Platform
The Hybrid Intelligence platform is for engineering and product teams building decision services where the quality of decisions matters more than the ease, speed or quantity of decisions, where errors cost money or cause harm, where regulatory and reputational exposure is high. Built on a neuro-symbolic architecture, it produces decision engines that combine learned logic with explicit symbolic reasoning, eliminating the trade-off between performance and governance.
The challenge of building with ML and LLMs in regulated business.
Constraints can't be programmed
Constraints are implicit in training data and prompts, not explicit in verifiable logic. There's no way to dial in the right level of constraint. Models end up either too conservative or too permissive.
Logic can't be tested
The logic is embedded in weights or prompts. There's no way to write unit tests for specific rules or verify that constraints were enforced. The only validation path is running thousands of examples without guaranteeing coverage.
Changes can't be reversed
Every deployment becomes a high-stakes commitment. Reversing a change requires rebuilding, not reverting. Iteration should be cheap and reversible but becomes expensive and permanent.
Decisions can't be explained
ML models and LLMs embed logic in weights and prompts rather than explicit symbolic structures, making reasoning inaccessible. This gap requires governance infrastructure before and after each decision, creating multiple disconnected systems running in parallel.
What the platform is
Neuro-symbolic AI
The neuro-symbolic architecture learns from data like a neural network but outputs explicit symbolic logic that can be read, tested and verified.
Editable logic
The rules, constraints and relationships are explicit and can be read and edited without retraining.
Familiar workflow
Connect data, define objectives and constraints, train and deploy. What changes is the output.
Native explanations
Decisions and explanations come from the same source. Every decision includes the reasoning that produced it and the causes that shaped it.
Single artefact
Training produces a decision engine that combines learned logic, native explainability, runtime governance, policy enforcement and evidence production in one deployable artefact.
Replaces multiple components
A Hybrid Intelligence decision engine replaces an ML model, a software decision component and a rules engine.
What you get
The neuro-symbolic architecture makes these capabilities native to the decision engine rather than requiring separate infrastructure.
Testable logic
The decision logic is explicit and inspectable. Write unit tests for specific rules. Verify constraints before deployment.
Shadow mode validation
Run the decision engine in parallel with existing systems. Compare outputs, validate behavior, and prove safety before committing.
Auditable decisions
Every decision includes the complete evidence: which rules fired, what inputs mattered, what constraints were enforced and what alternatives existed.
Incremental activation
Deploy by segment, threshold, or use case. The decision engine versions like code and deploys like an API.
Simple rollback
Each version is a standalone artefact. Reverting to a previous version is immediate. No rebuilding required.
No separate infrastructure
Decisions and audit trails come from the same operation. No observability pipelines to build. No reconstruction systems to maintain.
Beta access opening January 2026
We're opening the Hybrid Intelligence platform to a limited number of customers and partners starting in January 2026.
Beta partners get early access to the platform, direct engineering support and the opportunity to shape the product as we refine it for general availability.
Spaces are limited. We're working through a waiting list and prioritising teams building decision services in regulated environments where explainability and governance are critical.
If you're building services where decisions matter, we want to hear from you.