Interrogate your venture before the market kills it.
Six domain-specialized agents interrogate your business model, stress-test unit economics with 10,000 Monte Carlo runs, and cross-reference real-world merchant demand with startup failure forensics.
AI STUDY COMPANION
Resilience Score Breakdown
5 AUDITED PILLARSSandboxed Stochastic Unit Economics
10,000 RUNS // DETERMINISTIC PYTHON3Validation Priority // ranked by expected value of information (EVPI)
Empirical decision analysis over Monte Carlo draws: resolving each uncertain assumption is worth a measurable share of the decision value at stake.
[+] 6-Agent Swarm Deliberations & Risk Hypotheses 6 SPECIALIZED ROLES
[+] Epistemic Robustness Invariant Audit Trail AUDITED: 5 CLAIMS
Why traditional LLMs hallucinate viable startups.
Standard frontier models are RLHF-tuned for agreeable sycophancy. When you ask them about an idea, they flatter your assumptions instead of stress-testing fatal operational hazards.
Praises every feature request, assumes linear market adoption, and quotes generic TAM numbers without verifying procurement or statutory friction.
Spreadsheets calculate uniform payback periods using static assumptions, ignoring tail churn events, CAC inflation, and insolvent downside distributions.
Deterministic Python sandbox Monte Carlo, Razorpay merchant demand grounding, Loot Drop failure traps, and Epistemic DAG verification with anti-sycophancy caps.
Six specialized adversarial agents.
Each agent operates under a strict mathematical and operational charter, probing distinct failure vectors before reaching final consensus.
Establishes domain priors, identifies procurement cycles, and synthesizes crossfire arguments into actionable strategic verdicts.
Probes existential distribution risks, enterprise sales deadlocks, incumbent bundling traps, and uncompensated tail risks.
Analyzes latency, workflow integration, model inference costs, API dependency moats, and data residency constraints.
Queries Polymarket betting volumes, live news headlines, developer traction signals, and competitor displacement friction.
Matches propositions against real merchant problem catalogs (Razorpay Fix My Itch) and validates daily workflow switching friction.
Calibrates CAC payback bounds, gross margin thresholds, and verifies scores against isolated Python Monte Carlo output.
[+] DEVELOPER & MCP PROTOCOL INTEGRATION
Connect Dossier Adversarial Swarm to Claude Desktop, Cursor, & CLI
Expose the 6-agent swarm, isolated Python Monte Carlo runner, and epistemic verification engine over standard MCP stdio JSON-RPC.
View Stdio JSON Config
Connect Dossier Adversarial Swarm to Claude Desktop, Cursor, & CLI
Expose the 6-agent swarm, isolated Python Monte Carlo runner, and epistemic verification engine over standard MCP stdio JSON-RPC.
{
"mcpServers": {
"dossier": {
"command": "node",
"args": ["/home/sanjeev/projects/products/dossier/dist/mcp-server.js"]
}
}
}