Automation
Workflow orchestration
Compose multi-step agent workflows with branching, retries and approval gates, versioned like any other production artefact.
The platform
Nine components. One governed system.
Workflow orchestration, a model gateway, a knowledge fabric and an assurance layer, assembled for your operation and running inside your own boundary from the first week.
Nine components, one governed system
38ms
// Median latency
99.2%
// Grounded answers
40+
// Integrations live
Your cloud
// Deploy target
Platform capabilities
Everything needed to take a model into production.
Nine components, deployed together or individually, all running inside your own environment and governed by one set of policies.
Intelligence
Model gateway
One interface across open-weight, frontier and in-house models, with routing rules that pick the cheapest model that clears the quality bar.
Data
Knowledge fabric
Documents, tickets, telemetry and databases unified into one governed retrieval layer that respects the permissions of whoever is asking.
Assurance
Evaluation harness
Golden datasets, regression suites and adversarial probes that run on every change and block a release that regresses.
Intelligence
Real-time signals
Stream processing that keeps models fed with current state, so decisions reflect the last thirty seconds rather than last night.
Operations
Cost governance
Per-team, per-workflow and per-decision cost attribution with budget ceilings enforced at the gateway before spend happens.
Automation
Version control for prompts
Prompts, tools and policies live in your repository, reviewed and shipped through the same pipeline as the rest of your code.
Infrastructure
Edge runtime
Quantised builds that run on site hardware, tolerate connectivity loss and sync state when the link returns.
Operations
Decision analytics
Every autonomous action captured with its inputs, confidence and outcome, so you can audit quality and prove the business case.
Workflows in practice
A workflow is where a model stops being a toy.
Three patterns cover most of what we deploy. Each one is versioned, evaluated and reversible, and each one has a defined point where a human takes over.
Exception handling
A queue of cases that used to sit with a team of reviewers, now triaged, resolved or escalated with the reasoning attached.
Auto-resolved91%
Median handling1.4s
Reviewer load-73%
Every run logged, replayable and reversible
AI intelligence
Model choice is an engineering decision, not a loyalty.
We work across open-weight families, frontier APIs and small specialised models we train ourselves. What matters is which one clears the bar for the task at the cost you can defend.
Routing that respects the budget
Each request is scored for difficulty and sent to the cheapest model that clears the quality bar for that task. Expensive reasoning is reserved for the cases that need it.
71% of calls served by a small model
Fine-tuning on your own corpus
Domain vocabulary, internal policy and historical decisions folded into the weights, so the system stops needing a three-page prompt to behave correctly.
2.8x accuracy on domain tasks
Refusal as a feature
Groundedness is scored on every answer. Below threshold the system declines and escalates rather than producing something confident and wrong.
99.2% grounded responses
Continuous evaluation
Golden datasets and adversarial probes run on every change. A model that regresses does not reach production, regardless of who is waiting for it.
Evals on every deploy
Connected to what you run
It meets your estate where it already is.
We connect through documented APIs, event streams and database replicas. Where an interface does not exist, we build one and hand it to you with the rest of the system.
Snowflake
Databricks
Salesforce
SAP
ServiceNow
Workday
Kafka
Postgres
Kubernetes
Slack
Microsoft 365
Sigma
Plus a long tail of internal tools. If it has an interface, we can reach it.
Analytics and performance
If we cannot measure it, we do not ship it.
Every autonomous action is captured with its inputs, its confidence and its outcome. That record is what lets you audit quality, defend the system in a review, and prove the business case without anybody taking our word for it.
38ms
Median inference latency
p50 across production endpoints
99.98%
Platform availability
Trailing twelve months
4.1x
Cost per decision
Reduction against prior baseline
0
Data residency exceptions
Since the practice began
Deployment cohort
Twelve months after go-live, median across clients
Decisions automatedManual reviews
Month 01Month 12
Automated share climbs as confidence thresholds are widened on evidence, never on optimism.
Reviewer workload falls to the cases that genuinely need a person, which is the point of the exercise.
The honest comparison
Three ways to do this. Here is where each one lands.
Off-the-shelf copilots and in-house teams both solve real problems. We are explicit about when they are the better answer.
Time to production
- Generic copilot
- Fast to install, slow to matter
- In-house build
- 9 to 18 months
- RYVEN
- 8 to 12 weeks
Data residency
- Generic copilot
- Vendor cloud
- In-house build
- Yours
- RYVEN
- Yours, by design
Domain accuracy
- Generic copilot
- Generic baseline
- In-house build
- Depends on hiring
- RYVEN
- Fine-tuned on your corpus
Evaluation
- Generic copilot
- Vendor-reported
- In-house build
- Usually deferred
- RYVEN
- Contractual and continuous
Ownership
- Generic copilot
- Licensed
- In-house build
- Full
- RYVEN
- Full, transferred on delivery
Ongoing cost
- Generic copilot
- Per-seat, forever
- In-house build
- Team salaries
- RYVEN
- Fixed build, then your run cost
Where it earns its keep
Four operations, four sets of numbers.
Healthcare01
Claims adjudication at volume
Agents read the claim, the policy and the clinical notes together, settle the routine cases and route the ambiguous ones with a reasoned summary attached.
- Claims cleared daily
- 11,000Claims cleared daily
- Cycle time removed
- 4.2 daysCycle time removed
- Decisions audited
- 100%Decisions audited
Financial services02
Risk review that keeps pace
Counterparty and transaction risk assessed continuously against internal policy, with every flag carrying its citation back to source.
- Faster review
- 68%Faster review
- Unexplained decisions
- ZeroUnexplained decisions
- To apply policy change
- HoursTo apply policy change
Retail and supply chain03
Forecasting that reaches the shelf
Demand signals, weather, promotions and logistics constraints resolved into replenishment instructions that execute without a planner in the loop.
- Forecast error removed
- 31%Forecast error removed
- Working capital released
- $40MWorking capital released
- Level granularity
- StoreLevel granularity
Energy and industry04
Asset intelligence at the edge
Models running on site hardware read sensor streams in real time, predict failure windows and schedule intervention before output drops.
- Less unplanned downtime
- 22%Less unplanned downtime
- Local inference
- <40msLocal inference
- Tolerant by design
- OfflineTolerant by design
Platform questions
What teams ask before they commit.
Deployment model, ownership, evaluation and what happens on a bad day.
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.
See it running
Bring a real process and we will show you the system.
We run a working session against your own data and constraints, not a canned demo. You leave with an architecture sketch and an honest view of the effort involved.
// Every enquiry answered within one business day
