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 to the ATS as a native field.

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 against production outcomes instead of interviewer gut feel. 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, not inferred boilerplate.

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: Turnover costs 50-200 percent of annual salary. 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).

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, 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: Legal approval in 17 days at a carrier where six prior AI vendors were rejected over eighteen months.

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

Solution: The brain maps pay to production at the individual level, surfaces pay-equity gaps, and recommends adjustments grounded in output data rather than market surveys.

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

Problem: Some managers consistently develop top performers. Others consistently lose them. Nobody measures why.

Solution: The brain traces team-level production, ramp curves, and retention rates back to manager actions. The result is a coaching signal, not a leaderboard.

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.