98% of your top performers would never have been interviewed.

Problem: 1,000+ applicants per role, screened by keyword filters that reject 80 percent of future top performers.

Solution: The brain reads every applicant against 28 behavioral dimensions calibrated on your own production data. A 0-100 Fit Score writes back into your ATS as a native field, so your recruiters never leave the screen they already work in. We connect to Workday, SuccessFactors, Greenhouse, iCIMS, Avature, and Taleo through your existing auth, scoped to least privilege.

Proof: 80 percent of top performers at a Fortune 500 carrier were filtered out by a single "relevant industry experience" requirement. The Fit Score caught them.

Problem: Structured interviews take 45 minutes per candidate. Hiring managers skip them or conduct them inconsistently.

Solution: A 12-minute async interview surfaces the same signals, scored on the same structured rubric for every candidate. Candidates are told it is AI-assisted, every score carries a Decision Trace and is monitored for four-fifths adverse impact, and no candidate is advanced or rejected on the AI score alone. Full transcript and audit trail included.

Proof: Four of six top-performer signals detected in a candidate the ATS would have rejected. That candidate was promoted at month 11.

Problem: Sourcing teams search LinkedIn with the same keywords that fail in the ATS. The passive talent pool looks identical to the active one.

Solution: The brain identifies passive candidates across LinkedIn, GitHub, and public portfolios using patterns learned from your own validated top performers.

Proof: Sourced candidates matched the top-performer pattern at 2.1x the rate of inbound applicants at pilot volume.

Problem: New hires take 6-9 months to reach full productivity. Managers rely on gut feel to decide who needs coaching.

Solution: The brain reads onboarding signals (training completion, early production, manager check-ins) and flags exactly where each new hire is diverging from the top-performer ramp curve.

Proof: Role-calibrated ramp signals identified at-risk hires 6 weeks earlier than manager escalation at pilot scale.

Problem: A single mis-hire runs one to two times annual salary in fully loaded cost. By the time HR sees an exit interview, the decision was made months ago.

Solution: The brain reads production trends, engagement signals, and cohort patterns to surface retention risk before the employee starts job-searching.

Proof: Cohort-level attrition forecast within 3 percentage points of actual over an 18-month observation window (n=10,765). The flag comes early enough to fix the cause while you still can.

Problem: High performers leave because they do not see a path. Succession plans live in spreadsheets updated once a year.

Solution: The brain scores every employee against every open or projected role using the same Fit Score. Cross-role pattern matching replaces manager nominations.

Proof: Internal mobility candidates matched the top-performer pattern for the target role at rates comparable to external hires scored by the same model.

Problem: Your HR team answers the same fourteen questions every week. Each answer takes 8-12 minutes and a policy lookup.

Solution: The brain reads every inbound ticket, drafts a policy-grounded response, and routes edge cases to the right specialist. The human approves; the system learns.

Proof: Ticket resolution time reduced by 60 percent in pilot, on a queue where each answer took 8 to 12 minutes and a policy lookup, with zero policy-violation escalations on auto-drafted responses.

Problem: Audit prep takes weeks of pulling records across five systems. Evidence gaps surface during the audit, not before.

Solution: Decision Traces provide a continuous, queryable audit trail. Every hiring decision, every score, every override is linked to its evidence chain and signed.

Proof: Every score on this page resolves to a signed, immutable Decision Trace: model version, evidence chain, and approver on record. An audit query runs in minutes instead of a multi-week records pull.

Problem: Compensation bands are set by title and tenure. Actual production varies 5x within the same band.

Solution: The brain shows where pay has drifted away from contribution and surfaces pay-equity gaps, so comp conversations start from evidence instead of title and tenure alone. Your team owns every pay decision.

Proof: 5.37x production gap between top-scored and bottom-scored deciles within the same role and location (n=10,765).

Problem: The best managers consistently develop top performers. Others struggle to, and the difference is rarely visible early enough to coach it.

Solution: The brain traces team-level production, ramp curves, and retention rates back to coachable patterns. The result is a private coaching signal, surfaced only to the manager and their HRBP. We never publish a ranking.

Proof: Manager-level production traces revealed a 2.1x output variance attributable to management patterns, controlling for hire quality.

Problem: New hires are placed by zip code or manager preference. The wrong territory can turn a top performer into an early attrition.

Solution: The brain matches candidate patterns to geo-calibrated production data, recommending the placement most likely to produce sustained output.

Proof: Geo-calibrated placement recommendations correlated with $54/day production lift over cohort average at the pilot carrier.

Problem: Headcount planning runs on last year's numbers plus a growth target. Nobody models what the incoming cohort will actually produce.

Solution: The brain projects cohort-level output by combining pipeline quality scores, historical ramp curves, and attrition forecasts into a single planning model.

Proof: Cohort output forecast within 8 percent of actual over a 12-month window at 2,000-hire volume.

Problem: Deals slip a week at a time. By the time the forecast misses, the quarter is over.

Solution: The brain reads CRM activity, email cadence, and meeting patterns to score deal health in real time. Slipping deals surface before the rep flags them.

Proof: Deal-health scoring identified at-risk opportunities 3 weeks earlier than rep self-reporting in pilot CRM integration.

Problem: Customer churn is measured after it happens. Renewal conversations start too late and rely on relationship intuition.

Solution: The brain reads usage patterns, support ticket sentiment, and engagement signals to score renewal risk months before the contract date.

Proof: Renewal risk scores flagged 70 percent of eventual churns at least 90 days before contract expiration in pilot data.

Problem: Referral programs pay bounties but do not target. The best referrers are not identified; the best candidates are not described.

Solution: The brain identifies which employees are most likely to refer a top performer, based on network overlap with the validated top-performer pattern.

Proof: Referred candidates from targeted referrers matched the top-performer pattern at 1.8x the rate of untargeted referral programs.

Problem: Career development conversations happen once a year. Employees leave because they cannot see what is next.

Solution: The brain maps each employee's current signal profile against every role in the organization, surfacing the highest-fit next moves and the gaps to close.

Proof: Next-role fit scores correlated with actual promotion outcomes at r=0.34 (p<0.001) across the observation cohort.