Tailored AI Systems
Bespoke software. Real consulting. Your data stays yours.
Custom-built AI and software that fits your operations, protects your data, and is owned entirely by you. From workflow automation to intelligent decision support, delivered as software plus full consulting through to adoption.
How we help
Software built around your workflow. Consulting that sees it through.
Most AI engagements fail not because the technology is wrong but because the implementation is incomplete. Ravon owns both sides: the software we build, and the operational change required to make it valuable.
Custom AI and automation software.
We design and build AI systems that reflect how your organisation actually processes information: document intelligence, decision support, workflow automation, and predictive tools trained on your historical data. Not a vendor product against a checklist; software shaped to your operation.
Full systems integration.
A system that sits alongside your ERP or core platform creates more work, not less. We integrate directly into the systems your team already uses, so the AI lives inside the workflow rather than next to it.
Data sovereignty by design.
Every system we build is architected so your data remains under your control. Models are trained on your infrastructure, processing happens on your stack, and nothing passes through shared third party AI infrastructure.
Consulting through to adoption.
Software that is technically delivered but operationally unused is not a success. We work through every phase: discovery, build, integration, pilot and handover, and stay engaged while the team is building confidence in the system.
Sound familiar?
This is for you if...
You operate in a regulated sector and can't use shared AI infrastructure
Financial services, healthcare, insurance, and legal organisations cannot send sensitive data through third party AI platforms. You need a system that processes everything inside your own infrastructure.
You have tried off the shelf AI and it does not fit your workflows
Generic platforms are built for the average customer, not yours. When your processes don't match the platform's assumptions, the work moves back to your team rather than away from it.
Routine document and decision processing is consuming senior staff capacity
High volume document review, classification, and approval workflows are absorbing time that should be spent on work that compounds. The cost shows up in cycle time, error rates, and the capacity the business cannot deploy elsewhere.
You need AI that connects to your ERP, not another tool sitting alongside it
A system that runs next to your core platform creates a parallel workflow rather than replacing one. Real operational impact requires the AI to live inside the tools your team already runs every day.
What changes
Measurable outcomes across the operations.
Every engagement begins with two weeks of baseline measurement on your actual data. We do not accept self-reported figures. The same instrumentation runs throughout the programme, so progress is visible in real time.
| Metric | Before | After 6 months | What drives it |
|---|---|---|---|
| Touchless processing rate | 5 to 15% | 50 to 65% | AI handles routine cases end to end |
| Cycle time (average) | 3 to 5 days | Under 24 hrs | Automated extraction and routing |
| Senior staff time on data entry | High | Near zero | Routine cases automated; complex cases pre filled |
| Error and rework rate | 8 to 15% | 2 to 4% | Consistent rule application; pattern detection |
| FTE capacity redirected | Baseline | 20 to 35% | Staff redeployed to complex, higher value work |
| Data compliance exposure | Present | Eliminated | All processing on client controlled infrastructure |
Figures represent typical ranges observed across mid market deployments. Actual outcomes depend on data quality, workflow complexity, and team adoption.
Where it applies
The same pattern across industries.
The shape of the engagement is consistent across sectors: discovery, custom build, integration, pilot, scale and handover. The system we build and the data it handles vary by industry.
Financial services and insurance
Claims processing, trade finance document handling, loan origination, KYC and AML document review, prior authorisation workflows. High document volume, regulatory constraint, and zero tolerance for data leaving controlled infrastructure.
Healthcare and clinical administration
Prior auth processing, referral management, clinical coding support, patient record extraction. Speed of decision directly affects clinical outcomes; patient data cannot pass through third party AI.
Manufacturing and supply chain
Accounts payable automation, three way match processing, supplier onboarding, quality control documentation. High invoice volumes with low tolerance for error and tight working capital implications.
Professional and legal services
Contract review support, document classification, time and matter extraction, regulatory filing preparation. Senior fee earner time is the most expensive resource. AI removes the routine work that compresses it.
How the programme works
Four phases. One team. Defined outcomes.
The engagement runs end to end over about 20 weeks. One team across discovery, build, integration, pilot and handover with no handoffs between functions.
Discovery
Map the target workflow in detail. Instrument the baseline. Define outcome criteria in writing with named owners on both sides. Agree the infrastructure and data governance model.
Build and integrate
Design and build the AI system against the agreed specification. Integrate with your ERP or core platform. Set confidence thresholds and exception routing rules sized to your risk appetite.
Pilot
Run live on one workflow or product line. Train the team. Monitor against baseline. Tune thresholds and exception rules based on real production behaviour.
Scale and handover
Roll out across all workflows in scope. Hand the system to your operations team with documentation and training. Optional managed service tail available while confidence is built.
Why Ravon
The gaps that generic vendors and system integrators leave open.
Organisations exploring AI automation typically encounter three failure modes. The programme is structured to own each one.
The generic tool trap.
Off the shelf AI platforms are fast to procure and slow to deliver real outcomes. Built for the median use case, not yours. When your workflow does not match the platform's assumptions, the work moves to your team rather than away from it.
The data governance exposure.
Most commercial AI platforms process data on shared infrastructure. Some use customer data to improve their own models. For regulated businesses that exposure is not theoretical. It is a compliance risk that has to be eliminated, not managed.
The integration and adoption gap.
A system that does not connect properly to your existing ERP or core platform creates a parallel workflow rather than replacing one. A system that is delivered without consulting through adoption ends up technically live but operationally unused.
Our commitment
Pilot performance criteria are agreed in writing before kickoff, with named owners on each side. If we miss them for reasons within our control, we retune at no additional cost.
How we work
Diagnosis before prescription.
Every engagement follows three phases — discovery and diagnostic, impact & scoping, and solution design with explicit checkpoints. See how we reduce delivery risk before you commit scope.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context. We will identify the highest leverage intervention areas before any scope is agreed.