MaxedS Tools
SalesBook.
PitchBook answers “should I invest?” SalesBook answers “should I pilot?” It is the enterprise 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.
Search the registry
200 startups, ranked by enterprise sales-readiness. Browse the top 10, or search by company name, problem, or solution — results expand to show deal tombstones and buyer fit.
Is your startup listed?
Search all 316 companies in the registry — ranked, India hub, status watch, and historical records. See exactly what we have on you, and claim your profile to correct it.
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 enterprise? |
| Lens | Investor — VC fundability | Enterprise buyer — deployment readiness |
| Primary data | Funding rounds, valuations, investors, financials | Deal tombstones (enterprise wins), buyer-fit scores, pilot mechanics |
| Company universe | VC- and PE-backed companies | Startups selling into the enterprise — 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, PoC speed |
| 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. Ranked lists per sector (100 each; 4 more in the unranked Status watch lane), 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
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 — low-confidence fields are analyst inference for the founder to correct, not guesses presented as fact. Plausible-but-wrong is worse than missing.
- Beta: all 200 profiles are AI-generated from public sources. The default confidence on every claim is claimed — collected by AI, not independently corroborated. Entity-eligibility sweeps on Oct 5 2026 removed 21 entries from the rankings (bankrupt, merged, or product lines — kept as unranked historical records with terminal badges), excluded 2 more for reported financial distress, and flagged 6 as reported distress — verify before engaging. Rankings run on methodology v0.2.3 (published); every profile shows a readiness score and a separate evidence grade. On Oct 5 2026 a VC-backing gate excluded 12 profiles from the rankings (kept as unranked records with reasons), and 4 more moved to an unranked Status watch lane under published exclusion criteria (credible reported distress — visible with per-source notes, no rank number).
- No paid placement, ever. Startups cannot pay for rank, badges, or profile enhancement. Founders may claim their profile and correct any field free of charge — corrections are labeled founder-attested. MaxedS takes no referral, success, or placement fees from either side of a pilot. If that ever changes, it will be disclosed here first.
- Corrections: every claim carries its source and date. If a claim is wrong, the profile gets corrected and the correction is recorded — founders, buyers, or anyone can flag an error and we fix the record, not just the display.
- What rankings reward: public evidence. A startup with strong but confidential enterprise deployments will rank below a weaker one with public announcements. Rankings measure evaluable readiness, not underlying quality — the founder claim-and-correct loop is the structural fix.