Every AI company has great models. Almost none have solved memory. RecordorAI is the persistent memory layer that makes AI agents actually useful across days, sessions, and users.
Million-token context windows exist — but they're wiped at the end of each session. Paying for 1M tokens of context on every API call is cost-prohibitive for ongoing agentic workflows.
OpenAI and Anthropic are built for single conversations. An agent that needs to remember what a user decided three weeks ago, across a dozen sessions, is not a supported use case.
Every extra turn of conversation adds to the context window. At 10K MAU, naive API-based approaches run $75–180K/month in closed-provider fees — before you're even profitable.
Every memory API call to a closed provider means your users' conversation history leaves your infrastructure. Enterprise customers won't accept that. Regulated industries can't.
RecordorAI sits between your AI agents and their context — storing, compressing, and retrieving memory automatically. Your agents always have what they need.
5–10K tokens of active context in GPU VRAM. Instant recall within a session. Zero latency. Costs $0.003 per 1K tokens on RecordorAI hardware — 800x cheaper than GPT-5.4 for equivalent context density.
Full conversation history in pgvector + Supabase. Infinite retention, queried on demand. No per-token cost scaling with history length. Effectively free memory past the first retrieval.
Each entity (user, project, decision) summarized into ~500 tokens. Extractive compression — not generative. No hallucination risk. Costs almost nothing to store and retrieve.
RecordorAI's infrastructure hosts both its core inference business and a pipeline of vertical SaaS companies — built using A.L.I.C.E. and sold to larger players in each space.
RecordorAI memory layer serves 10K+ users via API and chat UI
$150K/mo MRR at 10K users. Infrastructure contract never sold.
Vertical SaaS products (CRM, AI assistants) built on RecordorAI memory
Each company sells to a larger player. RecordorAI retains infra contract.
10x inference throughput per watt. 1/10th cost per token vs Blackwell. Early infrastructure adopters gain a structural cost advantage that compounds.
Workstation-tier inference becomes real. RecordorAI's hybrid architecture (Mac Studio + Rubin GPU) is designed to exploit exactly this price/performance cliff.
GLM-5.1 and Qwen 3.6 now rank globally competitive on agentic coding benchmarks. Zero licensing cost. Full infrastructure control. The ecosystem is ready.
RecordorAI is building the memory layer for AI agents — a category that doesn't exist yet but will be foundational to every AI product built in the next decade.
If you make videos about AI tools, build AI products, or teach developers how to ship — RecordorAI is built for the kind of persistent, multi-session AI workflows that make great content and great products.
Build AI demos and tutorials that actually remember context across episodes. No more re-explaining your AI's setup in every video. Use RecordorAI to build agents that carry state across sessions — perfect for ongoing AI series.
Ship AI-powered tools and products without rebuilding memory from scratch every time. RecordorAI gives your audience a memory backend they can actually use in their own projects.
Build AI tutors and research assistants that remember each student's progress across weeks. RecordorAI's memory layer makes long-horizon AI education workflows tractable for the first time.
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