Tailored AI built around the wayyour business actually works.
For regulated firms where one high-volume process is eating senior staff time and off-the-shelf AI does not fit. We diagnose, build, integrate, pilot, and hand over to your team. Your data stays on your infrastructure. Outcomes are agreed in writing before kickoff.
How the programme works
How the engagement runs: 20 weeks, four phases, one team.
Every engagement begins with discovery and diagnostic, not a proposal. We map the workflow, instrument the baseline, and agree the outcome criteria before any build scope is set. The first two weeks pay for themselves in the proposals we don't write.
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.
No handoffs between functions. The team that scopes is the team that builds is the team that hands over.
What changes
The outcomes we commit to in writing.
Every engagement begins with two weeks of baseline measurement on your actual data, not self-reported figures. The same instrumentation runs through delivery, so progress is visible in real time. Pilot targets are agreed in writing before kickoff.
| 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.
How we help
What you actually get.
A working AI system that automates one specific operation, integrated with your existing tools, owned by you. Most AI engagements fail at integration and adoption, not at the model. We own both.
Custom AI built around one workflow.
We build the system to fit how your operation actually runs, document intelligence, decision support, workflow automation, or predictive tooling, trained on your historical data. Not a vendor product configured against a checklist.
Full integration with your ERP and core systems.
The AI lives inside the workflow your team already runs, not next to it. We integrate at the system level so the AI reads from and writes to your core platform directly.
Sovereign by default.
Models train on your infrastructure. Processing runs on your stack. Nothing passes through shared third party AI platforms. This is baseline, not a premium tier.
We stay until adoption.
A system delivered but not used is not delivered. We run pilot, train your team, and stay engaged while your operators build confidence, not just until go-live.
Sound familiar?
This is for you if one of these is true.
You operate in a regulated sector and can't use shared AI infrastructure
Financial services, healthcare, insurance, legal. You cannot send sensitive data through third party AI platforms. The system has to run inside your infrastructure or the project does not happen.
You have tried off the shelf AI and it does not fit your workflows
You bought a generic AI tool. It works on the average customer's workflow. It does not work on yours, because your processes are not average. The work moved back to your team instead of away from it.
Routine document and decision processing is consuming senior staff capacity
A small number of senior people are spending most of their week on document review, classification, and approval. The cost shows up as cycle time, error rate, and capacity you cannot deploy on higher value work.
You need AI that connects to your ERP, not another tool sitting alongside it
You do not need another dashboard alongside your ERP. You need the AI inside the ERP, reading the same records, writing back to the same fields, working inside the system your team already opens every morning.
Where it applies
The same engagement shape across four sectors.
Different industries, different operations, same delivery model: 20 weeks, one workflow, sovereign by default.
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.
Why Ravon
The three places generic AI projects fail. And how this one does not.
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 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 delivered system that no one uses is not a delivered system.
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 keep working at no additional cost until we hit them.
Next step
Start with a 30-minute call about your operation.
Tell us about the operation that's eating capacity. We will tell you within the call whether Backoffice Automation is the right shape of engagement, or whether something else fits better. No proposal until we both agree it's worth doing.
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.