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intrprt.it Agent-to-Agent Memory 1) Problem - Data silos: financial, macro, sentiment, news, dark data. - Ephemeral LLM outputs: no persistence, no reuse. - Wasted reasoning: same “inflation outlook” recomputed daily. - Agent gap: no qualitative time-series memory layer. 2) Solution - Persistent, time-indexed LLM columns = reusable memory units. - Agents in Coral: Ingestion builds/updates; Lookup serves. 3) Use Cases Finance: CPI “inflation outlook” column → instant reuse, no PDF crawl. Derivatives: OHLC + macro + sentiment → “recession probability” column. Market Research: forums + filings + reviews → sentiment trends. Breadth/Depth: merge 10 feeds into stress index; stack layers → cheaper each step. Pitch line: “Every new layer of insight gets cheaper the deeper you go.” 4) MVP - intrprt.it API: search, series.get, ingestion.run. - Stack: Supabase (Postgres JSONB, pg_cron), Edge Functions. - Tables: configs, ts_dtypes, logs. - Coral integration: ingestion + lookup agents. 5) Market - Alt-data $11.65B (2024) → $140B (2030). - Fin. data services $23.3B (2023) → $42.6B (2031). Gap: no open memory layer of LLM-derived qualitative streams. 6) Business - SaaS tiers: Basic / Pro / Institutional. - API pricing: per column or request. - Future: column marketplace, premium compute. 7) Roadmap Hackathon: 2 demo columns, Coral tools. Next: multi-source, weekly/monthly tables. Future: rollups, backfill, catalogs, marketplace. 8) Risks - Quality: schema validation, confidence scores. - Storage: derived outputs only. - Cost: budget caps, token accounting. - Complexity: strict JSON configs. 🔑 Hook “intrprt.it turns throwaway LLM answers into persistent, composable memory. Agents stop wasting tokens — every reasoning chain gets shorter, cheaper, and smarter.” 📊 Wins - Up to 95% cost savings at scale. - 0.5–1.8s faster per request (compounding). - >1000× lower energy per request.
21 Sep 2025