Hypogrid
Read guide Request access

New Agentic hypothesis testing platform for venture diligence

Diligence at AI speed.Conviction at human pace.

Hypogrid is a closed-loop diligence runtime for venture investors and operators. Every claim, contradiction, and decision flows back into a queryable ledger — so each deal makes the next one sharper.

Request access See the method

Built for the diligence patterns of

Multi-agent execution, human-in-the-loop on every gate.

Built to compress the slowest parts of venture diligence: source review, thesis framing, evidence mapping, contradiction finding, and memo assembly.

Manual DD assembly
0
Designed to reduce the hours spent turning raw notes, sources, and model outputs into a partner-ready diligence record.
Public VC playbook sources
0
A curated reference base of venture essays, partner memos, operator talks, and diligence heuristics that informs the workflow.
Analysis methods
0
Market sizing, competitive landscape, cap table, return scenarios, GTM quality, defensibility, cohort economics, and more.
Workflow templates
0
Reusable diligence flows for screening, full DD, IC memo prep, red-team review, operator discovery, and sector-specific theses.

Why not notes or spreadsheets?

Generic tools store diligence fragments. Hypogrid turns them into a decision system.

Notes, sheets, links, and tasks are useful inputs. Hypogrid keeps the investment question at the center: hypotheses, evidence, contradictions, open issues, and IC memo language stay connected as the deal evolves.

Comparison of fragmented notes and spreadsheets flowing into a structured Hypogrid hypothesis ledger with evidence, contradictions, open issues, and IC memo outputs.

01 — Method

Five stages. Five gates. Humans own every advance.

Hypogrid coordinates AI-assisted work across the diligence pipeline: fact-base extraction, hypothesis generation, multi-axis validation, independent critique, and memo assembly. Every stage boundary is an explicit investment-team decision: advance, route back, defer, or stop.

  1. Stage 1 · AI ▸ Human

    Kickoff & Fact Base

    Research, dataroom materials, and call notes are structured into a fact base, a sourced ledger, and an explicit gap list. You decide whether the picture is complete enough to start framing hypotheses.

    Gate 1 outcomes
  2. Stage 2 · AI ▸ Human

    Hypothesis Building

    The workspace proposes hypotheses across seven investment axes — Market, Team, Product, GTM, Financial, Competition, Cap Table — tagged Core, Risk, or Red Flag. You confirm the question set worth validating.

    Gate 2 outcomes
  3. Stage 3 · AI ▸ Human

    Validation

    Specialized analysis packs validate hypotheses in parallel — market sizing, comps, cap table, GTM repeatability, defensibility. Evidence and contradictions land in the ledger as they arrive. You read the matrix and steer.

    Gate 3 outcomes
  4. Stage 4 · AI ▸ Human

    Critique & Rebuild

    An independent critique pass argues the other side. Bear case, contradiction matrix, and IC questions surface here — before they surface across the partner table. You decide if rebuttals hold.

    Gate 4 outcomes
  5. Stage 5 · AI ▸ Human

    Memo & Decision

    Memo-grade outputs are assembled from the strongest claims, with every assertion traceable to a source. Your call — meet, track, pass, term sheet — is recorded in the decisions ledger.

    Gate 5 outcomes

02 — Unit of work

The unit of work is a hypothesis, not a section.

Investment theses spread across decks and spreadsheets blur the line between assumption, fact, and interpretation. In Hypogrid, the hypothesis ID is the unit of work — supports, contradictions, critiques, rebuttals, open issues, and decisions all hang off one identifier. Every output, from hypothesis brief to IC memo, renders downstream from a single ledger of record.

H-C04 Core Thesis Financial · UE
Proven Impact: High Evidence: Strong

Four-year revenue CAGR of ~75% with ARPA up +80% — willingness to renew and willingness to pay are both demonstrated.

Why we believe
  • ARR trajectory: FY22 $12M → FY25 $65M (data room financials)
  • NRR above 110%; zero churn across the top 20 accounts (mgmt interview)
  • Twelve months after the last price change, gross churn moved only +0.3pt
Why we doubt
  • 0% COGS with personnel at 57% of revenue reads as a services business — multiple risk (see H-R02)
  • First-party signal that 5 large accounts have flagged FY26 non-renewal (conflicts with H-K04)
Open issues
  • Segment-level NRR (enterprise / mid-market / SMB) is not yet broken out
  • Twenty-four-month cohort window after the pricing redesign is not yet reached
ledgers/hypotheses-ledger.json · status, impact, and lifecycle log all rendered from the ledger

03 — Matrix

See where conviction is earned. See where it isn't.

Hypotheses sort into three layers — Core Thesis (the reason to invest), Risks (what you live with), Red Flags (true means walk away). Crossed with impact, evidence strength, and status, the picture of where conviction is earned shows up in one screen. The slice below is from a sample Series B case with 21 hypotheses (illustrative — figures are anonymized).

TableKanban
21 hypotheses · 8 proven · 8 in validation · 5 open

Core Thesis 5 hypotheses · 4 proven

H-IDAxisClaimImpactEvidenceStatus
H-C01Competition · MoatCategory-defining brand position in the vertical workflow segment; late entrants face a hard catch-up curve.MediumStrongProven
H-C04Financial · UEFour-year revenue CAGR of ~75% with ARPA up +80% — both renewal intent and pricing headroom are demonstrated.HighStrongProven
H-C05GTMThree growth vectors — vertical depth, geographic expansion, and a recently signed acquisition LOI — are tracking to plan.HighModerateIn validation
H-C06MarketThe team can launch adjacent products, aligned with an AI-native category vision, from the data assets they already own.HighWeakOpen

Risks & Challenges 10 hypotheses · 4 proven

H-IDAxisClaimImpactEvidenceStatus
H-R01Financial · UEEnterprise segment FY26 plan of $38M vs. signed pipeline of $15M leaves a $23M gap that is unlikely to close.HighStrongProven
H-R03TeamNo CTO successor named after the prior departure; owner of Series B use-of-proceeds remains unclear.HighStrongProven
H-R04Financial · UEEnterprise monthly churn of 5% (annualized 46%) — the path to the planned 1.5% / month is not yet validated.HighWeakIn validation

Red Flags 5 hypotheses · true means walk away

H-IDAxisClaimImpactEvidenceStatus
H-K01MarketIf pure-play SAM tops out at $1.0-2.5B, VC return requirements (5-10x) are unreachable — direct counter to H-C06.CriticalModerateIn validation
H-K03GTM47% loss rate and 25-27% bid win rate flag a GTM repeatability problem.CriticalStrongProven
H-K04Financial · UEFive large accounts have signaled FY26 termination — a sharp retention signal that conflicts with the customer reference call. Needs immediate clarification.CriticalUnknownIn validation

Illustrative excerpt. Names and figures are anonymized. The full app adds layer / axis / impact filters, a Kanban view, jump-to-source for every claim, and an AI side panel for hypothesis refinement.

04 — Who it's for

Investor diligence and operator discovery on the same runtime.

For investors

VC, CVC, and deal teams

  • Translate post-screen Full DD (Stage 1–5) into H-IDs and a ledger of evidence — without losing the trace of why you believed each claim
  • Bind dataroom materials, public research, and call notes as per-hypothesis evidence, with explicit source tiers
  • Generate IC memo drafts from your strongest claims and unresolved open questions — edit, don't write from scratch
  • Record Gate 5 as founder meeting · further DD · pass · term sheet
Request access
For operators

Founders and new-business teams

  • Decompose your business idea into "if this is true, it scales" propositions — the form an investor will eventually pressure-test
  • Attach customer interviews, prototype metrics, and competitive observations as tests of those propositions
  • Use the Critique stage to argue against your own assumptions before someone with a check does
  • Walk into exec reviews and board updates with the same story every week: here is what we're testing right now
Request access

05 — Built-in analysis

Configurable analysis packs, organized around how venture teams actually underwrite.

Hypogrid is designed to start with the analyses every deal needs, then layer in deeper work when the investment question demands it. Teams can tune axes, evidence standards, and memo outputs to their own fund strategy and IC norms.

Market conviction

Is this market worth winning?

Market workflows move from sizing and landscape basics into strategy-grade structure: profit pools, share shifts, category power, and where the company can build a durable wedge.

Basic

TAM / SAM / SOM, segment analysis, competitor mapping, market share, growth drivers.

More

Value-chain analysis, profit-pool mapping, Porter / substitute pressure, regulation, saturation risk.

Company quality

Can this company compound?

Company workflows test whether the business quality holds below the pitch: retention, GTM repeatability, product usage, unit economics, team risk, and the operating levers behind the plan.

Basic

ARR bridge, gross margin, CAC payback, churn, pipeline quality, founder-market fit.

More

Cohort economics, burn multiple, sales productivity, usage intensity, org dependency, roadmap risk.

Investment underwriting

Does the investment clear the bar?

Underwriting workflows convert diligence into a decision: valuation, ownership, dilution, exit paths, downside exposure, and the questions that will come up in IC.

Basic

Trading comps, transaction comps, cap table, ownership, exit scenarios, bull / base / bear cases.

More

MOIC / IRR sensitivity, dilution modeling, fund-return contribution, contradiction matrix, deal-killer analysis.

06 — Technology stack

Local LLMs, database APIs, and markdown-native diligence infrastructure.

Multi-agent orchestrator

A coordinator dispatches specialized workstreams in parallel, while reviewers keep control over scope, routing, and final judgment.

Queryable evidence graph

Hypotheses, evidence, contradictions, and decisions live in ledgers with stable IDs.

Hybrid model routing

Local LLM secures dataroom files, expert-call notes, investment theses. Frontier models are opt-in per workstream with pinned model + prompt + context budget. No silent context leakage, no surprise spend, no model lock-in.

Markdown-native canonical

Hypogrid Harness system generates views deterministically. Diff it, grep it, version it, fork it, move it. No proprietary DB, no vendor lock-in — the workspace itself is the source of truth.

Turn pattern-match into structure.

Hypogrid is opening private beta access for venture teams and company builders who want a more auditable way to reach conviction.

Request access Read the guide

Private beta

Request access for your next diligence workflow.

We are prioritizing venture investors, CVC teams, and operators running high-stakes company evaluation. Share a few details and we will follow up with the right onboarding path.

Private beta access is reviewed manually. No workspace is publicly exposed from this page.