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Resonance: a feedback-trained memory and language system with no pretrained model inside and no training corpus behind it. It boots empty and acquires every capability from graded expert feedback. Because knowledge lives in an explicit store rather than in weights, every belief it holds is grounded, auditable, and correctable — a wrong fact is fixed by one graded correction, not a retraining cycle. The architecture is fully built, deployed, and hardened; the research page carries the measurements.

Today's AI fails on the two things knowledge work demands: trust and unit economics. A model that confidently states something false cannot be corrected without retraining — for serious knowledge work, that is disqualifying. And the standard AI business pays per token to a foundation model it does not control, with the price of its core input set by someone else.

Resonance answers both structurally. Our wedge is high-trust knowledge work — compliance, legal and regulatory reference, clinical knowledge, safety-critical documentation — buyers who are currently disqualified from using LLMs seriously, not underserved by them. The AI knowledge-management market these buyers sit in is estimated at $11–18B in 2026, growing toward $50B+ by 2030. At our unit economics, a single vertical taught well is a company; breadth is upside, not the plan.

Answering is a lookup, not a generation-scale computation, so Resonance's economics follow server hosting, not token billing. On pessimistic assumptions, infrastructure costs about $0.0022 per user per month — roughly 96,000 users on a $214/month redundant box pair, no GPUs. That funds a pricing posture rivals cannot copy: a genuinely free tier that costs us fractions of a cent, and a $10/month paid tier priced below the $20 convention yet profitable from the first user.

A data flywheel on data the public internet does not contain: graded expert corrections. The on-disk state IS the model — every confirmed fact, relation, and correction, attributed in a permanent ledger, impossible to reconstruct from public data. The board-of-experts curriculum and the expert relationships behind it don't transfer to a competitor, and two instances taught by different tutors are non-interchangeable assets. There is no foundation model here to be a wrapper of.

A $750K seed round. The architecture is already built, so technical risk and business risk point the same way: both are answered by teaching it well. The round funds 18–24 months of teaching — the board of experts and curriculum (40%), owned training infrastructure (25%), engineering and product (20%), and safety, measurement, and operations (15%) — to one fully-taught vertical with a published learning curve. That evidence prices the next round. Email us for the brief and a call.

We name them. One variable decides the company: whether serial feedback reaches frontier breadth in feasible time — corrections-per-unit-knowledge, measured continuously from the first day of teaching via a built-in held-out probe. Behind it sit encoder generalization, catastrophic interference, and parallel multi-tutor teaching, each with a measurement plan rather than a claim. The same candor applies to safety: correctability cuts both ways, so a value floor, corroboration, and abuse controls on the teaching endpoint are funded in this round, not deferred. The full list is on the research page.

Every answer traces to specific taught facts and confirmed relations — provenance is structural, not retrofitted. The entire belief state is human-readable and every training event carries tutor attribution, so the system's knowledge can be audited, not just its outputs. And it cannot hallucinate what it was never taught: outside its taught domain the worst case is a weak answer, never a confident fabrication.

Yes — that is its native shape. Resonance is self-hosted, runs on ordinary CPU hardware with no GPUs, and has no third-party model dependency. It is air-gappable, and the instance an organization teaches contains only what was put in it and never leaves the building. No frontier-model vendor can offer this shape at all.

That is Tessera, our applied practice: compliance-native AI systems integration, available now as subcontract and teaming engagements. Policy enforcement, output validation, injection mitigation, and full audit logging built into the orchestration layer over the models and systems you already operate — aligned to MOSA, the NIST AI RMF, and the FY2026 NDAA's AI security direction. Details on the Tessera page.

We're structured to team: a small, senior technical group built to execute defined AI scope inside a prime's existing contract vehicle and delivery schedule. SAM.gov registration is underway (UEI and CAGE pending), and internal practices are being aligned to NIST SP 800-171 with a CMMC Level 2 readiness roadmap. Michael Musilli leads contracting and teaming on our side. The right first step is an email introducing your office and interest.