PIE Intelligence: know which candidates will still be here in two years
Every ATS tells you who's qualified. PIE Intelligence tells you who will perform, integrate with your team, and stay, before you make the hire. The only predictive intelligence layer built into a startup ATS.
Performance · Integration · Engagement
Available on Scale plan · 500+ hiring teams · Predicts retention at 12 and 24 months
A wrong hire doesn't just leave, it costs you before it goes
Most applicant tracking systems solve the top-of-funnel problem. They help you collect applications, score resumes, and move candidates through stages. But they stop the moment someone accepts an offer. That gap is where bad hires happen, and where the real cost begins.
The root cause is almost always the same: the hiring decision was made on qualifications alone. The resume showed the right experience. The interview went well. But no one asked, and no tool helped answer, the harder questions: Will this person actually perform in this specific role, with this specific team? Are their working style and values aligned with how this company operates? Are there signals in their history that predict early attrition? Traditional talent acquisition software can't answer those questions. PIE Intelligence can.
What PIE Intelligence actually measures
PIE combines a short candidate assessment with the structured data already in your hiring process, JD, resume, and fit score, to produce signals no standard ATS captures.
How likely is this candidate to deliver strong on-the-job output in this specific role? PIE analyses role-specific competency signals in the candidate's career history, not just job title or tenure. A five-year track record in a similar scope of work at a comparable stage of company signals something very different from five years of adjacent experience. Performance prediction is the most role-specific of the three dimensions, recalibrating for every job type and seniority level.
How well will this person work within the team they're joining? Integration is built from communication style indicators, collaboration signals, and alignment with the role's working model, async or synchronous, independent or highly collaborative, structured or fluid. This is what most teams call 'cultural fit', but PIE makes it measurable rather than instinctive. The Integration score is what surfaces in the Team Fit Report, mapped against your existing team's composition.
What is the probability this person is still engaged and performing at 12 months? At 24 months? Engagement is built from career trajectory patterns, role-fit depth, and historical signals that correlate with early attrition, including pace of progression, depth of tenure at comparable roles, and alignment between the candidate's stated career direction and what this role actually offers. This is the retention signal every hiring manager wants and almost no recruiting software for HR provides.
Together, these three dimensions produce a single PIE Score (0–100) per candidate, a composite view of hire quality that sits alongside the AI Fit Score in your candidate pipeline. A high fit score and a low PIE score is a tension worth investigating before you extend an offer.
Five intelligence outputs from every PIE analysis
PIE doesn't just produce a score. It produces a full intelligence picture, for the candidate, for the team, and for the pipeline.
PIE Score (0–100, per candidate)
A composite score across all three dimensions, weighted to your role type and seniority level. This is where the critical tension lives: a candidate with a 94 fit score and a 61 PIE score is qualified but high-risk. A candidate with an 81 fit score and a 90 PIE score is your actual best hire. The PIE Score appears directly in the candidate pipeline alongside the AI fit score, so every decision-maker sees both signals at a glance.
Retention Forecast
12-month and 24-month retention probability, expressed as a percentage. "78% likely to be retained at 12 months" gives hiring managers a tangible risk signal they can weigh against the candidate's other strengths. This is not a verdict, it's a data point that changes the conversation. A 62% 12-month forecast doesn't mean don't hire. It means have a specific conversation about role scope, growth path, and compensation alignment before you extend an offer.
Bad Hire Risk Flag
When PIE detects a combination of signals that historically correlate with bad hires for this role type, misaligned seniority expectations, short tenure patterns, role-type mismatch, collaboration demand gap, it surfaces a risk flag with the specific reason. Not a disqualification. A conversation starter. The flag identifies the exact signal driving the risk, so the interview conversation can address it directly rather than discovering the issue six months post-hire.
Team Fit Report
PIE maps each candidate's Integration profile against your existing team's composition. If your engineering team is 80% structured independent workers and your candidate's profile suggests high collaborative dependency, PIE surfaces that tension before onboarding reveals it. The Team Fit Report is designed to be shared with the hiring manager, one-click PDF export, as a structured input into the final hiring conversation. It makes the 'cultural fit' discussion specific instead of instinctive.
Pipeline Intelligence Reports
This is where PIE becomes a recruitment management system learning loop rather than a per-candidate tool. Across all your open roles, PIE surfaces macro patterns monthly: which stages have the highest drop-off for high-PIE candidates? Which job boards are sending you high-fit-score but low-PIE applicants? Where are your compensation structures creating misalignment with retention-ready talent? These are the insights that make your next hire, and the one after, systematically smarter. Available on the Scale plan, generated automatically every month.
How PIE Intelligence works inside Recrofy
Candidates complete a short PIE assessment as part of the application flow. Once submitted, PIE scores them automatically, no manual configuration required on your end.
PIE activates automatically on every applicant
When a candidate applies, they receive a short PIE assessment (under 10 minutes, role-specific) as part of the application flow. Once completed, PIE combines their assessment responses with the JD, resume, and AI fit score to build their full intelligence profile. No manual configuration required for recruiters.
PIE Score and signals appear alongside the fit score
In your candidate pipeline, each applicant shows both their AI Fit Score (0–100, skills and experience match against the JD) and their PIE Score (0–100, performance, integration, and engagement prediction). Both scores update as more data enters the pipeline. High-PIE candidates are visually surfaced so hiring managers can prioritise conversations with the right people.
Retention Forecast and Risk Flags surface at offer stage
Before you extend an offer, Recrofy surfaces the full PIE report: retention forecast at 12 and 24 months, bad hire risk flags with specific reasons, and the Team Fit mapping. This report is designed to be shared with the hiring manager, one-click export as a PDF. It gives every offer conversation a structured, data-informed starting point.
Pipeline Intelligence Reports generate monthly
On the Scale plan, Recrofy generates monthly PIE Intelligence Reports across all your roles, surfacing systemic patterns, sourcing quality by channel, and retention signal trends across your entire candidate pool. Over time, these reports become the most valuable output PIE produces: a learning loop that makes each hiring cycle smarter than the last.
Why fit scores alone aren't enough, and what PIE adds
Standard AI resume scoring answers one question: is this person qualified for this role? That's necessary. It's not sufficient. This is what PIE adds on top of every AI fit score.
| What AI fit scores tell you | What PIE Intelligence adds |
|---|---|
| Skills match against JD | Likely performance in this specific role context |
| Years of experience | Retention risk based on career trajectory patterns |
| Education and credentials | Cultural integration signals from working style indicators |
| Keyword coverage | 12 and 24-month engagement forecast |
| Who passed the bar | Who will still be here, and thriving, in 2 years |
The best hires aren't always the most qualified candidates. They're the candidates whose full profile most closely matches what success looks like in this role, at this company, at this stage.
PIE Intelligence is built for hiring decisions that matter most
Series B+ companies scaling fast
When you're going from 30 to 150 people in 18 months, a single bad hire at the VP or Director level costs you more than the hire itself, in salary, in management time, in team morale, and in the months you lose before you can rehire. PIE's retention forecast and Team Fit Report are especially high-value for senior and leadership hiring, where the cost of a wrong decision is highest and the signals are hardest to read from a resume alone.
Engineering and technical teams
Technical roles have the highest bad-hire cost and the most specific performance signals. PIE's Performance dimension is particularly well-calibrated for engineering, data, and product roles, where the difference between a strong hire and a poor one shows up in output quality, not just presence. The Integration score is also critical for technical teams, where working style fit (async vs sync, independent vs pair) has a disproportionate impact on team velocity.
HR leaders building repeatable hiring systems
Pipeline Intelligence Reports give HR leaders the macro view that no standard hr and recruitment software provides: where is your hiring process leaking good talent? Which channels consistently send you high-retention candidates? Which roles are consistently attracting mismatched applicants? PIE turns every hiring cycle into a learning loop, so the quality of your next hire is structurally higher than the last, without requiring manual post-mortem analysis.
What teams using PIE Intelligence report
"The PIE score changed how we think about hiring. We had a candidate who scored 91 on fit and 58 on PIE. We dug into the risk flag, misaligned seniority expectations, had a specific conversation about it in the final round, and ended up not making the hire. Three months later, a nearly identical profile in another candidate left a competitor after 4 months. PIE saved us that mistake."
What PIE Intelligence doesn't do, and why that matters
We built PIE to be useful, not omniscient. A few things worth being explicit about, because honesty about limitations builds credibility for the claims we do make.
PIE does not score on demographic signals
Gender, ethnicity, age, nationality, and any protected characteristics are not inputs into any PIE dimension. PIE reads career history patterns, role-specific competency signals, and working style indicators, not identity.
PIE does not make hiring decisions
PIE surfaces risk signals and forecasts. Every hire is a human decision. PIE is designed to inform and structure the conversation, not replace the judgement of the people closest to the role.
PIE is probabilistic, not deterministic
A low retention forecast is a flag, not a disqualification. A high bad-hire risk score is a conversation starter. Context matters. The interview prompted by a risk flag is the point, not the score itself.
PIE improves with your data
The more hiring history Recrofy has from your organisation, the more calibrated PIE becomes to your specific culture and role patterns. PIE is most powerful after its first six months of observing your pipeline.
Frequently asked questions about PIE Intelligence
What does PIE stand for in Recrofy?
PIE stands for Performance, Integration, and Engagement, the three dimensions Recrofy uses to predict how a candidate will perform on the job, integrate with the team, and remain engaged over 12 and 24 months. PIE goes beyond resume fit scoring to surface long-term hire quality signals.
How is PIE Intelligence different from a standard AI fit score?
The AI fit score (0–100) measures how well a candidate's qualifications match the job description, skills, experience, credentials. PIE measures predicted performance in the role, cultural integration likelihood, and 12/24-month retention probability. A candidate can score high on fit and high on PIE risk, that tension is what PIE is designed to surface.
Does PIE Intelligence require candidates to complete an assessment?
Yes. Candidates complete a short PIE assessment as part of the application flow, typically under 10 minutes. It is role-specific and structured to surface Performance, Integration, and Engagement signals. Results are combined with the resume and JD to produce the full PIE Score and retention forecast.
What is a bad hire risk flag?
A bad hire risk flag is surfaced when PIE detects signals that historically correlate with poor outcomes for a role type, seniority expectation misalignment, repeated short-tenure patterns, or a significant mismatch between the role's collaboration demands and the candidate's working style. It's a conversation starter, not a disqualification.
What is the Team Fit Report?
The Team Fit Report maps a candidate's Integration profile against your existing team's composition. If your team has a particular working style distribution, it shows how a new hire's profile aligns with or tensions against it, before onboarding surfaces the problem naturally.
Which Recrofy plan includes PIE Intelligence?
PIE Intelligence Reports are available on the Scale plan ($149/month, unlimited credits). The Scale plan also includes custom approval chains, RBAC, audit logs, and a dedicated Customer Success Manager.
Can PIE Intelligence help reduce employee turnover?
Yes, that's the core use case. By surfacing retention risk signals before a hire is made, PIE gives hiring teams the information to make better decisions and avoid hiring on qualifications alone, the most common driver of first-year attrition. Teams on the Scale plan report up to 40% reduction in first-year attrition.
Stop hiring for qualifications. Start hiring for outcomes.
Every hire is a prediction. PIE Intelligence makes that prediction explicit, and gives you the data to get it right.