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🤝 Top Collaborators
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🤓 Latest Submissions
Venture Foundry
Venture Foundry solves a real problem with AI research tools: you can't tell what's true. Ask a chatbot for a market size and you get a number with no source, no method, no date. Venture Foundry fixes this structurally — every module follows Gather → Extract → Synthesize. A live Bright Data SERP search retrieves real results, gpt-4o-mini converts them into structured facts, and only then does gpt-4o reason over those facts. The synthesis model never sees raw web content, only verified evidence — and every claim links back to its source. Four connected modules — Ideation, Market, Cofounder, and Capital — write into one shared startup profile, so by the time a founder reaches investor matching, the system already knows their domain and market positioning. Two details make this more than a search wrapper: Cofounder matching is two-stage and agentic. A plain search only returns articles about cofounder matching, not people. Venture Foundry uses SERP to find LinkedIn profile URLs, then Bright Data's Web Scraper API to turn them into real person records — capped and cached to stay fast. The competitor map reasons per-market. Rather than fixed axes, the model reads every competitor's description and derives the two questions that actually differentiate that specific market, then places the idea and flags the emptiest quadrant as an open question, not a guaranteed opportunity. Security is enforced at the database layer: all six Supabase tables have RLS with zero policies, so the browser's anon key can read and write nothing — verified directly. All real work runs through Edge Functions with the service role, keeping API keys off the client. If Bright Data can't return evidence, the module fails loudly instead of quietly answering from memory. Built on Vite/React 19/TypeScript, Supabase Edge Functions, Bright Data, and the AI/ML API — Venture Foundry shows that AI research can mean every claim is checkable before you rely on it.
10 Aug 2026
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IncidentIQ
IncidentIQ is a multi-agent incident response platform designed to help engineering teams manage production outages faster and more efficiently. During critical incidents, engineers must simultaneously investigate root causes, communicate with stakeholders, update customers, coordinate teams, and document everything that happened. This creates delays, communication gaps, and operational chaos at the exact moment when speed matters most. IncidentIQ addresses this challenge through a team of specialized AI agents coordinated using Band AI. When an incident alert is received, the Orchestrator Agent activates the workflow. The Triage Agent analyzes the alert, classifies severity, estimates business impact, and identifies affected systems. The Diagnosis Agent investigates logs and deployment history to determine the most likely root cause and recommend corrective actions. A Validator Agent reviews the diagnosis, challenges weak assumptions, and improves confidence before decisions are accepted. Once the incident is understood, the Comms Agent automatically generates internal engineering updates and external customer-facing status messages. At the same time, the Postmortem Agent continuously documents the incident and generates a structured Root Cause Analysis report containing timelines, impact assessments, contributing factors, resolutions, and action items. Band AI acts as the collaboration layer, enabling agents to communicate, share context, and coordinate actions in real time. Instead of isolated AI workflows, IncidentIQ creates a collaborative system where multiple agents work together toward a common objective. By accelerating diagnosis, automating communication, and generating documentation automatically, IncidentIQ reduces operational overhead and allows engineers to focus on resolving incidents. Our vision is to transform incident response from a reactive process into an intelligent, coordinated, and AI-powered operational workflow.
19 Jun 2026
