Technology

Density

The cognitive engine builds an optimal semantic trajectory before each generation, then validates the response on output. Between each turn, the system recalibrates its constraints based on the conversation — it learns and refines itself as it accompanies you.

Observable result: given the same question, on the same model, the engine produces shorter responses containing more useful information. More propositions per paragraph, near-zero reformulation.

Memory

Exchanged information persists between sessions. Indexed by semantic relevance — by meaning, cognitive salience, intellectual interest.

Recall is automatic: before each response, the system injects relevant context. Continuous pre-retrieval, model-independent.

Classical RAG vs Native semantic retrieval

Classical RAG (Retrieval-Augmented Generation) splits documents into chunks, transforms them into vectors via an external embedding model (OpenAI, Cohere...), stores them in a vector database (Pinecone, Weaviate...) and searches by cosine similarity. Indexing is fragmented, search is approximate, and switching models loses all context.

Our approach is structurally different. HoloRAG indexes by cognitive salience, not by chunks. VDID assigns a unique vector identity to every piece of information. Pre-retrieval injects relevant context before generation, in a single pass. All of this runs in under 5 ms, regardless of the model used.

Portability

Switch models mid-conversation. Claude to GPT. GPT to Gemini. One click.

The conversation continues. The new model has full context. Memory follows. Tone may vary — each model has its personality — but substance, context and history remain intact.

Migration cost between providers: zero.

Frugality

The system operates in a single pass. One request, one response, one API call.

The architecture inverts the curve: the more the system is used, the less it consumes per request. Persistent memory avoids recomputing what has already been established. Pre-retrieval replaces multiple queries with a single injection.

Encryption

Every archived memory block is encrypted client-side before storage. Post-quantum cryptography by mathematical construction — resistance depends on the structure of the space, independent of parameters.

Security rests on a mathematical proof, not on the assumed difficulty of a computation. Intercepting an encrypted message compromises neither past nor future messages.

The key is the movement. The object does not exist.

Solving

Vktor Spectral® is our high-performance solver. It encodes complex structures — graphs, biological sequences, chemical spectra — into a proprietary high-dimensional mathematical space. Runs without training or GPU, and without specialised infrastructure. A laptop is enough.

The result is deterministic: the same input always produces the same vector. The search is exact, reproducible, and executes in milliseconds where standard methods take seconds or minutes.

Six families of industrial problems

MaxCut resolution alone opens six families of industrial problems. Three reduce to it with no algorithmic modification — exact polynomial reductions. The remaining three adapt only the final rounding phase.

No competitor covers all six verticals from a single engine.

The ground state of an Ising Hamiltonian is identical to the MaxCut of the underlying graph, requiring no algorithmic modification or approximation. Any QUBO instance reduces in polynomial time to a MaxCut instance on an augmented graph (Barahona et al., 1988). The remaining three families use the same engine with an adapted final phase.

Vktor Spectral vs Quantum Machines

MaxCut is the standard benchmark for QAOA (Quantum Approximate Optimization Algorithm). The largest quantum processors today (IBM Condor, 1,121 qubits) cannot run QAOA beyond a few hundred variables — decoherence destroys quantum states before convergence.

Beyond a few hundred variables, quantum computation collapses. Vktor Spectral solves 20,000 variables on a fanless laptop in under 17 minutes.