Neuro-symbolic AI for decisions you can trust and defend
Intelligent decision engines that learn, explain and enforce policy in real time.
Infrastructure for regulated decision-making.
The challenge with ML and LLMs in regulated business
Current AI
ML models and LLMs can't express constraints explicitly. Logic is embedded in weights or prompts and cannot be tested. Decisions can't be explained. Changes can't be reversed without rebuilding.
This forces teams to build separate governance infrastructure: guardrails before decisions, observability pipelines after. Multiple systems operating in parallel, each adding cost and complexity. Traditional approaches require constant coordination between disconnected components.
Hybrid Intelligence
Neuro-symbolic AI combines learned logic with built-in explainability, runtime governance and policy enforcement in a single deployable artefact.
The logic is explicit and editable. Decisions and explanations come from the same source. Each version is a standalone artefact that can be tested, deployed and rolled back like code. This eliminates the separate infrastructure required by traditional systems.
Solutions for Financial Services
Lending
Separate risk from lack of evidence. Model credit within peer segments rather than population averages. Enable proportionate decisions where evidence is incomplete but comparable outcomes are strong.
Target: 15-30% approval improvement while maintaining or reducing loss ratio.
Merchant Services
Optimise merchant portfolios as systems. Balance fraud risk, approval rates, processing cost and merchant experience simultaneously through coordinated decision-making.
Target: 30-50% fraud reduction while improving approval rates 10-20%.
Your Use Case
Every regulated business has unique decision challenges. Build custom decision engines for credit risk, fraud detection, AML monitoring, pricing or any domain where decisions must be both intelligent and auditable.
Built for engineering teams
What you build
Neuro-symbolic decision engines that replace ML models, rule-based decision components. Train on your data, inject domain knowledge and deploy as APIs returning explicit logic you can read, test and edit.
What you get
Decisions that are both more accurate and fully auditable. Built-in explainability, runtime governance and immutable audit trails. Every decision includes complete evidence: which rules fired, what constraints applied and what alternatives existed.
How you deploy
Shadow mode validation, incremental activation and simple rollback. Run in parallel with existing systems, validate behaviour and activate by segment. Deploy with confidence knowing any version can be restored instantly.
Research
The Architecture
Combining Neural and Symbolic Reasoning
How combining neural learning with symbolic reasoning creates decision systems that are both more accurate and fully auditable.
Hybrid Agents
Introspective Decision-Making
How agents evaluate the reasoning behind their actions before executing them, combining performance optimisation with explanation quality.
The Quality of Explanations
Beyond Post-Hoc Approximations
Why most explainable AI produces post-hoc approximations and how to build explanations that are structurally identical to the decision process.
Knowledge Representation
For Decision Systems
How to structure knowledge through integrated symbolic systems that support deduction, induction and abduction simultaneously.
Trusted by customers and partners
“After rigorous testing, the results were indicative of UMNAI’s technology outperforming alternative solutions in terms of predictive performance, accuracy and interpretability. Rather than continuing to build in-house models, we chose UMNAI as a strategic partner.”
Join the beta program
We're opening the Hybrid Intelligence platform, a full-function neuro-symbolic AI platform for regulated decision-making, 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 decision services where both performance and governance are critical, let's talk.