Adversarial Intelligence Platform
Prism pits two AI agents against each other in structured debate to separate genuine intelligence from noise. Read the arguments. See the scores. Judge for yourself.
Existing intelligence platforms score signals behind closed doors. One model, one opinion, no transparency. When it's wrong, you have no way to know why.
Feedly, Recorded Future, and Silobreaker assign scores you can't inspect or challenge. The reasoning is hidden, the calibration unverifiable, the confidence inflated.
One LLM assessing significance is prone to over-confidence and pattern-matching artifacts. Research shows homogeneous agents provide minimal improvement over a single model.
Threat intelligence platforms start at $50K/year. Mid-market teams doing competitive intel, regulatory monitoring, or market analysis are priced out entirely.
Most signal intelligence tools are cybersecurity-only. Teams tracking regulatory shifts, competitive moves, market trends, or emerging technologies have no purpose-built solution.
Prism takes a different approach. Instead of a black-box score, every signal passes through a structured adversarial debate between heterogeneous AI agents. An Advocate argues for significance. A Challenger argues against. An independent Judge evaluates the arguments and produces calibrated scores. You see every word.
Source adapters continuously scan NewsAPI.ai, GDELT, RSS feeds, Hacker News, arXiv, SEC EDGAR, Reddit, and more. Content flows through deduplication (MinHash + exact match), full-text extraction via Playwright, and embedding generation for semantic matching.
10+ source typesA lightweight LLM call scores topic relevance 0–1. Signals below 0.4 are discarded, eliminating ~70% of noise before expensive debate begins. This keeps per-topic costs manageable at scale.
$0.002/signal · 70% noise removedSurviving signals enter a structured 3-round debate. The Advocate (high temperature, creative reasoning) argues for significance. The Challenger (low temperature, skeptical) argues against. Each receives an evidence packet: full article text, related signals, and background context. Early termination triggers when the outcome is unambiguous.
3 agents · 2 rounds + judgmentAn independent Judge agent evaluates argument quality across both sides and produces six calibrated scores: relevance, importance, risk, urgency, confidence, and a computed priority. The consensus label (agree significant, agree insignificant, contested, etc.) captures the debate outcome. Full transcripts are preserved.
$0.077/debated signalThe accelerated timeline materially impacts compliance readiness. Article 6 high-risk system providers now face a February 2026 deadline instead of August. Companies that planned implementation around the original schedule are looking at a 40% compression of their compliance runway. For any organization deploying AI in regulated domains—healthcare, finance, hiring—this shifts from a strategic planning item to an operational emergency.
The acceleration only affects the high-risk classification deadline, not the full compliance framework. Most enterprise AI deployments fall under limited-risk or minimal-risk tiers, which remain on the original timeline. The companies genuinely affected—those with high-risk systems already deployed—have been tracking this since the trilogue. This is a known adjustment, not a surprise. Media framing amplifies urgency beyond what the regulatory text supports.
The Advocate establishes legitimate impact for the high-risk subset, but the Challenger correctly narrows the affected population. The acceleration is real but applies to a specific tier. Scoring reflects moderate-high importance for affected organizations, lower urgency for the broader market. Consensus: contested—significance depends on risk classification of the reader's AI deployments.
Every signal Prism surfaces includes calibrated scores, a consensus label, and the strongest arguments from each side of the debate.
Three heterogeneous AI agents (Advocate, Challenger, Judge) with distinct personas, temperatures, and reasoning styles. Based on ICLR 2025 research showing heterogeneous debate outperforms homogeneous or single-model approaches.
10+ source adapters: NewsAPI.ai, GDELT, RSS, Hacker News, arXiv, SEC filings, Reddit, Semantic Scholar, and more. Automatic deduplication, full-text extraction, and embedding generation.
Automatically identifies companies, people, technologies, and organizations mentioned in signals. Watch individual entities, build competitive sets, and configure threshold-based alerts for sentiment shifts.
PostgreSQL full-text search for keyword queries, pgvector HNSW indexes for semantic similarity. Find signals by meaning, not just string matching. Combined search returns ranked results across both methods.
Groups related signals into developing stories based on embedding similarity. Track how narratives evolve, identify convergent reporting from independent sources, and spot emerging patterns before they trend.
AI-generated daily briefings synthesize top signals per topic into structured intelligence reports. Weekly PDF digests with trend analysis. Configurable email delivery with per-entity and per-group alert thresholds.
Define monitoring topics with keywords, descriptions, and intent. 12 pre-built templates spanning AI policy, cybersecurity threats, competitive intelligence, regulatory changes, market trends, and emerging technology.
Full debate transcripts preserved for every signal. Read the Advocate's case, the Challenger's rebuttal, and the Judge's evaluation. Understand exactly why a signal scored the way it did.
Build competitive sets, thematic groups, and personal watchlists. Head-to-head leaderboards ranked by signal volume, sentiment, or priority. Aggregate timeline charts for group-level trend analysis.
Prism isn't cybersecurity-only. Any team that tracks external signals—competitive, regulatory, market, technology—can configure topics and start monitoring.
Competitive Intel
Build entity groups for competitive sets. Monitor product launches, leadership changes, partnership announcements, and funding rounds with head-to-head sentiment tracking and priority scoring.
Risk & Compliance
Monitor regulatory bodies, policy changes, and enforcement actions across jurisdictions. Adversarial debate stress-tests whether a regulatory signal is genuinely impactful or media-amplified noise.
Strategy & Research
Track emerging technologies, market shifts, and academic research. Signal clustering reveals developing narratives across independent sources. Briefings synthesize daily intelligence into actionable summaries.
Security Teams
Monitor vulnerability disclosures, threat actor activity, and attack technique developments. Priority scoring and urgency calibration help triage what demands immediate response versus awareness.
Product Teams
Track user sentiment, feature requests from public forums, competitor product launches, and technology adoption trends. Entity tracking on key technologies surfaces relevant signals automatically.
Executives
PDF briefings and email digests deliver prioritized intelligence without dashboard monitoring. Read the debate summary to understand why a signal matters. Forward to the team with context already built in.