measured results against those criteria, including the misses
- 04A clear press / amend / kill recommendation
- 05The risks and unknowns surfaced instead of smoothed over
- 06A costed path to production if it's a go
Proofs we have pulled
Every sector sets its bar somewhere different
Support & service teams
Where the bar is usually handle time and trust, and the hard part is proving the drafts survive contact with real tickets.
Finance & back office
Extraction and reconciliation, where accuracy has to clear a bar that a human reviewer would actually accept.
Retail & distribution
Forecasting proofs, where the honest test is measuring against the baseline the business already runs on.
Regulated industries
Where the proof has to answer whether it can be defended, as well as whether it works.
Two to four weeks
Find out on your data, never on somebody else's demo.
We'll agree the bar with you first, and a kill is a perfectly good place for this to end.
Choosing Flaidex for this
Four rules that make a proof worth pulling
The bar is agreed before we build
Together, and in writing. A criterion set after the result is known is just a description of whatever happened.
We pull it on your stock
A proof on a vendor's clean sample data proves nothing at all. We run on your real, messy data, because that's the only test whose answer transfers.
We build the risky part
The temptation is to build what demonstrates well. We build the bit most likely to fail, because that's the bit you're actually buying an answer about.
A kill is a good outcome
We say so before we start and we mean it afterward. If the proof fails, it did its job for a fraction of what the full build would have cost you.
Working with us
What the engagement itself is like
Two to four weeks
Scoped tightly to prove the core value instead of building a finished product, which is what keeps it fast and what keeps it honest.
Some of it is throwaway
Sometimes the foundation carries into production and sometimes it doesn't. We tell you which parts are scaffolding before you get attached to them.
You keep the answer either way
Press, amend, or kill, the evidence is yours. If it's a go, the path to production is costed from something real.
Questions
What people ask about a proof
Q1How is a POC different from just building it?
A POC is deliberately small and time-boxed to answer one question: does this work well enough to be worth building for real? It tests the risky part on your data, cheaply, before you commit to the full scope, so you never sink a big budget into an unproven idea.
Q2How long does a POC take?
Typically two to four weeks. It's scoped tightly to prove the core value instead of building the finished product, so it stays fast and focused.
Q3What if the POC fails?
Then it did its job, for a fraction of the cost of a full build that would have failed. You'll know exactly why and whether a pivot could work, and you'll have saved the much larger investment. A clear no-go is a good outcome.
Q4Do we set the success criteria or do you?
Together, up front. We agree the specific, measurable bar that means this is worth building before we start, so the result is judged against your goals instead of moved to fit the demo.
Q5Can the POC become the real thing?
Sometimes the foundation carries into production, but we build it to prove value first, and the final system comes after. If it's a go, you get a costed, honest path to the real build, with eyes open about what's throwaway.
Q6Is it built on our data?
Yes. A POC on a vendor's clean sample data proves nothing. We run it on your real, messy data because that's the only test that tells you the truth.
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