Products — Wave Based Semantic Memory ψ-stack
Explore the ψ-stack modules: EchoThesis, ResonanceDB, SenseMesh and ReasoningCore. A unified Wave Based Semantic Memory architecture for high-precision retrieval and accountable reasoning.
ψ-stack overview
Wave Based Semantic Memory
Phase-aware AI infrastructure
Products
EvaCortex Lab builds a ψ-stack for systems where meaning must remain stable under negation, exceptions and complex structure. Start with resonance-based retrieval, extend with wave–graph reasoning, and orchestrate trace-first agents on top of a phase-aware semantic core.
Wave Based Semantic Memory defines the paradigm on which the ψ-stack is built. ResonanceDB handles semantic storage and retrieval, EchoThesis projects meaning into a phase-aware representation, and SenseMesh with ReasoningCore enables graph-level and multi-step reasoning on top. Adopt a single component or the entire stack — the system remains compatible with what you already run.
How the ψ-stack fits your architecture
The ψ-stack introduces a different class of representation. Meaning is not treated as a single point in space, but as a phase-structured signal. Each module occupies a clear position in this architecture — from encoding, to memory, to wave–graph reasoning, to trace-first orchestration.
ResonanceDB — wave-semantic memory
Store ψ-patterns and retrieve them by resonance, not distance. Let constructive and destructive interference govern relevance, keeping negation, exceptions and counter-signal visible during retrieval.
Role: phase-aware retrieval and semantic stability.
EchoThesis — ψ-codec
Encode text into ψ-patterns ψ(x) = A(x) · e iφ(x) . Make salience, modality and relational orientation explicit, so semantic edits behave predictably instead of collapsing into averaged similarity.
Role: project language into phase-structured semantic signals.
SenseMesh — evidence-first RAG
Combine wave semantic memory with knowledge graph retrieval and evidence assembly. Retrieve evidence that preserves meaning-critical distinctions, connect it through entities and relationships, and build grounded context for LLM agents and agentic RAG workflows.
Role: evidence-first retrieval and reasoning for high-error-cost domains.
ReasoningCore — trace-first orchestration
Execute multi-step reasoning as explicit traces. Keep plans, dependencies, evaluations and checkpoints first-class — so agent workflows remain inspectable, reversible and governable.
Role: controlled, auditable reasoning over wave memory.
Modules at a glance
Adopt one module or deploy the full ψ-stack. Each product page covers the core model, typical architectures, deployment patterns, and what changes when semantics must stay precise under negation, policy caveats, and counter-evidence.
ResonanceDB
Retrieve by resonance, not cosine. Let interference decide relevance. Keep counter-evidence visible instead of averaged away. Build a memory tier that remains repeatable, explainable, and reliable under pressure.
Best for: high-stakes search, policy retrieval, safety, risk & compliance.
EchoThesis
Project language into amplitude–phase space. Make salience, modality and opposition explicit. Keep meaning stable under negation, paraphrase, and policy-style caveats — the cases where flat proximity blurs intent.
Best for: retrieval pipelines where “small edits” cause big semantic drift.
SenseMesh
Evidence-first RAG for complex reasoning systems. Combine wave semantic memory with knowledge graph retrieval. Retrieve evidence, connect it through graph structure, and assemble grounded context for LLM agents.
Best for: high-error-cost domains where semantic precision, provenance and grounding matter.
ReasoningCore
Orchestrate reasoning as a trace. Execute steps in parallel where possible. Enforce dependencies. Evaluate continuously. Roll back safely when constraints are violated — and keep every decision auditable.
Best for: long-running, multi-tool agent workflows that must stay governable.
Where teams start
Start from a concrete pain point — the moment your current retrieval or agent stack stops behaving predictably. Each entry point gives you a fast, measurable upgrade path.
Your RAG fails on negation, exceptions, and policy language
Add ResonanceDB and EchoThesis to make opposition and caveats first-class. Retrieve by resonance so “do not”, “unless”, and “except” stop collapsing into the same neighborhood as their opposites.
Outcome: higher precision on semantic-hard queries where correctness depends on structure.
You have graphs — but answers still lack provenance and structure
Deploy SenseMesh to turn knowledge graphs into evidence-first RAG context instead of dumping chunks into an LLM. Combine wave semantic retrieval with graph navigation, and keep the evidence trail explicit.
Outcome: grounded, explainable RAG with traceable evidence paths.
Your agents are powerful — but not governable
Introduce ReasoningCore to run multi-step workflows as traces. Make plans and dependencies explicit, enforce evaluation gates, and keep checkpoints so you can audit, steer, and roll back.
Outcome: controlled autonomy with trace-first accountability.
You need domain-grade semantics in a regulated field
Apply a Domain Pack (pharma, genetics, toxicology, AML/KYC) to tune the ψ-stack to the language, constraints, and edge cases of your field — and make retrieval behave like an expert reads.
Outcome: faster pilots and cleaner evaluation in high-error-cost environments.
Domains and deployment options
Deploy the ψ-stack in the way your environment demands — from pilots to production, from cloud to private infrastructure. Extend semantics with Domain Packs, then scale with enterprise-grade deployment options aligned to adoption.
Domain Packs
Phase-aligned semantics for specialised and regulated domains. Encode terminology, exception patterns, and high-error-cost language so retrieval stays precise where it matters.
Enterprise & deployment
Run on-prem, air-gapped, or in private VPCs. Keep control of data residency, observability, and security boundaries — and deploy the ψ-stack where your constraints live.
Learn more
Want to understand the technology behind wave-based semantics? Read deep dives, architectural guides and practical comparisons on our blog.