Recallr AI Inc.
January 2025 - Present
San Francisco, CA, USA — On-site
Founder and CTO
- Built Recallr, a persistent, queryable long-term memory layer for conversational AI systems that retains facts, preferences, relationships, and decisions across multiple conversations while preserving the source of each memory. Architected its ingestion pipeline, evolving knowledge graph, semantic retrieval, temporal reasoning, knowledge-update handling, and configurable merge and conflict-resolution rules.
- Built and ran an LLM evaluation and benchmarking pipeline for Recallr on LongMemEval, achieving 97.5% overall accuracy, including 97.0% temporal-reasoning accuracy and 97.4% knowledge-update accuracy. Optimized inference and retrieval performance to P95 latency of 408 ms for Low-Latency recall, 1.575 seconds for Balanced recall, and 8.619 seconds for Agentic recall.
- Evolved Recallr into the intelligence and memory layer for private capital, transforming fragmented deal memos, data rooms, diligence, meeting transcripts, partner notes, filings, returns, and investment-committee history into a continuously updated, queryable decision graph that preserves how a firm's investment judgment evolves over time. Generated over $300,000 in revenue across eight institutional pilots and raised $200,000 in angel investment.
