Five centuries outward
Hemispheres mapped, orbits measured, galaxies photographed. Outward exploration was humanity’s project. The next frontier is not in distance — it is in the precision of what is already here.
Rancagua · Chile · Applied R&D
We do not claim AI should know everything. We design the pattern that truly anchors modern AI.
Validating all human language inside a black box is unfeasible. We build deterministic neuro-symbolic architecture: a C++23 method where each critical claim anchors into a continuous [-1, 1]³ space and an auditable graph. AI proposes. Silicon verifies.
01 — Origin
Rancagua, Región de O'Higgins. Semantic Geometry did not start in a funded laboratory or inside a tech corporation. It started with an intuition its author held through decades of self-taught development: that an idea, a feeling, or a physical law can be located exactly in space.
Human meaning, then, would not be a static definition in an alphabetical dictionary, but a mathematical position in a continuous space of coordinates. If Earth has latitude, longitude, and altitude to locate any object on its surface, thought should have its own Cartesian axes: a GPS of knowledge.
Years later, reviewing the formal literature, that intuition turned out to converge with the lineage of classical psychometrics: Louis Thurstone's measurement of attitudes and Charles Osgood's semantic differential. It was not an orphan idea; it was an idea arriving late to its own scientific tradition.
That is where the technical stance that defines the company comes from. Against models that get it right most of the time without being able to explain why, that cannot tell what they know from what they invent, and that depend on enormous servers, Semantic Geometry was built on the opposite principle: the model proposes, but the graph and the cube verify.
There is also a beautiful and deeply honest paradox in this journey: this engine exists to discipline and anchor language models, but without them it would never have seen the light of day. For decades, semantic geometry was a solitary intuition kept in notebooks and local scripts. Hand-crafting millions of concepts, regulatory meshes, and rigorous C++23 suites would have taken a lifetime. Generative AI was the cognitive catalyst: the scaffolding that accelerated exploration and turned the architecture into reality. Its author gratefully acknowledges that contribution: LLMs were the fire that forged the anchor that now keeps them sane.
Capacity of the [-1,1]³ cube
Hardware / IEEE 754unique representable points
Per axis 2046·2⁵² + 1 9,214,364,837,600,034,817
1 sign · 11 exponent · 52 mantissa — IEEE 754 binary64
Recomputable from the IEEE 754 specification. The 1022 exponents that give |x| < 1, plus the one that gives exactly ±1 with zero mantissa, across both signs.
Current graph occupancy
semantic.db v8.0unique concepts grounded in [-1, 1]³
Taxonomic links 5.86M (active typed edges)
884k synonyms
Measured on 2026-11-03 in semantic.db (2,439,645 concepts / 5,862,311 links / 884,325 synonyms). Zero synthetic data. Counts are regenerated per version, never copied by hand.
Modern models do not yet explore this space — and they don’t have to: the gateway already exists. AI proposes, the graph verifies. The analogy measures address space, not semantic quality: today the graph holds 2.44M concepts, and the value is in verifiable evidence, not in the size of the container.
If every concept occupies a point in the cube — just as every atom occupies a point in physical space —, the cube has capacity for as many concepts as atoms would fit in ~6 million Earths. It is a comparison of address space, not of measured knowledge.
The Inward Expansion
For five centuries humanity expanded outward: crossing oceans, mapping continents, launching telescopes. Semantic Geometry proposes the inner exploration: a space conquered not by distance, but by precision. 7.82 × 10⁵⁶ addressable positions in common silicon at 15 watts.
Hemispheres mapped, orbits measured, galaxies photographed. Outward exploration was humanity’s project. The next frontier is not in distance — it is in the precision of what is already here.
Von Neumann left us a linear bus: memory as a street where everything queues. SG does the opposite: similar things live together. Knowledge is not traversed — it is found. Spatial indexes in SQLite, physical locality in silicon.
The industry compresses words to a single bit (BitNet) and keeps hallucinating cheap. Compressing language gives it no home. SG gives truth an address: coordinates (x, y, z) in the cube. Truth does not depend on eloquence — it depends on its address.
O'HIGGINS · CHILE
“O'Higgins has mined copper for the world for a century. The Inward Expansion proposes a different kind of extraction: an almost boundless semantic cube, where AI finds instead of invents.”
IEEE 754 density is not uniform across [-1, 1]: it is ~2048× greater near 0 than near ±1. Effective resolution varies by region.
Useful resolution is limited not by numerical precision, but by real semantic separation between concepts (coordinates typically ~0.01–0.5 apart). The growth headroom is several orders of magnitude.
Capacity is not the achievement. The achievement is that the container imposes no ceiling: the graph can grow by orders of magnitude without changing architecture.
Two ways of answering when you don’t know
Generative model
Always answers. If the data is not there, it builds something plausible: fluency and truthfulness come from the same mechanism, so from the outside there is no way to tell them apart.
Deterministic verifier
Allowed to stay silent. Geometry guides inference, but only graph evidence certifies it. When there is none, the system says so: refusal is an answer, not a failure.
The difference is not who gets it right more often. It is that one can be wrong without knowing it, and the other cannot assert without evidence.
02 — Verticals
On top of the deterministic verification core we build regulated verticals: domains where a hallucination costs money, a verdict, or a fine. Each one states its stage honestly, without hype.
Fleet operations
Geofences, telemetry, and fleet control in C++23
Header-only C++23 engine with REST + TCP tracker, multi-tenant isolation, and hardware telemetry. Designed to operate 100% offline, at the edge.
Accounting & tax · Chile
Payroll, RLI, and deterministic tax balance
Chilean regulatory calculation without heuristics: Previred settlements, ProPyme RLI, and financial vs. tax balance. Every result is auditable against the law.
Experimental ontological verification. Does not constitute binding tax or accounting advice.
Credit & banking · Chile
Credit simulation with traceable amortization
Credit simulations and amortization schedules with full audit: every payment decision is backed by the engine, ready for inspection.
Labor law · Chile
Ontological grounding of labor regulation
Applying the verification engine to law: labor claims validated against the graph and the regulation, with a formal lineage traceable to the law.
Experimental ontological verification. Does not constitute binding legal advice.
No vertical competes with an LLM at text generation: it competes in what an LLM cannot promise: an answer that knows when to stay silent and can show the regulation behind it. And where an LLM cannot reach —no connectivity, standard silicon, at the edge— the engine can.
03 — Pillars
We do not sell hours of generic development. Each pillar solves a different kind of problem, under the same criteria: explicit structure, measurable performance, and results that hold over time.
We build the plumbing where data arrives intact and on time. Fault-tolerant ETL pipelines, flow optimization, and robust microservices, sized for real production volume.
We bridge the geometry of continuous latent spaces with the certainty of ontological graphs. Generative AI proposes; our neuro-symbolic engine verifies and anchors with mathematical evidence.
Analytic algorithms for problems that do not admit approximations. Combinatorial, geospatial, and financial optimization applied to industrial operations and regulated decision-making.
05 — Paradigm & Retrieval
The market attempts to solve hallucinations with RAG (similarity retrieval). We explain why vector similarity is not verification and how geometry provides the certainty RAG lacks.
| Dimension | SG Semantic Grounding | Traditional RAG (Vector DB + LLM) |
|---|---|---|
| Retrieval Mechanism | Continuous mathematical projection in [-1, 1]³ and transitive path search across a formal graph. | Cosmetic text similarity search (embedding cosine distance) injected into the prompt. |
| Ontological Guarantee | Deterministic: if there is no causal path supported in the graph, it returns NO_EVIDENCE and remains silent. | Zero. The LLM can still hallucinate, omit, or contradict the very paragraphs retrieved by RAG. |
| Cost per Query | 0 tokens to verify. The entire ontological verification runs in local C++23 memory. | High. Thousands of tokens per call to pay for the context window of injected documents. |
| Response Latency | Direct local lookup (p50 0.37 ms desktop / 1.37 ms edge); bounded multi-hop BFS. Public MCP adds network (~0.3–0.6 s). | Seconds (2,000 ms to 6,000 ms between vector lookup and autoregressive token generation). |
| Infrastructure & Power | 15 Watts on local standard hardware. Operates 100% offline with no GPU and no cloud. | Cloud vector databases, high-power GPU clusters, and mandatory internet connectivity. |
| Handling Contradictions | Topological incompatibility: detects rule collisions and formally rejects them. | Blends contradictory texts into an eloquent but false synthesis ("soft hallucination"). |
| Traceability & Auditability | Provable: cites the exact node, weight, and regulatory/technical source for each edge. | Approximate: points to which document was read, but cannot prove how the conclusion was reached. |
What Semantic Geometry Gains:
Formal auditable verification, local microsecond latency on direct lookup (<0.2 ms), bounded BFS separately, 0 tokens to verify, and protection against contradictions. Requires formalizing organizational critical rules.
What Traditional RAG Gains:
Ease of ingesting massive unstructured documents (PDFs, transcripts, notes) without prior ontological modeling. Loses truth control and logical auditability.
Semantic Geometry and RAG do not need to compete: SG is the output guard that makes RAG viable in production.
Text similarity is not ontological truth. RAG finds similar words; Semantic Geometry proves whether the claim holds in reality.
06 — Live Production Lab
Explore real running engine instances across various verticals and protocols in real time.
Automotive Vertical
Live VIN decode, SII official homologation, and vehicle model disambiguation.
Retail & Navigation Vertical
Where is the yogurt in the supermarket? O(1) aisle and category resolution.
Model Context Protocol API
Autonomous agents and LLMs querying and verifying factual claims against the cube.
Live sites running from a 4-core Acer; 15W netbook as fallback, without GPU or cloud.
Current status
Semantic Geometry is not finished. It is built not to lie while it is not.
A model with incomplete coverage that fills the gaps is riskier than one with the same coverage that stays silent. For industrial operations and regulated decisions, that difference is not a detail: it is the product. The public node runs on a 4-core Acer with the netbook as fallback; the read-only LRU cache lives in the bridge, outside the core.
04 — Identity & Contact
Tell us what system you need to build, what pipeline is failing, or what decision you cannot back today. We respond with a technical diagnosis, not a brochure.
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