APT — Product Backlog

Show employers mandate performance: avg APT Total of candidates reviewed, how their Role Context Profile compares to mandates with long-tenure placements, time to shortlist. Justifies the fee with data.

Quarterly external report: aggregate, anonymised data on cognitive profiles, placement success rates, industry trends. No candidate PII. Thought leadership, SEO, inbound leads, academic credibility. Supports Assistant Professor positioning.

Let candidates describe their ideal role context — complexity, ambiguity, autonomy — and use as two-way matching signal alongside their cognitive scores. Better matching, candidate ownership of process, richer data.

Employer buys block of 20 assessments for existing team. APT delivers Transformation Readiness Report — aggregate cognitive profile, gaps, recommendations. New B2B SaaS revenue stream separate from placement fees.

License CognitiveEdge as an API to other boutique executive search firms. Pay-per-assessment. APT handles scoring, they own the client relationship. APT becomes the cognitive assessment layer for executive search.

Aggregate all mandates and placements for a company. Build Transformation Readiness Score — how well-matched is the leadership team to their stated transformation agenda. Opens retainer-based advisory relationships with PE firms.

Show candidates an anonymised fit index: how their profile aligns with active mandate types (not specific mandates). Increases portal engagement and gives candidates a reason to return.

Translate Likert items into German and French. Block 4 (Reasoning) is language-independent. Expands addressable network for PE Mittelstand mandates requiring German-speaking candidates.

Push mandate status updates, new candidate completions, and employer decisions to a dedicated Slack channel. When 10+ active mandates are running, email notifications become noise.

Let candidates invite peers to take the assessment. Track referral chains. Add a Network Contributor badge. Senior transformation professionals have tight networks — one strong placement should generate 3–5 referrals.

After 20+ placements with outcome data, train regression model: given candidate cognitive profile + role context profile, predict 12-month retention probability. Show as % on shortlist. Core differentiator at scale.

Allow candidates to retake after 12 months. Show delta view: score changes and what they mean for context fit. Creates longitudinal dataset correlating score shifts with placement outcomes — no competitor has this.

After placement confirmed, auto-send 3-question surveys to candidate and employer at 30, 60, 90 days. Store in placement_feedback. This IS the retention data flywheel — every placement without outcome data is a wasted data point.