MaxedS Tools
Salesbook.
PitchBook answers “should I invest?” Salesbook answers “should I pilot?” It is the Fortune 500 sales-readiness registry: which startups are credibly ready to sell into and deploy at the world’s largest enterprises — evidence-graded, buyer-lensed, and built for the age of agents.
An honest comparison
PitchBook is the institutional standard for investment diligence. Salesbook does a different job. Here is exactly where they differ — and where they don’t compete.
| PitchBook | Salesbook | |
|---|---|---|
| Core question | Should I invest? | Should I pilot? Is this startup sales-ready for the F500? |
| Lens | Investor — VC fundability | Enterprise buyer — deployment readiness |
| Primary data | Funding rounds, valuations, investors, financials | Deal tombstones (F500 wins), buyer-fit scores, pilot mechanics |
| Company universe | VC- and PE-backed companies | Startups selling into the F500 — funded or not |
| Evidence standard | Analyst-curated | Every claim sourced, dated, confidence-labeled. Low-confidence fields are labeled, not smoothed. |
| Buyer decision fields | Not structured for pilot decisions | 14-field buyer ontology — stage, categorized ask, honest weakness, pilot data needs, 90-day milestone |
| Agent access | API on institutional plans | MCP, agent-queryable — built for buyer and seller agents |
| Price | Institutional subscription | Free while we expand the corpus |
Characterization by MaxedS, October 2026. PitchBook is a Morningstar company; this table describes product positioning, not a review of PitchBook’s quality at its own job — investment diligence — where it remains the standard. Corrections welcome.
How it works
Three steps. No black boxes.
01
Evidence-graded profiles
Every startup profile is built from public sources and founder declarations. Each field carries its source, date, and confidence. Founder-declared fields are labeled attested; AI-inferred fields are labeled with their confidence. Nothing is smoothed.
02
Deal tombstones
Public claims of enterprise commercial wins — founder posts, press releases, case studies — collected and graded by strength, from named contracts down to logo walls. Tombstones measure announced wins, never unqualified “traction.”
03
Ranked against buyer archetypes
Each sector gets an anonymized archetypal buyer profile — buying mechanics, pilot bars, what “good” looks like — modeled on real F500 buying patterns. Startups are scored on fit to the archetype, tombstone strength, profile completeness, and growth signals.
The evidence standard
Not all announcements are equal. Every tombstone is graded:
Contract
Named F500 customer with contract or rollout language — “selected for enterprise-wide rollout.” Highest weight; upgraded to corroborated only with F500-side confirmation.
Pilot
Named F500 with pilot or PoC language — “piloting with,” design partnership.
Partnership, vague
Named F500 with no commercial specificity — “excited to partner with.” Counts, but discounted: this is the gameable tier.
Logo
“Trusted by” walls and website logos, unnamed or unlinked. Weakest signal — presence only.
Sectors: retail and healthcare first
Starting where our buyer ontology actually applies — derived from real enterprise buying patterns in these two sectors. A Top 100 list per sector, ranked on the rubric above.
Archetype R1
High-volume consumer retail
Buy-first for non-differentiating tech; vendor data handling heavily scrutinized. Pilots must show measurable member or operator value.
- Very limited data requirements win pilots
- Rapid PoCs — signal in weeks, not quarters
- Proof is numeric: measurable improvement or friction reduction
- Exploration explicitly ≠ implementation commitment
Archetype H1
Large integrated health system
Committee-held buying with competitive-bid discipline. Clinical workflow fit and safety signals dominate.
- Evaluation is async-consumable — no live-pitch dependency
- One consistent format; one categorized ask
- Honest weakness and honest non-fit are expected, not punished
- 90-day success milestone stated up front
Honesty labels
- Archetypes are composites, v0 — synthesized from public buying patterns across many large enterprises. They are not modeled on any single buyer, use no confidential information, and any resemblance to a specific company is coincidental.
- Rankings are provisional — methodology is published and weights are versioned; old lists remain interpretable after the rubric changes.
- Fields the AI cannot verify are labeled by confidence or left to founder declaration. Plausible-but-wrong is worse than missing.