Tools · Publishing
Publishing
Go from a research dump to a titled, positioned, chapter-by-chapter content plan.
How it works
Three chained-but-independent stages (ideas → concept → outline), each callable alone; status ladder idea→concept→outline; audience/market metadata carried through.
You bring
{ source: deep_research|notes, text, sourceId? } per stage, or the full pipeline
You get
BookIdeaCanonical[] → EnhancedBookConcept → BookOutline (parts/chapters/learning objectives)
Use it for
- →Lead magnets / guides for the CTA ladder's lowest rungs (read-the-Guide offers)
- →principal-issues pipeline: corpus reading → candidate essay/series outlines
- →Client play: a business's expertise → an ebook/guide that powers their email capture
See it work
example outputSource: a deep-research dump on AI-assisted performance reviews; run through the full ideas → concept → outline pipeline.
Book Concept — The Reviewed Machine: Fair AI in Performance Reviews
Status: outline · Source: deep_research
The idea
A practical playbook for HR and people-analytics leaders deploying AI in performance reviews without importing bias or eroding trust. Pairs the measurement discipline (validity, adverse-impact testing) with the change-management reality of telling a workforce an algorithm now touches their ratings.
Value proposition: the first review-process book written for the team that actually has to defend the model — not the vendor selling it.
Unique selling points
- A four-test fairness checklist any people team can run before go-live
- Real adverse-impact math, worked end to end
- Scripts for the "we're using AI in your review" conversation
Target audience
- Primary: people-analytics leads + HR directors at 500–5,000-person firms piloting AI in talent processes
- Secondary: comp & total-rewards teams; employment-law counsel reviewing the rollout
Market opportunity
- Timing: regulatory pressure (NYC LL144-style audits) is making "we can't explain the model" a liability
- Trend: every major HRIS is shipping AI review features faster than buyers can govern them
Positioning
Not another "AI will transform HR" book — a field manual for keeping the review process fair when a model is in the loop.
Differentiators: methodology-first, defensible, written for the buyer not the seller.
Outline
Part I — Before You Switch It On
- What a review is actually measuring — Objectives: name the construct; distinguish behavior from outcome; spot the proxy trap.
- The four fairness tests — Objectives: run validity, reliability, adverse-impact, and explainability checks on a model.
Part II — Running It in the Open
- The disclosure conversation — Objectives: script the employee-facing message; pre-empt the trust collapse.
- Calibration with a model in the room — Objectives: keep human judgment accountable; audit drift quarterly.
Part III — Defending It
- The audit you'll be asked for — Objectives: assemble the evidence file; survive a regulator or a grievance.
Two parts, five chapters drafted at outline stage — ready to advance to writing.
Run it on your data
Call it on your own inputs — over the API, or hand it to your AI agent via MCP. Discovery is open; running it is metered.