Fund Manager Evaluation

Evaluate fund managers with systematic intelligence

Six proprietary signals score every fund manager across dimensions that matter: alignment, relationship depth, track record, process rigor, organizational health, and terms. The machine flags what traditional DDQs miss.

The Process
From intake to ongoing intelligence
A systematic pipeline for evaluating and monitoring fund managers. Each stage produces machine-scored outputs that feed the next.
01

DDQ Intake

Manager submits DDQ. AI pre-scores across six signals within 48 hours.

02

Signal Scoring

Heat-mapped evaluation. Machine flags anomalies, cross-references claims.

03

Machine Synthesis

Composite recommendation with probing questions for IC discussion.

04

IC Decision

Investment committee reviews synthesis. Invest, pass, or probe further.

05

Ongoing Probing

Continuous monitoring: performance updates, media, filings, signal drift.

Fund Manager Signals
Six dimensions of manager quality
Purpose-built signals for evaluating fund managers. Distinct from the company-level paradigm signals used in direct investment evaluation.
Signal Configuration
Configurable weights reflecting DFO priorities. Relationship and alignment weighted higher given principal-to-principal approach.
Alignment
Thesis overlap with DFO interests
25%
Relationship
Trust depth, duration, cultural fit
20%
Track Record
Performance quality, consistency, realized vs. unrealized
20%
Process Rigor
How systematic, codified, repeatable
15%
Org Health
Team depth, culture, key person risk mitigation
10%
Terms & Governance
Fee alignment, transparency, LP protections
10%
Worked Example
M31 Capital — Evaluation
M31 Capital applied through the standard DDQ intake. Below is how the machine scored, synthesized, and prepared their evaluation for IC.
Atypical Manager Profile Detected. M31 Capital does not fit the standard fund-manager archetype. Rather than a traditional GP raising a fund to deploy into a defined strategy, M31 operates as an investment intelligence firm — their process and systematic thinking is the core asset, not the fund vehicle itself. The $10M fund size is a proof-of-concept for a larger systematic capability. Traditional scoring dimensions (fund size, AUM growth, LP concentration) may underweight their actual value proposition. The relationship here is better understood as principal-to-principal partnership rather than LP/GP allocation. Scoring below reflects this adjusted framing.

Signal Heat Map

SignalScoreConfidenceAssessment
Alignment9.2HighDeep overlap: systematic investing (foundational DNA), AI/frontier tech (core thesis), ocean/climate (DePIN mapping), EdTech (adjacent). Multiple active sectors match DFO interest areas.
Relationship9.5HighJen Berry: 16 years at Bridgewater, direct work with Ray Dalio as COO of Investment Engine. Michael Swensson: ex-Bridgewater COO. Decades-long institutional trust. Verified.
Track Record8.7Med+203% historical IRR. Realized exits confirm (Celestia 8.3×, Riot 2.5×). Unrealized positions (xAI 9.6×, Bless 13.0×) carry mark-to-market risk. Exceptional but 5-year track record — still early.
Process Rigor8.9HighProprietary signal framework for paradigm detection. 7-stage pipeline, 5 gates, 55+ checks. Level of systematization rare in sub-$100M funds. Ongoing codification effort acknowledged.
Org Health6.8HighZero turnover (positive). Team of 8 (lean). Key person risk: very dependent on Nathan Montone — self-identified (transparency positive). Bridgewater cultural DNA strong.
Terms & Gov.7.4High2% mgmt / 20% carry (standard). Escalates to 30% above 5×. 1% GP commitment. No preferred return hurdle. Co-invest offered selectively at standard fees.
Machine Synthesis
▲ ADVANCE TO DEEP DILIGENCE — WITH ADJUSTED FRAMING

M31 Capital is not a fund allocation — it's a capability partnership

Standard fund-manager evaluation metrics systematically undervalue what M31 offers. Their $10M fund is proof-of-concept for a larger thesis: building the Bridgewater of private markets. The signal framework, systematic process, and Bridgewater alumni network represent infrastructure that doesn't exist elsewhere in frontier-tech investing.

The recommendation is to advance M31 not as a traditional fund allocation but as a strategic intelligence partnership — where the DFO gains access to systematic paradigm detection, deal flow in suppression-signal sectors, and a post-investment Labs capability that provides operational accountability traditional VC lacks.

Key risk: Key person dependency on Nathan Montone. Primary mitigation: systematization effort actively converting tacit knowledge to codified process. Secondary: Bridgewater-trained team provides institutional culture even if individual contributors change.

8.5
Weighted Composite
+203%
Historical IRR
6.9×
Avg Exit MOIC

DDQ Detail

I. Strategic Alignment & Relationship
▾
Can you describe your history/relationship/connection with the Dalio Family?
Close relationship over long period of time
Decades-long relationship. Jen Berry spent 16 years at Bridgewater Associates including as COO of the Investment Engine. Michael Swensson served as COO at Bridgewater.
Platform Intelligence: Verified. Strongest relationship signal in current pipeline.
Top themes of investment interest? Overlap with Dalio Family interests?
High amount of overlap
Systematic investing (foundational), AI/frontier tech (core), ocean/climate (DePIN mapping), EdTech (adjacent via decentralized credentials).
Signal Layer: M31's active sectors score high composite. Concentrated in sectors with optimal entry timing.
Specific learning opportunities through this investment?
High amount of learning opportunities
Signal framework for paradigm detection, Web3×AI convergence research, systematic approach to identifying shifts before consensus.
II. Organization & Track Record
▾
Regulatory scrutiny, court proceedings, LP scrutiny?
Never
No past matters involving the Firm, its affiliated entities, or any personnel.
Degree of key person risk?
Very dependent on one person
Nathan Montone is the key person. Active systematization effort is core mitigation strategy.
⚠ Flagged for IC: Manager self-identified this risk (positive transparency signal). Worth probing: how much of Nathan's process is now codified vs. intuitive?
Aggregate net IRR across all funds?
>20% overall net IRR
Historical +203% IRR. See Fund & Investment-Level Track Record.
Platform Intelligence: Exceptionally high. Celestia (8.3×) and Riot (2.5×) confirmed realized. Unrealized positions carry mark-to-market risk. Net of unrealized, still top-decile.
Senior team turnover?
No senior-level turnover
Zero turnover since founding in 2020.
Platform Intelligence: Zero turnover across 5+ years in a volatile asset class is exceptionally strong.
Firm culture characterization?
Well established, strongly fits strategy
Nearly half the team from Bridgewater. Principles-based, meritocratic culture. Radical transparency and systematic decision-making.
III. Strategy & Edge
▾
What gives your firm a sustainable competitive advantage?
Unique edge, high sustainability
Three reinforcing pillars: Bridgewater-trained people, systematic signal-based processes, +1,100% track record. Proprietary paradigm detection framework. M31 Labs post-investment support.
Signal Layer: The systematic process IS the edge. No other emerging-tech fund has comparable framework. "Suppression signal" methodology proprietary and validated by real exits.
Investment process characterization?
Represents edge over competitors
7 stages, 5 gates, 55+ evaluation checks. Systematic pre/post-investment process.
Platform Intelligence: Verified via documentation. This systematization level is extremely rare in sub-$100M funds.
Operational value-add?
Exceptional
M31 Labs: 6-8 week founder bootcamp, gap analysis, coaching, BW-alumni operator network. Post-investment accountability layer traditional VC lacks.
IV. Risk, Terms & Governance
▾
Risk of losing >30% of capital?
>40% chance
"Feature, not bug of venture investing." Liquid VC structure provides mitigation via exchange/secondary market relationships.
Platform Intelligence: Honest self-assessment (positive). Liquid structure genuinely differentiated — most venture funds have zero liquidity pre-exit.
Management fee?
2.0% of committed capital
Standard for early-stage venture strategy.
Carried interest?
20% / 30% above 5×
Standard 20%. Escalates to 30% after LPs receive 5× contributed capital.
⚠ IC Probing Point: 30% carry escalator above 5× worth discussing. Does escalator align LP interests at the upper end?
Expected distribution timeline?
Within 3 years from initial close
Historical average 18-24 months from first check to realized exit.
Ongoing Intelligence
Probing & Live Monitoring
Post-IC, the platform continuously monitors performance, media, filings, and signal drift. Alerts surface when attention is needed.

Fund Performance

Bitcoin Fund (2020)+1,111% · 12.9×
DeFi Fund (2020)+1,135% · 12.3×
Web3 Fund (2022)+479% · 5.8×
Ventures SPVs (2021-24)$36.5M AUM
Total AUM~$100M
Venture Investments21 companies
VC Investment Losses0

Active Probing Questions

P-01 · Key Person RiskWhat is the current state of systematization? How much of Nathan's process is codified vs. still intuitive?
P-02 · Fund SizingWith $10M fund size — is this a proof-of-concept for a larger vehicle? What is the roadmap to $50M+?
P-03 · Carry AlignmentHow does the 30% carry escalator above 5× align LP interests at the upper end?
P-04 · Labs ScalabilityCan M31 Labs' bootcamp model scale beyond the current portfolio? What is the capacity constraint?
P-05 · Partnership FramingWhat does a strategic intelligence partnership look like vs. a traditional LP commitment?

Portfolio Company Snapshot

Bless (DePIN)13.0×
xAI (AI)9.6×
Celestia (Blockchain)8.3× EXITED
Huddle01 (Web3)6.7×
Aizel Network (AI×Web3)2.6×
Riot Platforms2.5× EXITED
Gensyn AI (DeCompute)2.5×
Space & Time (ZK Data)2.0×

Update Feed

LP UpdateFeb 2026Illustrative
M31 Ventures Fund II nearing close. Over 85% of target raise secured. 75%+ existing investor re-up rate.
MediaJan 2026Illustrative
Portfolio company Perplexity AI raises at $9B+ valuation. M31 entry positions fund for potential IPO exit within 12-18 months.
Jan 2026Illustrative
Nathan Montone publishes research on "Suppression Signals in AI Agent Infrastructure" — identifies regulatory opposition as bullish indicator.
Filing2025
M31 Capital files as Exempt Reporting Adviser. No material changes to business, personnel, or regulatory status. Clean filing.
LP UpdateDec 2025Illustrative
Portfolio company Bless marks up to 13.0× MOIC following DePIN sector momentum. M31 Labs bootcamp contributed to product pivot.
MediaNov 2025Illustrative
M31 Capital featured in institutional coverage highlighting systematic approach. Compared to "Bridgewater for private markets."
Direct Investment Evaluation

Evaluate companies with custom intelligence signals

A systematic process for evaluating individual companies — from signal detection through deep diligence to post-investment accountability. Built on a 16-dimension scoring matrix, 55+ due diligence checks, and proprietary signals developed with DFO.

Signals
Custom signal framework
We would develop proprietary signals tailored to DFO's investment philosophy and priority areas. Below is an illustrative framework based on DFO's known interests and Dalio Principles. Final signals, weights, and thresholds would be co-designed.
Illustrative — Would Be Custom. These signals are a preliminary draft based on DFO's publicly stated priorities (OceanX, Dalio Education, Health Justice, Endless Network) and Ray Dalio's Principles (systematic thinking, radical transparency, macro cycle awareness). In practice, M31 would work with DFO to design, weight, and calibrate a bespoke signal framework reflecting the family's specific investment thesis and values.
Illustrative Signal Configuration
Draft signals reflecting DFO priorities. Weights and thresholds to be co-designed.
Mission Alignment
Overlap with DFO priority areas: ocean, education, health equity, digital access, financial inclusion
25%
Systematic Edge
Codified processes, data-driven decisions, principles-based operations — Bridgewater DNA
20%
Macro Positioning
Alignment with structural shifts: great power cycles, changing world order, paradigm transitions
20%
Impact Multiplier
Generates both returns AND measurable social impact. Philanthropic leverage potential.
15%
Resilience & Antifragility
Robust under stress. Diversified risk. Gets stronger from volatility and opposition.
10%
Network Effect
Strengthens DFO portfolio, creates synergies across investments and philanthropic initiatives
10%
Evaluation Frameworks
Scoring matrices & checklists
Click to expand the 16-dimension company scorecard (scored 1–5), the pre-investment checklist with 55+ due diligence checks, and the values alignment screen.

Values Alignment Screen

GATE · PASS/FAIL
▾
Illustrative — Would Be Custom. Values screen dimensions would be co-designed with DFO to reflect the family's specific principles and philanthropic priorities. Below is a draft based on publicly stated DFO values.

Non-negotiable gate. Must pass all applicable dimensions to proceed to full evaluation.

Meaningful Work & Relationships — Does this company build something that creates genuine value? Are the founders building meaningful relationships with stakeholders, not just extracting value?
Radical Transparency & Truth-Seeking — Is the company built on honest claims and verifiable data? Do founders prioritize accuracy over hype? Are they intellectually humble?
Principled Decision-Making — Does the company operate from codified principles? Are decisions systematic and evidence-based rather than ad-hoc?
Positive-Sum Impact — Does this create value broadly? Does it align with DFO philanthropic priorities (education, ocean/climate, health equity, digital access)? Or is it zero-sum/extractive?
Long-Term Orientation — Is this built for enduring value, not short-term arbitrage? Multi-generational alignment with family legacy?
ResultDecision
FAILDoes not align — pass, regardless of financial merits
CONDITIONALPartial alignment — evaluate on other merits, flag for IC
PASSStrong alignment — proceed to full evaluation

Company Scorecard

16 DIMENSIONS · 1–5 SCALE
▾

Rate each dimension 1–5. Detailed rubrics define each score level. Machine pre-scores and flags dimensions requiring deeper evaluation.

A. Team
1. Founder — Does the founder have the vision, attributes, skills & experience required to build and lead the startup? Evaluates: leadership/vision, passion/drive, experience/expertise, grit, fundraising ability, venture-scale thinking, cash management, coachability, and awareness of gaps / ability to recruit.
2. Team — Does the team have the VAS & experience to execute and support the startup? Evaluates: key hires in place, domain expertise, team cohesion/commitment, filling founder gaps, plan to fill remaining gaps.
3. Advisors — Do the advisors have the VAS & experience to support and assist the startup? Evaluates: advisor quality and engagement (skin in the game), oversight/accountability structures (board members etc).
B. Opportunity
4. Idea — Does the idea address a clear problem and deliver a valuable solution? Evaluates: novelty/value, clear vision/design, problem size and fit, competitive advantage/defensibility (IP, trade secrets), venture-scale size.
5. Plan — Does the plan match up with the requirements of the startup and building process? Evaluates: practical visualization/strategic clarity, scenarios/assumptions, milestone tracking, accountability cadence.
6. Feasibility — How difficult will the startup be to pull off? Evaluates: progress/traction so far, difficulty level, risk profile (moonshot vs. proven path).
C. Market
7. Accessibility — How easy is it to get to the customer and deliver the solution? Evaluates: access to customer, ease of delivery, land-and-expand potential.
8. Market Size — Is the market big enough for venture-scale returns? Growing or shrinking? Evaluates: market size, momentum/trends, macro tailwinds, evidence of market demand.
9. Competition — How fierce is the competition and how differentiated is the solution? Evaluates: competitive landscape, disruption likelihood, market share potential.
D. Traction
10. Product — How fast and well have they built their product? Evaluates: quality of product, speed of innovation/evolution.
11. Demand — How much demand have they demonstrated or realized? Evaluates: customer interest, retention/usage after purchase, referral behavior, feedback quality.
E. Economics
12. Finances — How good are the financials? Evaluates: financial model quality, budget planning/adherence, cash runway forecasting, clean balance sheet/cap table.
13. Monetization / Business Model — Does the business model fit and make sense? Evaluates: pricing/margin/unit economics, model durability, market match, need-to-have vs. nice-to-have.
14. Profitability — Given the costs, plan, and monetization, is there a clear path to profitability? Evaluates: traction against plan, believable path, risks seen and accounted for.
F. Misc
15. X Factor — Does the startup demonstrate exceptional qualities? The intangible. A killer product, a founder you'd follow anywhere, an amazing macro trend, genuine innovation in a broad-need area. This allows weighting of qualities that don't fit neatly elsewhere.
16. Deal / Terms — Do the terms accurately value the risk and opportunity? Evaluates: deal dynamics (co-investors, round size), valuation, terms (liquidation preference, protective provisions, founder vesting, anti-dilution), further fundraising/dilution risk, exit clarity and probability.
ScoreMeaning
1Significant gaps or red flags. Would not invest on this dimension alone.
2Below expectations. Risks outweigh positives or key elements missing.
3Meets baseline. Viable but with questions, gaps, or areas needing development.
4Strong. Clear strengths with minor gaps. Would want to invest in this dimension.
5Exceptional. Top-tier on this dimension. No meaningful concerns.

Pre-Investment Checklist

55+ CHECKS · 6 PHASES
▾

Every check must be completed or explicitly waived with documented reasoning before capital goes out the door.

1. Due Diligence Questionnaire — Founder / Project Team
Founder & Team
Founder intelligence and competitiveness assessment
Founder "fire" — ambition, drive, resilience, hustle
Team experience, credibility, and gaps
Team tenure and historical turnover
Organizational structure and track record
Strategy & Market
Strategy and core investment thesis
Market size and growth potential
Competitive landscape and key competitors
Barriers to entry and defensibility
Traction to date (users, revenue, pilots, KPIs)
Roadmap progress vs. expectations
Financials & Economics
Historical financials
Projections and assumptions
Burn rate and runway
Use of funds
Scenario modeling (upside / base / downside)
Deal Structuring
Deal terms and valuation
Round structure and cap table verification
Growth plan, deliverables, exit expectations
Legal & Technical
Past or ongoing legal actions
Past financial/code audits
Technology diligence (product, IP, security)
2. Investment Readiness — Deal Lead
One-pager completed ("Why I like it")
Initial DDQ screening & scoring
AI agent scoring & synthesis
Independent research completed
Investment snapshot / memo prepared
3. Background & References — Ops Team
Background checks (AI/automated for <$250K; enhanced EDD for >$250K)
2-3 investor references
2-3 personal references
Ask founder: worst thing they've been through and how it changed them
4. Conflicts & Legal Review — Legal / External Counsel
Conflicts check against portfolio companies
Conflicts check against LPs
Entity Docs Review
Certificate of Incorporation / Charter (incl. Amendments)
Bylaws or Operating Agreement
Capitalization Table (fully diluted, pro forma)
Shareholder / Member Register w/ IDs
Board & Officer Registry
Company Policies (IP assignment, confidentiality)
Trademarks and/or Patents
Deal Docs Review
Term Sheet (final agreed version)
Stock / Unit Purchase Agreement
Investor Rights Agreement
Voting Agreement
Right of First Refusal & Co-Sale Agreement
Side Letters (if any)
Disclosure Schedules
Structural Protections
Ownership verification prior to release of funds
Seller representations and warranties
Escrow or milestone-based tranche structures
Clawbacks, liquidation preferences, anti-dilution
Co-investor alignment check
Regulator, sanctions, ESG, and reputational review
5. Strategy & Growth Alignment — Labs
Labs strategy kick-off sprint (6-8 weeks)
Key risks, flags, and gaps identified
Key milestones and KPIs defined
ROI assessment / growth analysis
Post-investment plan & roadmap: advisory, hiring, fundraising, GTM, infrastructure
6. Decision / Closure — Investment Committee
Confirm conviction: Final scoring, risk review, alignment with fund strategy
Set structure: Investment instrument, vehicle, key terms
Size the investment: Check amount, ownership targets, portfolio fit
Decide: IC discussion and formal vote (approve / conditional / decline)
Execute: Authorization, legal close, post-investment ownership
Evaluation Pipeline
Seven stages from signal to exit
Each stage has a gate question. The machine evaluates evidence at each gate and produces a go/no-go recommendation.
01
Signal Detection
Ongoing
02
Sector Mapping
1-2 weeks
03
Company Screen
48 hours
04
Deep Diligence
2-4 weeks
05
IC Decision
1 meeting
06
Deal Execution
1-3 weeks
07
Active Monitoring
Continuous

Signal Detection

Gate: Do custom DFO signals indicate this is worth deeper evaluation?

The machine continuously monitors data sources aligned with DFO's custom signal framework, surfacing opportunities that score above threshold.

Mission alignment with DFO priority areas detected
Macro positioning: structural shift or cycle alignment
Impact multiplier potential assessed
Systematic edge indicators in founding team
Resilience / antifragility markers present
Portfolio network effect potential evaluated

Sector Mapping

Gate: Does this sector have enduring inefficiencies and a multi-decade opportunity runway?

Map the sector: key players, subsectors, value chain, phase timeline. Identify entry timing window.

Sector size and growth trajectory mapped
Key players identified (incumbents, challengers, enablers)
Subsector breakdown with opportunity ranking
Phase assessment: which paradigm phase?
Entry timing: too early, optimal, or too late?
Build vs. buy assessment

Company Screen

Gate: Defensible position in high-signal sector with capable team?

AI pre-screen using the 16-dimension scorecard (1–5 scale). Produces scored intake within 48 hours.

Scorecard pre-score: 16 dimensions across Team, Opportunity, Market, Traction, Economics
Founder quality: intelligence, fire, domain expertise, coachability
Idea assessment: problem/solution fit, novelty, defensibility
Signal alignment score (composite across custom DFO signals)
Competitive positioning within sector map
Initial valuation and entry terms assessment

Deep Diligence

Gate: Under full scrutiny, does thesis hold? Are risks manageable?

Full 55+ check pre-investment checklist. Background checks, legal review, financial modeling, tech audit. Machine cross-references claims against data.

Background checks on founders and key team
Technology audit: code review, architecture
Financial model review and stress testing
Legal: entity docs, deal docs, structural protections
Reference checks (2-3 investor + 2-3 personal)
Full Company Scorecard: all 16 dimensions scored 1–5

IC Decision

Gate: Expected return justifies risk? Strengthens portfolio?

IC reviews complete diligence package with machine synthesis. Decision: invest, pass, or probe further.

Machine synthesis reviewed with probing questions
Portfolio concentration and correlation check
Risk/return asymmetry assessment
Entry terms and structure finalized
Labs post-investment support plan defined
Exit thesis and timeline documented

Deal Execution

Gate: Legal, financial, and operational terms fully documented?

Execute investment. Labs support plan activated. Company onboarded into monitoring platform.

Term sheet signed
Legal documentation completed
Capital deployed
Labs strategy kick-off sprint activated
Company added to monitoring dashboard
Exit thesis parameters set for automated alerts

Active Monitoring

Gate: Is the company executing on thesis? Are signals strengthening or degrading?

Continuous signal monitoring with automated alerts for phase transitions, signal degradation, or exit triggers.

Custom DFO signal tracking (monthly recalculation)
Phase transition alerts (automated)
Financial performance vs. plan
Competitive dynamics shifts
Intervention triggers (if signal degradation)
Exit timing optimization
Example Portfolio
Active monitoring dashboard
Live signal tracking across portfolio companies. Phase transitions trigger alerts. Signal degradation flags intervention.
Illustrative. Below uses M31 Capital portfolio companies as examples to demonstrate how the monitoring dashboard would work. In practice, this would display DFO's own direct investments tracked against DFO's custom signal framework.

Gensyn AI

Decentralized Compute · AI × Web3
ACTIVE

Decentralized network for scalable AI training. GPU shortage + token incentives = paradigm shift in compute.

2.5×
MOIC
Phase 4
Optimal
Hold
Action
Systematic Edge 8Macro Positioning 8Network Effect 9

Bless

DePIN · Edge Computing
ACTIVE

DePIN protocol networking compute from everyday devices to form a global edge computing network.

13.0×
MOIC
Phase 4
Optimal
Hold
Action
Mission Alignment 7Resilience 8

xAI

AI · Truth-Seeking Models
⚠ WATCH

Truth-seeking AI. Monitor for late-cycle dynamics — when major incumbents are all building competing products, early-mover advantage may be eroding.

9.6×
MOIC
Phase 5
Watch
Monitor
Action
Macro Positioning 5 ⚠Late-cycle risk

Space & Time

ZK-Proof Data · AI × Web3
ACTIVE

Decentralized data warehouse with verifiable ZK-proof queries for secure AI applications.

2.0×
MOIC
Phase 4
Optimal
Hold
Action
Systematic Edge 7Macro Positioning 8

Perplexity AI

AI · Search Infrastructure
ACTIVE

Real-time AI search disrupting Google's information monopoly.

1.7×
MOIC
Phase 5
Late Optimal
Hold
Action
Macro Positioning 8Impact Multiplier 9

Celestia

Blockchain · Modular Architecture
EXITED

Exited at optimal phase transition. Validates systematic monitoring approach.

8.3×
MOIC
Phase 6
Exit Triggered
Exited
Status
Macro Positioning 8 (at entry)Resilience 9
Post-Investment Accountability
Labs — audit, support, intervene
Systematic post-investment accountability. Continuous probing, gap analysis, and intervention when signals degrade. The operational layer traditional VC lacks.

Onboarding & Audit

6-8 week bootcamp evaluating operational readiness across every critical dimension.

  • CEO/founder coaching and development plan
  • Gap analysis: people, tech, strategy, product, ops, security
  • Metrics framework and accountability cadence
  • Design partner and customer introductions
  • Governance and reporting structure setup
  • Vision, strategy, and values alignment check

Ongoing Monitoring

Regular probing cadence catches problems early, before they compound.

  • Monthly signal recalculation and phase tracking
  • Quarterly deep probes: financials, team, product
  • Dashboard reporting with automated alerts
  • Competitive landscape monitoring
  • Exit readiness assessment
  • Principles/logic documentation for each case

Intervention & Support

When signal degradation is detected, Labs deploys targeted resources.

  • Fractional executive deployment
  • Bridgewater-alumni operator network
  • Strategic pivot coaching
  • Business development / client introductions
  • Additional funding and round strategy
  • PR/launch support and exit management
Labs creates accountability venture capital typically lacks. Most VC post-investment support is ad-hoc — board seats and occasional introductions. Labs systematizes this with structured bootcamps, defined metrics, regular probing cadences, and automated signal monitoring. When a portfolio company shows signal degradation, intervention is triggered by data, not intuition.

Two flows. One systematic platform.

From fund manager evaluation to direct company investment — your entire decision process, systematized. The same principles-based approach applied to every layer of the investment stack.