Everyone says judgment matters. Nobody defines what it is. We did.
Three measurable dimensions. Nine researchers. The first product that develops what AI is eroding.
$1–$1.25M Seed · cbrp.ai
Law firm alerts are written for everyone. A GC at a PE-backed logistics company and a GC at a public biotech get the same briefing. The work of figuring out what actually matters to your company falls entirely on you.
The GC sees risk through a legal lens. The VP of Operations sees it through an operational lens. Nobody sees where those perspectives diverge. The most dangerous exposures live in the gaps between how different functions perceive the same event.
Pilots have flight simulators. Surgeons have cadaver labs. Legal professionals develop judgment through years of unstructured experience with no feedback loop. Whether you get good depends on what cases you happen to see and who happens to mentor you.
$12B+ is spent annually on legal technology. Nearly all of it makes work faster. None of it makes thinking sharper.
What We Built
We defined what judgment consists of in advisory contexts: connection density, anticipation rate, and Trusted Advisor EQ. Then we built the product that develops all three.
Personalized legal intelligence for in-house counsel.
Anonymized GC demand intelligence for law firms.
The flight simulator for legal judgment.
Board intelligence for individual directors.
Compliance intelligence for small businesses.
One engine. Five products. Every interaction develops the professional.
AI will reshape the practice of law, that much is certain. The tools in the current wave of AI disruption drive process efficiency and speed. None of them help trusted professionals think better in a world that's changing faster than any individual can track.
Jayden Daniels broke the NFL rookie completion record by using VR to simulate thousands of defensive reads before he ever took a snap. Pilots train in flight simulators. Surgeons practice on cadaver labs.
Legal professionals develop judgment through years of unstructured experience with no feedback loop. The profession that makes the highest-stakes decisions in the most complex environment has the least structured way to develop the skill that matters most: judgment.
20%
Doctors who used AI regularly saw their diagnostic accuracy drop 20% when AI was turned off. First empirical proof of AI-induced deskilling.
The Lancet Gastroenterology & Hepatology, 2025
"What is going away is the quiet bargain firms relied on for decades: that judgment would be trained automatically through drafting reps."
Bloomberg Law, March 2026
"AI cannot replace high-level human judgment. The ability to interpret AI-generated insights and make strategic, high-stakes decisions remains a purely human domain."
Jamie Dimon
CEO, JPMorgan Chase
CBRP enables legal teams to adapt and scale with AI-driven change, whatever form it takes.
Not by automating their work, but by sharpening the human judgment that no automation can replace.
| Harvey | Contract execution and AI-assisted drafting |
| Legora | Document review, research, drafting workflows. $5.5B valuation, 800+ firms. Zero judgment infrastructure. |
| GC AI | Document interaction and retrieval |
| Spellbook | Word-native contract drafting and review. 4,000+ teams. |
| CoCounsel | Legal research workflows (Thomson Reuters/Westlaw) |
| Lexis+ AI | AI research layer on LexisNexis. Incumbent play. |
| Ironclad | Contract lifecycle management |
| Diligent | Board materials and governance process |
Billions invested. All execution. None of it helps a legal team make better judgment calls.
| Connective | Shows what each development touches in your world: the connections a senior advisor would see |
| Contextual | Knows your companies, committees, jurisdiction. Intelligence is specific, not generic. |
| Role-aware | Specialist, generalist, and GC see different connections from the same event |
| Networked | Cross-functional risk sensing: when legal and operations diverge, that gap is the intelligence |
| Calibrated | Closed-loop feedback: outcome detection shows whether the call was right |
| Compounding | Every interaction builds connection density. The product develops judgment through use. |
| Measurable | Three dimensions: connection density, anticipation rate, Trusted Advisor EQ |
This layer didn't exist. We built it.
Personalized legal intelligence for in-house counsel. Company-specific context turns generic news into specific briefings. Every scored interaction builds a portable Judgment Profile. At the enterprise tier, the GC orchestrates: business unit leaders score alerts through their operational lens, and divergences reveal where legal and business see risk differently. 100K+ US GCs.
The other side of the CounselBrief data asset. Anonymized GC demand intelligence sold to law firms as a BD tool. Which practice areas are surging? Where is judgment diverging from market consensus? Law firms buy visibility into the demand signal their clients generate. They also feed their memos through the platform, reaching GCs engaging with those exact topics. Bidirectional marketplace.
The flight simulator for legal judgment. Three channels:
Every law student trained on Acuity becomes a future GC who already knows the platform.
Board intelligence for individual directors. Nobody serves them: Diligent sells to the corporate secretary, AlphaSense costs $10K+/seat, NACD is education only. Committee-contextualized alerts. D&O severity up 27% to $56M avg settlement. 250K+ US board seats.
Same engine, calibrated for a non-legal audience. 33M+ US small businesses, 77% relying on Google for regulatory guidance. Translates complexity into actionable guidance without a lawyer. A product trained on Wall Street, walked down to Main Street.
One engine. Five products. Each one deepens the advantage.
CounselBrief Individual + enterprise plans
Source: ACC, CLOC
CB Acuity Judgment training for associates, partners, and law students
Source: NALP, AmLaw, ABA 509
CB Analytics Anonymized GC demand intelligence + targeted memo distribution
Source: AmLaw, NLJ 500
DirectorBrief Individual + enterprise plans
Source: BoardEx, NACD, PitchBook
RegulatorPulse Individual + SMB plans
Source: SBA, U.S. Chamber
Five buyer pools. Five products. The same engine — with a different persona — serves each one.
Each data point maps to a dimension:
Scores + gap patterns → Connection Density · Fragment responses + anticipation → Anticipation Rate · Dismiss reasons + context signals → Trusted Advisor EQ
What we don't collect: AI conversation content. Ever. Company-specific data stays siloed per account. Judgment patterns train the base model for everyone. This is architecture, not policy.
Level 1: Direct Signal
GC scored IP assignment as "Material" 12 times. The system prioritizes IP alerts. Any preference form could capture this. Necessary but not sufficient.
Level 2: Adjacent Inference
GC never scored derivative works, but their IP assignment, trade secret, and open source scores triangulate around it. The system infers a priority the GC hasn't articulated. No preference form captures this.
Level 3: Closed-Loop Calibration
GC dismissed an antitrust signal in March. The DOJ opened an investigation in April. The system detects the outcome and adjusts future relevance weights. The system and the human improve together.
Level 4: Network Intelligence
When 30 PE-backed GCs flag the same FTC enforcement pattern, the 31st sees it proactively. Individual judgment compounds into collective intelligence.
Why This Data Matters
"Ant colonies work through a network of very simple local interactions.
Individual ants use their recent experience to decide what to do.
The whole colony operates through a network of these very brief interactions."
— Deborah Gordon, Stanford University
Relational. Discrete. Explicit.
Judgment scores across 15 areas
Smart Dismiss reasons
Company intelligence profiles
Vote signals & corrections
Acuity drill performance
What users tell us.
No single user creates this.
▸ What the profession cares about right now
▸ Where judgment is weakening across roles
▸ Judgment profile archetypes by industry
▸ Market-level regulatory attention signals
▸ Predictive patterns no survey captures
What the colony knows.
Vector. Continuous. Implicit.
Dwell time & engagement depth
Navigation path signatures
Topic combination patterns
Cross-user attention convergence
Temporal priority shifts
What users reveal without knowing it.
Through isolated interactions, each ant produces macro intelligence.
Similarly, each user's interaction with our tools produces valuable macro data that compounds with every user who joins.
Each user's company becomes an intelligence node. At signup, we build a structured profile: what they do, where they operate, what regulations apply. Every alert is evaluated against this model. "This EPA amendment impacts your polymer coatings product line," not "this may affect manufacturing." Cold start drops from months to weeks.
Across all users, attention patterns reveal what's emerging before it's news. When 30 fintech GCs shift focus to state money transmission licensing, the 31st sees it proactively. Cross-role correlation multiplies: GC + director at same company engaging the same development = confirmation signal neither produces alone.
Both models train from passive signals — dwell time, clicks, scroll depth. No chat logs. No surveys. Users engage with their briefing and the system learns.
Others handle process.
CBRP develops judgment.
Enterprise subscribers don't just receive intelligence. They operate inside a system that learns their company, connects their team, and gets sharper every day.
The GC invites business unit leaders as Signal Correspondents. Each scores curated alerts through their operational lens. The GC gets aggregated risk visibility across the entire organization. The real intelligence is in the divergences: when Legal scores "Not Material" but Operations scores "Material," that gap is where hidden exposure lives.
Continuously updated intelligence file: acquisitions, litigation, exec changes, risk factors, regulatory filings. Every alert is contextualized against what the company is actually facing today, not a static profile set during onboarding.
Cross-portfolio alerting for PE firms. When one portfolio company faces a regulatory action, every sibling GC is briefed simultaneously. The institutional memory no org chart captures.
Personalized daily brief arrives. 8 alerts ranked by company relevance, each with specific practice points and action items grounded in the living dossier.
GC scores alerts: 2 Material, 3 Monitor, 3 Not Material. Dismisses one with a note: "We exited that market in Q3." System learns.
System pushes a targeted update request to the VP of Operations on an OSHA development. She scores it "Material" and adds context. GC now has an operational read Legal wouldn't have surfaced alone.
Sister portfolio company is implicated in a CFPB enforcement action. GC knows before the PE sponsor reaches out.
5 judgment calls recorded. 1 divergence resolved. 2 action items generated. Tomorrow's brief will be sharper because of today's decisions.
Enterprise
$30-50K/yr
Individual
$99-199/mo
In the contexts where trusted professionals operate, judgment consists of three measurable dimensions. Grounded in nine researchers across three decades of cognitive science. No prior framework defines professional judgment this way. We're the first.
How many threads does the advisor see when a single development lands? The junior sees a compliance checklist. The trusted advisor sees the CEO's trading plan, the acquisition, the comp committee, and the D&O renewal. This is the core skill AI is eroding.
Research: Klein (1998) pattern recognition; Reyna (2012) gist-based reasoning; Epstein (2019) cross-domain analogical thinking
How many connections does the advisor see before anyone points them out? Connection density operating in real time. The difference between the advisor who raises the implication and the one who reads about it later.
Research: Ericsson (2008) deliberate practice; Tetlock (2015) calibration; Kahneman (2021) noise reduction
Which connections matter to this client at this moment? The advisor who sees twelve threads but knows which three the CEO needs at 9 PM. The relational dimension that separates an analyst from an advisor.
Research: Heineman (2016) partner-guardian; Grant (2021) challenge networks; Maister/Green (2000) trust progression
20% diagnostic decline in doctors after regular AI use. First empirical proof of deskilling. Lancet, 2025
"Judgment was trained automatically through drafting reps. Those reps are gone." Bloomberg Law, 2026
"We can't teach what we can't describe." Everyone sees the problem. Nobody has a framework for what judgment consists of. Thomson Reuters, 2026
Score, then connect. User commits a read (Material / Monitor / Not Material), then sees the cross-domain connections a senior advisor would make. Daily reps across 15 practice areas.
Network as panel. "78% of PE-backed GCs scored this Material." The gap between your call and the cohort is where judgment sharpens.
Declining delta. Success = the user needs fewer system-provided connections over time. They're building the pattern library AI took away.
Nine researchers. Three dimensions. One product. Everyone sees the problem. We defined the skill. Then we built the product that develops it.
CEO & Co-Founder
28 years in BigLaw. Saw firsthand how judgment is built — and how the apprenticeship model is breaking. Built all four CBRP products.
Co-Founder
General Counsel and Chief Legal Officer for over 15 years, following over 15 years in BigLaw. Lives the in-house counsel workflow every day.
First hires: engineering, content, and marketing. Team stays lean.
One founder built the product because he lived the problem for 28 years. The other lives it every day as a sitting CLO.
Key signal: Enterprise accounts at $40K/yr anchoring ARR from day one. Product-market pull without paid acquisition.
| Year 1 | Year 2 | Year 3 | |
|---|---|---|---|
| Subscription ARR | $383K | $1.4M | $3.1M |
| Enterprise ARR ($40K/yr accts) | $160K | $640K | $1.6M |
| CB Ecosystem % | 84% | 90% | 93% |
| Enterprise + Acuity ARR | $220K | $1.0M | $2.4M |
This raise builds the team and funds the path to profitability. A Series A becomes a choice, not a necessity.
CBRP Co. | cbrp.ai | The Judgment Layer.
12-month
168 paying CB individual subscribers, 4 enterprise contracts, 2 Acuity law firm deployments. $383K ARR. Unit economics proven.
18-month
Acuity in 5 law firms and 2 law schools. Enterprise deals accelerating. Five products live across all segments. $675K ARR run rate.
24-month
$1.4M ARR. CB Ecosystem at 90% of revenue. 12 Acuity deployments across firms and schools. Judgment data compounding. Raising Series A from a position of strength.
40% — Team
$500–625K
30% — User Acquisition
$375–470K
20% — Content Engine + Model
$250–312K
10% — Operations
$125–156K
"The subscription funds the data. The data trains the model. The model sharpens the product."