What is neuro-symbolic AI?

Neuro-symbolic AI combines neural networks (like LLMs) with symbolic reasoning (logic, knowledge graphs) to create AI systems that are both powerful and explainable. ReasoningLayer provides this integration layer for enterprise applications.

How does ReasoningLayer improve LLM reliability?

ReasoningLayer adds a reasoning layer that validates LLM outputs against formal knowledge bases, enforces business rules through fuzzy logic, and provides auditable proof trees for every decision.

Is ReasoningLayer suitable for enterprise use?

Yes, ReasoningLayer is designed for enterprise with features like multi-tenancy, role-based access control, SOC 2 compliance, and on-premise deployment options.

The reasoning layer for AI teams

Turn AI answers
into decisions you can prove.

ReasoningLayer unifies your knowledge and rules into one engine that governs every decision, reasons it with real logic, and backs it with a proof, on your data and your rules, running anywhere from a phone to the cloud.

Book a demo Watch the pitch deck

In a regulated industry? See the compliance-ready platform →

The reasoning layer behind explainable, provable AI

Backed by Europe's deep-tech ecosystem

The trouble with ungoverned AI

An answer you can't prove is a decision you can't ship.

AI models guess, your knowledge is scattered, and your rules live in code, so the decisions that matter most stay manual, and every answer is a black box you can't stand behind.

GuessworkScattered knowledgeNo proof

What you can build

One reasoning layer, many products

Give your AI your knowledge and your rules, and it can power the decisions that used to need a team of specialists.

AI agentsAgents that stick to your rules instead of improvising.

Decision automationAutomate the decisions that were too risky to leave to a guess, with a proof for each.

Scheduling & planningSolve rosters, routing and planning by setting the rules, and see every option that works.

Checks & validationCheck any AI answer against your rules, and catch mistakes before they ship.

How it fits your stack

Keep your data and AI. One engine instead of a stack of reasoning tools.

ReasoningLayer works on the information, documents and AI you already use — right where they are, with nothing to move. It does the joining-up, rule-checking and reasoning you would otherwise stitch together from separate tools.

Your applications

WebMobileBack-officeAI agents

Plugs in once

One engine, not a stack of tools

ReasoningLayer

One engine to connect your information, apply your rules and reason over it.

UnifyGovernDecideProve

Works on your data where it already lives

Your data & AI — stay where they are

Tap or hover any card for the technical detail

Runs anywhere — on a phone, your own servers, or your cloud

Nothing gets ripped out — ReasoningLayer replaces the separate reasoning tools that used to sit between your data and your apps.

Proof, not promises

Models guess. Knowledge graphs stop short. Only ReasoningLayer reasons.

Every number below comes from a public, reproducible benchmark, measured the same way on both sides — and we flag the cases where the others are faster, too.

An AI model alone

Give an AI model the same knowledge and it still guesses. ReasoningLayer reasons over it — and says "I don't know" when a fact is missing instead of inventing one.

85%

Right 85% of the time on a real medical-diagnosis test — vs 73% for a strong AI model given the very same knowledge.

~3×

Giving the AI your structured knowledge makes it about 3× more accurate on real business questions (54% vs 16%).

A knowledge graph alone

Most graph databases store facts but only half-reason — they quietly miss the facts your rules imply. ReasoningLayer works them all out, and faster.

18 / 18

Works out every conclusion your rules imply on a standard reasoning test — where rival engines miss many, or never finish.

~1 ms

Answers the heavy queries in about a millisecond — where rival engines take 5–247 ms; fastest of the seven we tested.

Medical differential: DDXPlus (Fansi Tchango et al., NeurIPS 2022), measured with the same knowledge base on both sides, 150 patients. Reasoning completeness and speed: OWL2Bench, LUBM and WatDiv, with correctness cross-checked across seven independent engines; the millisecond figures are a single-machine snapshot. LLM-accuracy figure: data.world / Sequeda et al. (arXiv:2311.07509). ReasoningLayer also passes the W3C SPARQL 1.1 conformance suite.

Why ReasoningLayer

Real logic behind every decision

What turns a guess into a decision you can prove: unified knowledge, your rules, and real reasoning.

Handles "it depends"Works with shades of grey and close matches, not just yes or no.

Understands timeReasons about what happened when, and what still holds today.

Solves hard problemsExplores every combination that fits your rules — not just the first answer.

Knows what it doesn't knowAsks for the missing piece instead of making one up.

In their words

Trusted by the people building with us

With ReasoningLayer, our AI decisions are no longer black boxes: every answer ships with its proof. For our clients' trust, that changes everything.

Nicolas CharonAutrice

ReasoningLayer combines the best of both worlds. As a researcher, I am convinced that uniting the power of deep learning, and generative AI in general, with the robustness of symbolic reasoning is the key to trustworthy AI.

Mehdi KaytoueAssociate professor at INSA Lyon

European deep-tech, qualified by Bpifrance: a unified, governed, sovereign decision infrastructure.

Book a demo

Ready to ship decisions you can prove?

See ReasoningLayer on your own use case, book a demo, or watch the 3-minute pitch deck.