Your customer relationships, in high fidelity.
Every interaction with your customer should build on the last one. But AI forgets. Every time they come back, the relationship starts over from nothing. We fixed that. RCI is the memory layer that lets your AI hold a customer, a patient, a client, a case — accurately, across every interaction, for as long as the relationship lasts.
AI forgets. Every time.
That's why you've probably tried it once, found it useful for a day, and quietly stopped.
For the relationships that accumulate.
RCI exists wherever the same customer comes back. Over and over. For months, then years. Where what happened last time matters, and what's changed matters more.
Businesses with continuous customer relationships.
AI products that need persistent customer memory. Service practices where every visit builds on the last — therapists, coaches, chiropractors, financial advisors, attorneys. Software platforms where accounts outlive sessions. Anywhere a customer keeps coming back, and the business needs to actually remember what happened.
A picture of each customer. Built once. Tended continuously.
RCI doesn't index your conversations. It synthesizes them into a structured, evolving picture of every customer in your system — and keeps that picture current as your relationship with them develops, whether the updates come from conversation, interaction, or automation.
It remembers.
RCI builds a compact picture of each customer in your system. Not a transcript. Not a search index. A structured, evolving likeness of who they are and what you've been to each other.
It refines.
Every interaction sharpens the picture. New facts merge in. Old ones get corrected. Active context stays foreground; older detail moves into a tiered archive that never deletes, where it stays retrievable but stops competing for attention.
It scales without scaling the bill.
The picture deepens over time — more texture, more detail — but the cost of reading from it stays flat. A customer at month one and the same customer at year five look like the same line on your invoice.
Most AI memory is lo-fi. High fidelity is a different architecture.
Most AI today remembers by retrieval — pulling fragments of the past whenever a customer returns. It's fast, it's cheap, and it degrades over time. Recent research measures the gap: retrieval-based systems lag human memory by 56% overall, and by 73% on temporal reasoning alone. Not because the retrieval is broken, but because fragments were never designed to reassemble a person. RCI is built differently — as a continuously-refined picture, not a fragmented search.
How this chart was drawn, and what the RCI measurement means.
The RCI measurement at day 40 reflects production data from Tomoni, a consumer product built on RCI. Over a 40-day continuous window using claude-sonnet-4-5 with prompt caching enabled, per-inquiry token cost has remained flat at approximately $0.029 per interaction. Input tokens average 16,850 per call, of which 9,164 hit the Anthropic prompt cache (billed at 10% of standard input). Output tokens average 220.
The coherence curves for RAG and context-stuffing reflect published long-term memory research: the LoCoMo benchmark of conversational agents across 35 sessions, Chroma's work on context rot, and Databricks research on context-length degradation. The shape of the RAG curve is qualitative but not speculative — temporal blindness and context pollution are measured failure modes.
RCI is built on the architectural claim that a synthesized memory preservation mechanism can hold a person in a way fragmentary retrieval cannot. As more RCI deployments reach production, measured data points will replace projected ones.
Three phases. One partnership.
RCI isn't a SaaS signup. It's a substrate your product will sit on, and getting the shape of it right for your business is the whole job. We work with a small number of partners at a time.
Discovery.
We meet your team, your data, and the relationship your product is trying to keep with its customers. We map what RCI needs to hold, where the current system is losing fidelity, and what a working substrate would look like for you specifically. Scoped engagement. Fixed deliverable.
Build.
We build the substrate. Your interface stays yours — RCI sits underneath it, invisible to your users, producing a memory that your existing AI stack reads from. We work alongside your engineering team through integration and the first customer cohort in production.
Retain.
The substrate doesn't drift, but your product will evolve. We stay engaged as a memory-infrastructure partner — tuning, extending, and bringing new capabilities as they mature. You own the relationship with your customers. We keep the substrate fresh.
Running in production. Today.
RCI is the memory substrate powering Tomoni, our consumer product. It has been running continuously in production for over forty days, with the numbers to show for it.
Stable cost, unmoved.
Forty days of continuous production use. Per-interaction cost has not moved. Cache hit rate above 54 percent on every interaction after the first. Dashboards and architecture notes available to qualified partners after a briefing.
Architecture, patent, provenance.
RCI is patent-pending and architecturally distinct from RAG, context-stuffing, and conventional memory layers. Built by the team behind Tomoni, now deployed as licensable infrastructure for partners whose business depends on remembering.
A briefing is the fastest way to find out if RCI is for you.
Thirty minutes. No pitch deck. We show you the substrate, you show us your relationship problem, and we both decide whether there's something to build together.
Request a briefing