/
PH1 core capbility
YEARS EXPERIENCE
3 to 5 years
TYPICAL CLIENT
VP Product, Head of UX Research, Director of AI
NECESSARY TIMELINE
2 to 3 months
BUDGET NECESSARY
Up to $50,000
Our POV
Silent failure is the defining AI product problem of 2026. Traditional UX evaluation was designed for deterministic software. AI is not deterministic. The same prompt returns different answers. The system sounds confident when it is wrong. It works for 70% of users and silently fails the other 30% — and your A/B test averages it all out into a number that tells you nothing useful.
PH1 built the evaluation methodology the market is finally realizing it needs. We specialize in multi-turn interaction testing — the complex, sequential workflows where AI products most commonly fail and where standard UX evaluation is most blind. A single-turn test can tell you if a feature works once. Multi-turn evaluation reveals whether it works across an entire workflow, whether trust compounds or degrades across interactions, and whether the AI earns the sustained confidence that predicts customer success and account expansion.
What We Do
Every evaluation is oriented toward calibrating your AI product for sustained customer success — not just identifying bugs. We score every AI feature in scope against the full AI Product Calibration framework:
Power — does it do what it claims, correctly and consistently across multi-turn interactions?
Speed — does it reduce genuine effort across the full workflow, not just the first interaction?
Impact — does it change behavior in ways that drive the business metrics you actually care about (retention, expansion, NPS)?
Joy — does it earn the trust that makes customers return, recommend, and defend the product?
We use multi-turn workflow testing, inconsistency analysis, silent failure detection, and customer success impact assessment to produce a scored evaluation, a prioritized failure list by severity and segment, and specific improvement recommendations with expected impact.
What We'll Deliver
Full AI Product Calibration scored assessment across Power, Speed, Impact, and Joy for every feature in scope
Multi-turn interaction analysis: where trust and performance compound versus degrade across sequential use
Silent failure documentation: behaviors that look acceptable on dashboards but are not delivering customer value
Inconsistency analysis: where the AI produces different outputs for the same inputs, and the trust implications
Customer success impact assessment: how evaluation findings connect to retention, expansion, and account scaling
Prioritized improvement recommendations with rationale and expected impact
Reusable evaluation framework your team can run internally on future releases
When This is Essential
Your AI product is live and something feels off, but standard metrics cannot pinpoint the problem
Churn or retention signals are diverging from activity dashboards
Customer Success is escalating trust issues your product analytics cannot see
Before scaling an AI product into a new segment or larger customer base
Before a major release that depends on the AI feature performing consistently
Frequently Asked Questions
How is this different from A/B testing?
A/B testing was designed for deterministic software where the same input always produces the same output. AI products are probabilistic — the same prompt returns different answers, and A/B tests average out the failures that matter most. AI Product Evaluation uses multi-turn workflow testing and Calibration scoring to find what A/B testing cannot see.
Can you evaluate multi-turn conversations specifically?
Yes. Multi-turn interaction testing is PH1's specialty. We test how trust, consistency, and performance compound across sequential interactions — where most AI products actually fail.
Do you work with our data and analytics team?
Yes. PH1 combines behavioral research (what users actually do) with your analytics (what the dashboards say). The contrast between the two is often where the most valuable findings live.
Can our team use your evaluation framework after the engagement?
Yes. Every engagement includes a reusable evaluation framework and scorecard template your team can run internally on future releases. We leave your team more capable, not dependent on PH1.
How fast can you turn around findings?
3–6 weeks depending on scope. Preliminary findings are often available in week 2 so your team can start acting on high-priority issues before the final report lands.
Combine With These Services
AI Launch & Value Acceleration — Continuous Calibration scoring during the critical 8–12 weeks after launch
Rapid Prototyping Sprint — Use evaluation findings to prototype and test specific improvements
LLM Product Strategy & Specification — Re-specify the model when evaluation reveals fundamental gaps
/
Submissions