Precognition Labs provides an AI‑augmented moderation platform that integrates with existing trust‑and‑safety pipelines to ingest and analyze text, images, video, and metadata, delivering real‑time action recommendations with calibrated confidence scores. The solution includes continuous reviewer performance monitoring, policy A/B testing, and audit‑ready logs, offered as a cloud SaaS with API‑first integration for B2C content platforms.
Funding
Funding not disclosed
Founders
Product
Problem
B2C platforms that host user‑generated content must continuously moderate posts, enforce community policies, and maintain trust‑and‑safety standards. Manual review processes are labor‑intensive, introduce latency, and often yield inconsistent decisions, especially at scale.
Solution
Precognition Labs delivers an AI‑augmented decision platform that embeds directly into a company’s existing moderation stack. The suite—PreCog Dash, Arthur, and Agatha—ingests cases from any source, enriches them with signals from internal databases and third‑party APIs, and applies proprietary machine‑learning models to generate actionable recommendations. Human reviewers receive real‑time confidence scores and suggested actions, allowing them to resolve cases in seconds while preserving judgment quality. Arthur continuously monitors reviewer performance, flags policy deviations, and surfaces root‑cause insights for coaching. Agatha runs controlled A/B experiments on rules, models, or workflow variants, automatically routing cases and reporting lift, cost, and risk metrics. The platform is delivered as a cloud service with API‑first integration, enabling rapid deployment and enterprise‑grade auditability.
Target Audience
Primary customers are B2C enterprises that operate social networks, marketplaces, or any platform with large volumes of user‑generated content and dedicated trust‑and‑safety teams.
Features
- AI‑driven case ingestion that parses text, images, video, and metadata, then maps them to policy attributes
- Real‑time recommendation engine that proposes actions (approve, reject, flag) with calibrated confidence scores
- Continuous quality scoring across reviewers, workflows, and content streams, with anomaly detection and automated coaching cues
- Policy A/B testing framework that routes cases to variant rule sets, captures lift, accuracy, cost, and risk, and visualizes results in a dashboard
- End‑to‑end encryption, role‑based access control, and immutable audit logs meeting SOC 2 and GDPR requirements
- API/SDK integration layer supporting REST, gRPC, and webhook callbacks for seamless embedding into existing moderation pipelines
- Auto‑learning loop that incorporates reviewer feedback to fine‑tune models without manual re‑training cycles
- One‑click deployment via containerized microservices, reducing time‑to‑value from months to minutes