The next era of intelligence needs a memory.

A world of agents needs a system of memory.

AI is moving from answering questions to doing work. An agent returns to a codebase. A support workflow encounters the same customer. A robot operates at the same site. Each creates experience worth carrying forward.

Our thesis: persistent memory becomes a core infrastructure layer for these machines. The opportunity is to make that memory useful, inspectable, and accountable as deployments grow.

A memory system that measures its own value.

UniMem combines lessons, time-aware facts, a knowledge graph, conversations, memory blocks, and procedures. Its randomized evidence engine compares outcomes with and without eligible memories, with an integrity audit before an effect is reported.

Teams can use that evidence to withdraw harmful guidance, review changes, and decide what deserves to stay. This connects retrieval to a question that matters to the business: did the memory help?

Real software. Inspectable evidence.

The public product includes a local Python core, CLI, MCP server, HTTP gateway, and a multi-tenant Hub. It also includes a ROS 2 bridge and physical-AI interfaces, whose maturity and integration requirements are documented in the source.

Published LoCoMo measurements report 95.8% gold-evidence delivery and 152 ms median served recall latency on a shared 16-core CPU host. These are scoped benchmark results, not promises about every deployment.

• Run the offline demo to inspect the memory, measurement, and governance loop.
• Review the benchmark methodology, baselines, sample sizes, and reproduction commands.
• Inspect the project status table for stable, developing, and experimental components.

Start with builders. Grow with organizations.

The initial wedge is teams running recurring coding and support workflows. An open local core makes evaluation accessible. Organization-level deployment creates the commercial opportunity: shared memory, identity, governance, audit, and operational controls.

The commercial strategy centers on managed or self-hosted Hub capabilities and commercial licensing. Outcome-linked pricing and shared evidence are longer-term opportunities to validate, rather than assumptions built into the business case.

Build conviction one deployment at a time.

We are seeking funding and design partners to turn the product into infrastructure teams depend on. The next phase should be judged by measurable milestones:

• Validate recurring coding and support workflows with design partners and agreed outcome measures.
• Make onboarding, deployment, and day-to-day operation reliable enough for a team to own.
• Establish repeatable evidence of utility and a clear path from evaluation to paid adoption.
• Expand toward multi-agent organizations and physical AI as those deployments validate the need.

The hard questions belong in the conversation.

Memory needs reliable outcome signals, and every experiment depends on its assumptions. Competition is strong. The product surface is broad, and the near-term commercial focus needs to stay narrow.

Physical-AI integrations require hardware-specific evaluation. Cross-deployment evidence and commercial models need validation. We welcome investors who want to examine the architecture, the limits, and the milestones alongside the opportunity.

Help build what intelligence remembers.

If you invest in developer tools, AI infrastructure, or intelligent machines, let’s talk. We can walk through the product, the evidence, and the work required to take UniMem to its next stage.