Human-readable canonical work
DD notes, hypothesis briefs, IC memo drafts, and review outputs stay readable to humans and writable by agents.
Technology Stack Local-first runtime for agent-assisted validation
Infrastructure for AI workflows that need evidence, control, and auditability.
HypoGrid is built around a local trust boundary, a Markdown-native workspace, configurable diligence packs, specialist agent orchestration, and a ledger that keeps every claim traceable.
Stack Overview
Four layers, one validation system.
Each layer protects a different part of the workflow: source ownership, repeatability, tool integration, agent execution, and decision history.
01
Local Forest Trust Boundary
Keep sensitive source material local while making it addressable to workflows.
02
Configurable Workflow
Turn firm-specific diligence taste into repeatable gates and analysis packs.
03
Multi-Agent Orchestrator
Coordinate specialist agents without losing scope, review, or handoff discipline.
04
Ledger Layer
Make hypotheses, evidence, contradictions, and decisions queryable over time.
Architecture Primitives
The product is built around durable work artifacts, not a chat thread.
PRD-level technical choices show up as primitives that make the workspace portable, inspectable, and extensible.
Local files without forced upload
HypoGrid can remember paths, metadata, projections, and state while source materials remain in the local folder.
Schema + prompt + template + renderer
Startup DD, commercial DD, financial DD, founder DD, and expert-call workflows can ship as executable packs.
Claude Code, Codex, Cursor, Notion
External tools can feed observations and research back into the same hypothesis, evidence, and decision loop.
Local-first, cloud-ready when needed
Teams can start as an open local workbench, then move selected workflows to web or API-based model routing.
01 — Local Forest Trust Boundary
Work from the folder without surrendering the folder.
Dataroom exports, call notes, PDFs, spreadsheets, and intermediate analysis can remain in the user's file system. HypoGrid stores the operational layer around those files: paths, metadata, projections, embeddings, workflow state, and review history.
- Controls
- Which files, folders, models, paths, and workstreams are allowed into each analysis run.
- Protects
- Source ownership, sensitive materials, and the boundary between local and frontier model usage.
- Enables
- Fast AI-assisted review without forcing proprietary cloud storage as the system of record.
- Stores
- Local SQLite can track file paths, metadata, projections, embeddings, and workflow state around the folder.
02 — Configurable Workflow
Encode the team's judgment without freezing the process.
HypoGrid is meant for teams whose diligence style is a competitive advantage. Workflows can reflect firm-specific evidence standards, conviction thresholds, source tiers, memo structure, and route-back logic.
- Templates
- Startup DD, commercial DD, expert interviews, market scans, pricing tests, and red-team critique.
- Pack format
- Each diligence pack can include schemas, prompts, templates, workflows, renderers, and review gates.
- Customization
- Analysis preferences, decision gates, required sources, output formats, and escalation rules.
- Learning loop
- Prior diligence folders can become examples that sharpen future workflows instead of disappearing as one-off work.
03 — Multi-Agent Orchestrator
Run specialist agents in parallel, then force structured handoffs.
HypoGrid coordinates agents across market work, customer evidence, competitive analysis, financial checks, expert interview synthesis, and critique loops. The goal is not a single magical assistant; it is a controlled division of analytical labor.
- Specialization
- Each agent owns a bounded workstream with explicit inputs, outputs, prompts, and review expectations.
- Model routing
- Local LLMs, wrapped coding agents, and frontier models can be selected per sensitivity and workstream.
- Human gates
- Material transitions can pause for approve, route back, defer, reject, or ask for more evidence.
- Handoffs
- Outputs land as claims, evidence, contradictions, open issues, and proposed patches, not loose prose.
04 — Ledger Layer
Keep the reasoning record stable even as views change.
The ledger is the canonical record underneath the matrix, briefs, timelines, and memo drafts. Hypotheses, evidence, contradictions, open issues, and decisions keep stable IDs so the team can inspect how a judgment was formed.
- Stable IDs
- Claims and decisions remain addressable as evidence changes and outputs regenerate.
- Generated views
- The matrix, memo, brief, and timeline render from the same ledger rather than diverging into separate files.
- Auditability
- The workspace can be diffed, reviewed, versioned, forked, and moved without losing the reasoning trail.
- Memory
- Past hypotheses, red flags, decision reasons, and validation patterns become reusable diligence memory.
Roadmap
From local DD execution to a hypothesis-led diligence OS.
The roadmap starts with a personal local execution environment, then expands into a shared system for repeatable validation across investors and builders.
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01
First Step: Personal Due Diligence Environment
Open, local-first workflows for individuals who want to run diligence from their own folders, models, and toolchain.
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02
Second Step: Local-First Due Diligence Workbench
Abstract repeated diligence work into reusable packs, ledgers, review gates, and agent-assisted outputs.
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03
Third Step: Around the Hypothesis Validation OS for Every Business Visualization
Extend the same hypothesis-led system beyond venture diligence into business validation, visualization, and decision workflows.
Operating Principle
Use AI aggressively. Keep judgment explicit.
The stack is designed to compress the repetitive parts of validation while preserving what matters most: source boundaries, review gates, decision criteria, and a record the team can defend later.