SyntheticLab — Autonomous Adversarial Buyer Simulation

Stress-test SaaS pricing against 100 adversarial buyers powered by NVIDIA Nemotron, live Tavily competitor intel, and automated Nebius Cloud MicroVM technical verification sandboxes.

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Inspiration

Every founder, product manager, and builder has faced this painful nightmare: you spend 6 months building a product, set what you think is a fair price, launch... and hear deafening silence.

Customer discovery calls take months to schedule. Worse, asking generic LLMs "Would you buy this?" produces sycophantic, polite hallucinations: normal AI models always praise your idea because they have no budget limits, no board of directors, and no fear of getting fired.

I built SyntheticLab for the Nebius × NVIDIA Global AI Hackathon to turn this paradigm on its head: an autonomous adversarial procurement arena where tough, budget-constrained AI buyers attack your pricing, terms, and technical claims before you launch.

What It Does

SyntheticLab simulates an adversarial buyer committee that stress-tests any product pitch or URL:

  1. Heterogeneous Buyer Swarms: Concurrently spins up 10 to 100 autonomous decision-makers (Enterprise CFO, Staff Infrastructure SRE, Director of SecOps, Bootstrapped SMB Founder) with conflicting budget ceilings and risk appetites.

  2. Deep Market Grounding (Tavily AI Search): Objections are anchored in live market reality across Reddit, G2, and competitor pricing pages, exposing hidden overage traps and vendor lock-in.

  3. Autonomous MicroVM Proof-of-Concept (Nebius Sandboxes): When a technical buyer demands proof of speed or safety, SyntheticLab boots an ephemeral Nebius MicroVM sandbox to run real benchmarks (e.g., 3.2ms p99 latency), sealing the result with a cryptographic SHA-256 receipt.

  4. Live Negotiation & Economic Auditing: Founders spar live with skeptical buyers. The buyer enforces a deterministic economic rule:

Realized Value = Gross Benefit × Confidence

Net Gain = Realized Value - Contract Price > 0

Marketing buzzwords are pushed back, but hard spend caps and SLA guarantees flip rejection votes into adoption.

  1. Anti-Circular Hold-Out Validation: To avoid "grading my own homework", NVIDIA Nemotron 3 Ultra rewrites the pitch, which is then retested against a blinded hold-out panel (Cohort B) that never saw the initial debate.

How I Built It

Inference & Strategy via Nebius Token Factory:

NVIDIA Nemotron 3 Ultra (550B): Used for deep strategic arbitration, contractual commitment formulation, and pitch optimization.

NVIDIA Nemotron 3 Super (120B): Drives high-throughput, structured persona evaluations and parallel debate turns.

NVIDIA Nemotron 3 Nano (30B): High-speed Monte Carlo sweeping.

Epistemic Market Anchor via Tavily Search Engine:

Leveraged @tavily/core with searchDepth: 'advanced' and includeAnswer: 'advanced' to synthesize live consensus on competitor pricing traps.

Deep URL extraction parses live product pages and SPA pricing tables into structured market models.

Hardware-Isolated Technical Due Diligence via Nebius Sandboxes:

Ephemeral MicroVMs (cgroup-v2 isolation) execute automated stress benchmarks and CVE vulnerability audits with exit code 0 verification.

Frontend Craftsmanship:

Built with Next.js App Router, TypeScript, Tailwind CSS, and Lucide Icons featuring Apple/Linear-inspired sleek dark mode, interactive Swarm Constellation Maps, and a live SSE streaming pipeline.

Challenges I Ran Into

Eliminating AI Sycophancy: Out-of-the-box LLMs want to be helpful and agreeable. I engineered strict adversarial persona prompts and programmatic blocker checklists where an ADOPT vote is mathematically locked until every fatal constraint is resolved with binding terms.

Avoiding Circular Holdout Grading: A common pitfall in AI benchmarks is retesting on the same agents that generated the critique. I engineered a blinded Hold-Out Cohort B generator with mirrored roles but distinct organizations and zero memory of Cohort A.

Handling SPA Pricing Pages: Modern SaaS pricing pages heavily rely on client-side JavaScript. Combining Tavily's deep extraction with fallback scrapers allowed me to extract authentic pricing even across gated sites.

Accomplishments That I'm Proud Of

Empirical Accuracy: Validated against real-world historic cases (such as the Unity Runtime Install Fee backlash), capturing 83% of the exact fatal objections raised by the engineering community.

100% Robustness: Passes all 38 automated architectural unit tests and 15 live adversarial attack cases (prompt injection, authority bluffs, and empty promises).

Sub-Second Streaming Swarms: Streaming real-time evaluations across 100 autonomous buyers with live token telemetry on Nebius Token Factory.

What I Learned

Multi-model tiering (Nemotron 550B Ultra for strategy + 120B Super for swarms) delivers a massive improvement in cost-efficiency and latency without sacrificing reasoning depth.

Real-time search grounding with Tavily transforms LLMs from creative fiction writers into ruthless, data-backed enterprise procurement officers.

What's Next for SyntheticLab

Automated CRM & Stripe Sync: Connecting real Stripe checkout and CRM churn data to continuously calibrate persona risk tolerances.

Multi-Persona Voice Del

Built With

  • adversarial-simulation
  • artificial-intelligence
  • lucide-react
  • microvms
  • multi-agent-systems
  • nebius-token-factory
  • next.js
  • node.js
  • nvidia-nemotron
  • react
  • sandboxes
  • tailwindcss
  • tavily-ai
  • typescript
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