HealthcareClaims adjudication engine
// 11,000 claims cleared daily
Scale what you know.Deploy with RYVEN.
Private neural agents, custom models and operational automation, delivered in one continuous flow.
+2,400 autonomous deployments and 340 enterprise teams run on RYVEN infrastructure every day.
Northwind LogisticsAtlas HealthMeridian BankVolta EnergyKestrel RetailOrbit Telecom
Our approach
Automate the manual, accelerate the outcome. RYVEN builds private intelligence that measurably lifts throughput and operational precision.
214%
Throughput gain after go-live
91%
Manual review removed
38ms
Median inference latency
48
Systems in production
Imani Sowande
// Co-founder, RYVEN
Our belief
We think intelligence should not simply replace the work. It should remove the parts nobody wanted, and give the judgement back to the people who are good at it.
Diagnose
Find the decisions worth automating.
Architect
Design the system and its guardrails.
Deploy
Ship into a controlled slice of traffic.
Assure
Evaluate continuously, forever.
Operators, engineers and risk owners who took a RYVEN system live and stayed with it.
PR
Priya Raghunathan
Chief Operating Officer, Atlas Health
RYVEN moved us from a stalled proof of concept to a system clearing eleven thousand claims a day. The difference was engineering discipline, not model magic.
TL
Tobias Lindqvist
VP Engineering, Meridian Bank
They refused to ship until the evaluation suite passed. Frustrating for a fortnight, then quietly the best decision we made all year.
CD
Carmen Delgado
Head of Supply Chain, Kestrel Retail
Our forecasting error dropped by a third and the model runs inside our own tenancy. Legal signed off in nine days, which is unheard of here.
MO
Marcus Oyelaran
Director of Operations, Cirrus Freight
The agents handle the first ninety percent of every dispatch exception. My team now spends its time on the cases that genuinely need judgement.
HW
Hannah Weiss
Chief Risk Officer, Volta Energy
What sold the board was the audit trail. Every autonomous action is logged, attributable and reversible. That is what made this deployable.
The platform
Intelligence
by design.
Four layers, engineered together: the interface people act through, the orchestration that carries the work, the knowledge it reasons over and the assurance that keeps it honest.
Explore the stack
38ms median inference99.98% availabilitySOC 2 Type II
Deployment cycle
From raw data to refined intelligence.
Twelve weeks from first conversation to live traffic. Four stages, each with a date and something you can use at the end of it.
We map where decisions are actually made, what they cost and which of them a model can carry. You get a ranked opportunity map, not a slide deck.
Week 1 to 2Decision inventoryData readiness auditRanked value map
We design the system end to end: model selection, retrieval layer, guardrails, escalation paths and the exact numbers it has to move.
Week 3 to 5Reference architectureEval criteriaSecurity review
We build inside your environment, ship to a controlled slice of real traffic, then widen the aperture as the evidence comes in.
Week 6 to 12Production systemObservability stackRunbooks
Once the first system holds, each additional one gets cheaper. We hand over the platform, the practice and the people to run it.
OngoingTeam enablementQuarterly evalsRoadmap ownership
The collective
Sixty-two engineers, researchers and operators working at the edge of applied AI.
Imani Sowande
Co-founder, Chief Executive
Daniel Ferreira
Co-founder, Head of Research
Adaeze Nwosu
Director of Engineering
Rafael Moreau
Head of Applied Design
Anders Holm
Principal Architect
Lucía Ortega
Head of Assurance
Core
$4,950
// per month
A single automated workflow, instrumented and supported.
- One production workflow
- Model gateway access
- Standard evals
- Business-hours support
Growth
$12,500
// per month
Several connected systems with a shared knowledge layer.
- Up to four workflows
- Governed retrieval layer
- Custom fine-tuning
- Weekly eval cycles
Scale
popular$29,000
// per month
A dedicated squad compounding systems across the operation.
- Unlimited workflows
- Private model hosting
- Edge inference runtime
- Dedicated squad
- On-call coverage
Sovereign
Custom
// annual
Full on-premise deployment with ownership transferred outright.
- On-premise install
- Weights and source transferred
- Team enablement
- Security review support
Common queries
Everything you need to know about our AI.
Answers on security, deployment timelines, ownership and what happens when a model gets something wrong.
Inside your boundary. We deploy into your cloud tenancy or on-premise hardware, run inference against models you own, and keep vector stores in infrastructure you control. Nothing is sent to a third-party provider for training, and retention rules are written into the architecture rather than a policy document.
A scoped first system reaches controlled production traffic in eight to twelve weeks. The first two weeks are diagnosis, the next three are architecture and security review, and the remainder is build, evaluation and staged rollout. Complex regulated environments add roughly three weeks for approvals.
Yes. We connect through documented APIs, event streams, message queues and database replicas, and we have built adapters for the usual enterprise estate: SAP, Salesforce, ServiceNow, Snowflake, Databricks, Workday and a long tail of internal tools. Where no API exists, we build one.
Completely. Source code, model weights, fine-tuning datasets, prompts, evaluation suites and infrastructure definitions are yours, transferred on delivery with full documentation. There is no licensing trap and no dependency on us to keep the system running.
We agree on the numbers before we write code: cycle time, error rate, cost per decision, containment rate, whichever genuinely matters to your operation. Those metrics are instrumented on day one and reported continuously, alongside the model quality evals, so the business case is visible rather than asserted.
Every system ships with confidence thresholds, escalation paths and a human review queue. Low-confidence decisions route to a person by design. Actions are logged and reversible, drift monitors flag degradation early, and regression suites run on every model change before it reaches production.
Whichever fits the constraint. We work with open-weight families for anything that must stay private, frontier APIs where quality justifies it, and small specialised models we train ourselves for narrow high-volume tasks. Model choice is an engineering decision we revisit each quarter, not a loyalty.
That is the preferred shape. We pair with your engineers throughout, run the practice openly, and structure the engagement so your team owns the second system with us in support and the third one on their own.
Next step
Bring us the decision that costs you the most.
Two weeks from now you could have a ranked map of where intelligence pays in your operation. The first two sessions cost nothing.
// Every enquiry answered within one business day
