Wave Based Semantic Memory — ψ-stack for high-precision retrieval
EvaCortex Lab develops Wave Based Semantic Memory — a ψ-stack enabling high-precision, nuance-aware retrieval and accountable AI reasoning. Phase-aware memory, resonance search and structured orchestration for high-stakes domains.
Wave Based Semantic Memory
High-precision retrieval
Graph-augmented RAG & trace-first agents
Wave Based Semantic Memory for your AI stack
Add a Wave Based Semantic Memory alongside your existing models, embeddings and RAG. Keep your current stack and gain phase-coded memory and resonance-grade retrieval for high-precision, interference-based search.
Built for teams already running vector search, RAG or agentic workflows in domains like pharma, genetics, toxicology, AML/KYC, finance and compliance — where retrieval precision, explainability and auditability are non-negotiable.
Products
Our wave-based semantic stack comprises a suite of interoperable modules. Each product operates on complex amplitude/phase encodings and together they form a cohesive ψ-stack that extends your existing RAG, vector search and agentic systems.
ResonanceDB
Phase-aware memory engine that stores ψ-patterns and retrieves them through interference and resonance rather than distance alone. Use it as a primary wave-semantic retrieval layer, or deploy it alongside your current stack as an upgrade path.
EchoThesis
Semantic ψ-codec that projects text into ψ-patterns with amplitude and phase, making salience, modality and relational orientation explicit for phase-aware retrieval and reasoning.
SenseMesh
Graph-augmented RAG engine that operates in Wave-RAG, Graph-RAG or hybrid modes. It combines resonance-based retrieval with domain graphs to assemble provenance-linked semantic subgraphs that LLMs and agents can reason over directly.
ReasoningCore
Trace-first multi-agent orchestrator built on top of the ψ-stack. It manages plans, dependency graphs and timelines with explicit policies, safety checks and rollback points so reasoning remains auditable.
Domain Packs
Domain Packs configure the EchoThesis for specialised verticals. They extend the core wave semantics with curated vocabularies, ontologies and retrieval patterns for domains like pharma, genetics, toxicology and compliance, so you start with semantic depth instead of cold-start tuning.
Why embeddings aren't enough
Most modern RAG systems rely on vector search: each fragment of text is turned into an embedding and retrieval selects the "nearest" vectors. This is a strong baseline, but treating "one vector = one meaning" as the only model creates hard limits. Magnitude-only similarity struggles with negation, modality and relational structure — precisely where high-stakes systems need clarity, recall and precision.
Negation & modality
Queries like "patients where drug X is not recommended" often retrieve passages about recommendations for X, because magnitude-only vectors place them in the same neighbourhood. The model cannot express "direction" of meaning in a dedicated channel.
Relations & structure
Guidelines, interactions and exceptions depend on how statements relate: support, contrast, alternative, hypothetical. Plain embeddings retrieve text that looks similar, not text that is structurally relevant in a reasoning chain or policy graph.
Explainability & trust
Similarity scores can say "these vectors are close" but not which semantic components aligned, which cancelled, or where contradictions sit. This is not enough for regulators, domain experts or risk committees that expect a defensible trail of evidence.
Wave Based Semantic
Wave semantics represents meaning not as a single point in a vector space, but as a structured signal with multiple channels. Instead of relying only on magnitude-based similarity, information is expressed as wave patterns whose interference reveals alignment, contrast and contextual nuance. This approach can sit cleanly alongside existing retrieval pipelines, while enabling a higher-precision semantic layer where meaning is compared by resonance, not just proximity.
Amplitude
Encodes how strongly content is expressed. Assertions, emphasis and salience become explicit signals rather than side-effects of training or corpus frequency.
Phase
Captures modality and relations — support, contrast, exceptions, conditional or hypothetical structure — allowing retrieval to reflect how ideas relate, not only how similar they appear on the surface.
Resonance retrieval
When wave patterns interact, constructive and destructive interference naturally strengthen coherent evidence and cancel contradictions. This yields more precise retrieval in domains where negation, policy constraints and relational logic are critical.
Wave Based Semantic Memory is the representation underlying ResonanceDB. EchoThesis is our codec for generating ψ-patterns, but the concept itself is model-agnostic and complements your current stack by adding a phase-aware semantic layer without replacing what you already run.
The ψ-stack at a glance
ψ-stack — is a four-module architecture that layers on top of your existing AI stack. Start with ResonanceDB as a wave-semantic memory and retrieval layer, add EchoThesis and Domain Packs for ψ-encoding, then bring in graph-augmented retrieval and trace-first orchestration as your use cases mature.
ResonanceDB — wave-semantic memory engine
Stores ψ-patterns and performs resonance-based retrieval that respects negation, modality and context. Acts as a standalone wave-semantic index that can also sit next to existing retrieval layers, letting you move from conventional retrieval to hybrid to wave-first at your own pace.
Plugs into: search APIs, ingestion pipelines and observability.
EchoThesis — ψ-codec
Encodes text and other signals into ψ-patterns ψ(x) = A(x) · e iφ(x) . It extracts logical structure, modality and relations and makes them explicit at the semantic layer.
Plugs into: RAG systems, retrieval services and domain-specific indexes.
SenseMesh — Graph-Wave RAG
RAG engine that can run in Wave-RAG, Graph-RAG or hybrid modes, combining resonance-based retrieval with domain graphs. Nodes are semantic acts and facts, not tokens, yielding provenance-rich subgraphs that agents and LLMs can reason over.
Plugs into: existing KGs, catalogues, document stores and multi-source corpora.
ReasoningCore — multi-agent orchestrator
Multi-agent orchestrator that coordinates trace-first reasoning over ResonanceDB and SenseMesh for autonomous, tool- and shell-enabled agents. Plans, dependency graphs and rollback points are explicit, so underlying LLMs remain auditable and replaceable.
Plugs into: your current LLMs, tools, schedulers and agentic workflows.
ResonanceDB section anchor
EchoThesis section anchor
Seamless migration
Wave Based Semantic Memory — ψ-stack — is designed as an upgrade path, not a rewrite. You keep your current stack and introduce the wave layer alongside it — moving from experiments to hybrid to wave-first without throwing anything away.
Step 1 — Wave memory alongside your stack
Deploy ResonanceDB as a wave-semantic index next to your existing AI stack, optionally paired with EchoThesis. Ingest the same corpus into ψ-patterns and compare retrieval behaviour offline, without touching production traffic.
Step 2 — Hybrid scoring
Combine conventional similarity with resonance signals. Route a portion of queries through the hybrid path to improve recall, precision and explainability on real workloads and KPIs.
Step 3 — Wave-first
Promote wave-semantic retrieval to the primary layer once it proves itself on your metrics. Conventional indices remain as auxiliary structures and safety nets — no forklift migration or vendor lock-in.
Use cases
Complex decision support
Use phase-coded memory and graph evidence to capture subtle dependencies in healthcare, life sciences, law and finance. Interference patterns ensure contradictions cancel while coherent evidence reinforces, yielding more defensible decisions.
Scientific & R&D search
Domain Packs for pharma, genetics and toxicology extend the core semantics with specialised vocabularies. Navigate literature, clinical trials and omics data by hypotheses and constraints, not just keyword matches or raw cosine scores.
Multi-step agentic workflows
SenseMesh and ReasoningCore enable autonomous agents that can plan, execute and self-audit across heterogeneous knowledge sources and tools. Hybrid retrieval surfaces the right subgraphs; trace-first orchestration keeps the reasoning trace inspectable.
Secure enterprise search
Deploy on-prem or in a private VPC to maintain control over your data. Combine phase-aware retrieval with provenance and audit logs to support regulatory, internal review and model-risk requirements.
Plans & deployment
EvaCortex Lab offers cloud deployments and enterprise delivery options for the ψ-stack. Entry tiers cover EchoThesis and ResonanceDB; advanced plans add SenseMesh, ReasoningCore and Domain Packs. Visit our plans page for an overview of available deployment options and stack configurations.
Enterprise & deployment
For regulated industries or large-scale deployments we offer on-prem, air-gapped and private VPC installations. You keep full control over data residency, encryption and compliance while adding Wave Based Semantic Memory — ψ-stack — as a contained, observable layer on top of your existing AI systems.
Get in touch
Ready to explore Wave Based Semantic Memory in your stack? Contact our team to discuss your use case, request a demo or design a pilot that fits your current infrastructure and risk constraints.