Every AI request.
Governed. Routed. Recorded.
One intelligent layer between your enterprise and every model you use.
Olive selects the right intelligence for every request — balancing enterprise context, policy, quality, privacy, latency and cost — while creating a permanent record of what works.
Model choice is no longer a decision. It is a routing problem.
Model choice is no longer a decision. It’s a routing problem.
The right intelligence changes with the request.
Task. Data. Sensitivity. User. Policy. Quality. Cost. Latency. Outcome history.
Your models don't know your business.
Generic models understand the internet. They don't automatically understand your terminology, policies, history, customers, teams, goals or definition of a good outcome.
Your data shouldn't follow your model.
Olive decides what stays inside your perimeter, what can be anonymized, and what is allowed to reach external intelligence.
Stop paying frontier prices for commodity work.
Not every request needs your most expensive model. Olive routes each workload to the lowest-cost intelligence that meets the required quality, privacy and latency.
See what happens after the employee presses Enter.
Watch one request move through Olive.
- Surface
- Salesforce
- Department
- CRM
- Task
- Analytics + summarization
- Data
- Customer behavioral data
- Sensitivity
- Restricted
- Intent
- Executive briefing
First-pass acceptance: Yes
- Request ID
- REQ-8231-CRM
- User / Application
- CRM analyst · Salesforce
- Models used
- Llama (VPC), Enterprise Search, Forecast ML, anonymized frontier
- Models blocked
- Frontier model (raw customer data)
- Policy version
- v14.2
- Data classification
- Restricted
- Total cost
- $0.12
- Frontier equivalent
- $0.89
- Latency
- 1.4 sec
- Quality
- 96 / 100
- User action
- Accepted
- Outcome
- Executive summary sent
Models can be rented.
The record cannot.
Know what your AI investment is actually returning.
Most enterprises know how much they spend on AI. Few can answer:
- Which workloads create value?
- Which models are worth the premium?
- Where is money being wasted?
- Which teams get real productivity?
- Which AI decisions improve business outcomes?
Don’t measure AI adoption.
Measure AI return.
| Request | Intelligence | Cost | Quality | Action | Outcome |
|---|---|---|---|---|---|
| CRM Brief | Llama | $0.03 | Accepted | Sent | Campaign launched |
| Legal Analysis | Claude | $0.31 | Edited | Approved | Contract completed |
| Support Case | Fine-tuned Support Model | $0.02 | Accepted | Resolved | No escalation |
| Demand Forecast | Forecast ML | $0.01 | 97% | Approved | Inventory updated |
Cost per token is an infrastructure metric.
Cost per successful outcome is a business metric.
The cheapest model isn’t always the right model.
Olive doesn’t minimize AI cost. Olive minimizes cost subject to quality, privacy, latency and outcome.
Not every request saves money. Complex reasoning still routes to frontier intelligence — Olive optimizes the portfolio, not every token.
Your AI infrastructure should learn from your enterprise.
Traditional gateways route a request and forget it. Olive remembers what happened next.
“High-value creative requests from CRM teams are escalated to frontier reasoning 72% of the time.”
Use anonymized frontier reasoning by default for this workload.
Every request makes the next decision better.
Thousands of requests become an empirical map of how your enterprise should use intelligence.
Which intelligence produced the best result?
Which team, role, task and workflow benefited?
What quality was achieved for the cost, latency and risk?
AI should get more efficient as you use it.
More usage shouldn’t simply mean more AI spend. It should mean better AI economics.
Illustrative / conceptual
Olive proves its own ROI.
“CRM creative requests were manually escalated to frontier intelligence 64% of the time. A proposed routing update is expected to reduce rework while selectively increasing model spend.”
Better AI economics in practice.
Reduce inference cost
Premium frontier model used for almost every request.
Routine extraction, summarization and classification move to efficient models while complex reasoning still goes frontier.
- Lower inference spend
- Reduced frontier dependency
- Quality thresholds preserved
Make frontier AI safe
Sensitive enterprise information prevents teams from using powerful external models.
Restricted data remains inside the enterprise perimeter while approved anonymized context can reach frontier intelligence.
- Sensitive data protected
- Frontier capability retained
- Every request auditable
Turn usage into learning
Employees continually override or escalate the default route.
Decision records reveal which intelligence actually works for the task. Olive recommends an improved route.
- Less rework
- Higher first-pass acceptance
- Better allocation of AI spend
Your AI estate.
One control plane.
Bring every model. Keep every application.
The best model today may not be the best model next quarter. Olive lets the enterprise benefit either way.
Models compete.
Olive wins either way.
No model lock-in. No application rewrite.
Policy before inference.
| Self-hosted | Customer cloud | Approved frontier | Unapproved frontier | Consumer AI | |
|---|---|---|---|---|---|
| Public | Allowed | Allowed | Allowed | Logged | Logged |
| Internal | Allowed | Allowed | Allowed | Approval | Blocked |
| Confidential | Allowed | Allowed | Anonymize | Blocked | Blocked |
| Restricted | Allowed | Allowed | Anonymize | Blocked | Blocked |
| PII | Allowed | Anonymize | Anonymize | Blocked | Blocked |
| Regulated | Allowed | Approval | Blocked | Blocked | Blocked |
Six months later, answer “Why?”
- Request ID
- REQ-8231-CRM
- Model
- Llama (VPC)
- Policy version
- v14.2
- Cost
- $0.12
- Accepted
- Yes
- Outcome
- Executive summary sent
Every AI decision explainable after the fact.
Run Olive where your enterprise requires.
Customer Cloud
Maximum control- Private deployment
- Private networking
- Customer-managed storage
- Customer-managed encryption
- Self-hosted models
- Private ML
- Full audit trail
Hybrid
Control + frontier capability- Customer-cloud routing
- Self-hosted workloads
- Approved external models
- Anonymization
- Cost monitoring
- Policy enforcement
SaaS Control Plane
Fastest start- Administration
- Model catalog
- Policy management
- Analytics
- Decision records
- Learning proposals
The enterprise shouldn’t standardize on one AI model.
Standardize on the layer that controls them all.
The intelligence layer that makes enterprise AI safer, more economical and smarter with every request.
See Olive Model Fabric in Action →Model Fabric answers
Frequently asked questions
Clear answers about how Olive Model Fabric governs, routes, and evaluates enterprise AI.
What is an enterprise AI control plane?
An enterprise AI control plane is a coordination layer between business applications and AI models. It applies policy, selects appropriate intelligence for each request, evaluates the result, and records the decision so organizations can manage AI use as an operating system rather than a collection of disconnected model calls.
How does AI model routing work?
AI model routing matches each request to the intelligence that best fits its task, data, policy, quality, privacy, latency, cost, and outcome requirements. A route can combine multiple capabilities, such as enterprise search, forecasting, an in-perimeter model, or frontier reasoning, instead of sending every request to one default model.
How does Olive protect sensitive enterprise data?
Olive applies policy before sensitive data reaches an AI system. Depending on the request and policy, data can remain inside an enterprise environment, be anonymized or aggregated, or be blocked from a particular model. The page's request example shows raw customer data blocked while an anonymized aggregate is allowed.
Does Olive Model Fabric replace existing AI models?
No. Olive Model Fabric is designed to coordinate the models and intelligence systems an organization already uses. It provides a governed layer for deciding when to use different models, enterprise search, forecasting systems, or other capabilities.
How does Olive optimize AI cost?
Olive optimizes the AI portfolio rather than minimizing every individual request. It considers quality, privacy, latency, and outcome requirements alongside cost, routing routine work to less expensive intelligence when it meets the required standard and reserving premium reasoning for requests that justify it.
How does Olive evaluate model quality?
Olive evaluates a result against the requirements of the request and records signals such as quality, grounding, latency, cost, policy version, user action, and outcome. Those records make it possible to compare routes and learn which intelligence works for particular workloads.
What is the difference between an AI gateway and an AI control plane?
An AI gateway primarily manages the movement of requests to models. An AI control plane adds decision-making and operating context: it applies policy, chooses among capabilities, evaluates results, connects requests to outcomes, and preserves a record that can improve future routing.
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