Everyone shipped the demo. 1 in 20 reached production.
Pirxey takes one customer-facing AI capability from demo to production standard: requirements, engineering, evaluations, QA and handover. You keep product strategy, architecture governance and the final call on release.
What the data says
Only 1 in 20 custom enterprise AI tools reaches production. MIT NANDA, 2025.
AI assistants raise defect risk by 30% inside unhealthy code. CodeScene, peer-reviewed, 2026.
AI-generated pull requests carry 1.7x more issues than human-written ones. CodeRabbit, 470 PRs.
Changes touching code older than a year fell 74% since 2023. GitClear, 623 million changes.
Four places customer-facing AI stalls
A model that behaves in a demo behaves differently against real customer data: empty fields, the wrong format, a provider timing out at 2am. Until validation, failure handling and logging are designed in, every unusual input becomes a support ticket, answered by the people who built the feature.
We take the capability through that production layer: schema validation, retries and fallbacks, logging, and integration tests that run before release, not after a customer finds the bug. That is how we built the LLM extraction workflow for Cambrian's CHF monitoring product.
Four steps from demo to production standard
Step 1: Assessment
Outcome, architecture and unknowns mapped before anyone estimates. Paid, time-boxed and deliberately narrow. It ends in a decision: build, narrow, work around, or don't build.
Step 2: Production pilot
One capability wired into your data, your permissions and your release process, with evaluations, fallbacks and human review designed in rather than bolted on afterwards.
Step 3: Benchmark
Measured against what we agreed in step one and observable after release: quality, adoption, and the senior time it cost you.
Step 4: Scale or handover
Apply the pattern to the next capability, or hand it to a named internal owner who can maintain it without us.
What Pirxey runs. What you keep.
What Pirxey runs
The outcome of one bounded workstream
Requirements, engineering and integrations
Evaluations, QA and release readiness
Knowledge transfer and handover
You keep
Product strategy and prioritization
Architecture governance and the repository
Data governance
Final release authority
The reasonable objections
- Correct, not immediately. That is why the first step is an assessment rather than a scope commitment, with unknowns mapped alongside your internal experts before anything is estimated.
We have done this inside mature product organisations before.
Client names belong in a conversation, not on a landing page. Ask on the call and you get the names, the timelines, and the parts that did not go to plan.
120+
Engineers
100+
Projects delivered
2018
Founded
4.9
Clutch rating
Łukasz Graliński
Pirxey co-founder
“AI accelerates development. It does not replace judgement, architecture or responsibility. We hire selectively, work with senior engineers only, and treat AI as a tool rather than a shortcut. Our team is fully Poland-based, recruited from the country's top universities of technology, and I stay close to how we build those teams and how we deliver.”
Senior, Poland-based teams. The same delivery model, before AI changed what sits inside it.
Questions we get before the first call
- Turning a customer-facing AI feature into a part of the product people can rely on: defined behaviour, validated outputs, failure handling, tests, release ownership, and someone accountable after launch.
Start with one capability, not a transformation program
A workstream assessment is paid, time-boxed and deliberately narrow. You get the unknowns written down instead of priced in, a phased plan with an estimate band, and a straight recommendation: build, narrow, work around, or don't build.
Book a 30-minute Workstream Assessment call
Mike KacprowiczBook a callChief Growth Officer