REJH Solutions
We design and build AI systems for difficult operational and decision problems — from agentic software that can reason and act to machine-learning models built around your data.
Data + Tools + SystemsIntelligenceAction
What we believe
Useful AI has to work with real data, real systems and real constraints. It has to survive failure, uncertainty and change — and ultimately produce an outcome worth measuring.
That is what we build.
We start with the problem, not with a model.
Capabilities
01 / Agentic systems
We build agentic systems that can interpret objectives, reason about work, use tools and data, coordinate specialised capabilities and complete tasks within controlled boundaries.
Explore Agentic Systems →02 / Machine learning
We build models around the decisions and data specific to your business — from classification and forecasting to risk prediction, optimisation and decision intelligence.
Agentic delivery
An agent that can answer a question is straightforward.
A system that can perform meaningful work across business data, APIs and applications — reliably, safely and repeatedly — is a different engineering problem.
The platform
We do not rebuild the plumbing for every engagement.
Our agentic delivery platform provides reusable foundations for orchestration, specialised agents, tool integration, state, evaluation, observability and governance.
That lets us concentrate engineering effort where it belongs:
on the problem unique to your organisation.
Not every workflow should become agentic.
Good engineering also means recognising when deterministic software, traditional automation or machine learning is the better solution.
Explore Agentic Systems →
Selected work / Financial services
A Western European invoice-financing company was constraining growth with a generic external credit score and static eligibility rules.
The decision framework was redesigned around stronger risk governance, richer internal operational data and a machine-learning model trained specifically on the company’s historical data.
The work also strengthened the organisation’s risk framework, recalibrated seller, debtor and invoice eligibility rules, and introduced dedicated monitoring to verify that increased eligibility did not increase the underlying risk profile.
Before
After
Our products
Decision intelligence for professional football.
Be-Healthy uses machine learning and large-scale football data to identify changes in player risk and help clubs make earlier, better-informed decisions.
research data modelling engineering production monitoring validation
Be-Healthy is not a demonstration of what we think can be built.
It is a product we actually build and operate.
How we work
01
Understand the decision, workflow, constraints and value before choosing the technology.
02
Test the assumptions that matter and establish whether the proposed approach can actually work.
03
Build the production system — not merely the demonstration.
04
Measure it, monitor it and improve it against the outcome that matters.
No predetermined model.
No mandatory platform.
No AI for AI’s sake.
Senior people do the work
The people thinking about your problem are the people engineering the solution.
Engineering over theatre
We care about architecture, evaluation, observability, failure modes and what happens after the demo.
Outcomes over novelty
The interesting metric is rarely how sophisticated the technology is. It is whether the system improves something that matters.
You don’t need to know whether the answer is an agent, a machine-learning model or something else entirely.
That’s part of our job.
Tell us what you’re trying to solve.