Efficient model
Best fit$0.63
Selected for this request
Olive Model Fabric
Get more value from every AI dollar.
Olive Model Fabric is the enterprise intelligence layer that governs, optimizes and learns from every AI request.
Reduce avoidable AI cost while protecting quality, security and business outcomes.
An animated story in eight beats. A marketing manager working in an application they already use types “Draft the marketing brief for our Q3 launch.” and submits it; the request lifts out of the application and travels toward Olive Model Fabric, gathering three enterprise context signals on the way: the launch timeline, brand guidelines and unreleased product data. A gold boundary closes around the request, applying permissions, the confidential-data boundary, approved capabilities and the quality threshold. Three intelligence possibilities appear: an efficient model, an advanced model that costs more than the outcome needs, and an enterprise tool outside the policy boundary. Olive selects the efficient model as the best fit, with quality protected, lower cost, and secure and governed confirmed, at an illustrative comparison of $1.42 to $0.63 per outcome. The finished draft returns to the same application, “Marketing brief drafted, ready for your review.” Finally a small evidence signal travels back to Olive, because every outcome improves the next decision. All figures shown are illustrative.
Olive Model Fabric employee request story
Q3 product launch · ask for help
Marketing brief drafted
Ready for your review
Olive Model Fabriccontext · policy · intelligence · learning
Efficient modelBalanced quality and costBest fit for this outcome
Advanced modelHigher cost than needed
Enterprise toolOutside the policy boundary
Quality protectedLower costSecurely governed
An employee asks for help where they already work.
Works across the AI stack you already use.
Keep your applications and models. Add intelligence, governance and control.
01Applications and copilots
02Commercial, open and private models
03Cloud and enterprise systems
04Tools, workflows and agents
Start read-only · No application replacement required
01 / The problem
AI investment is growing faster than enterprise value.
Four symptoms show up long before the spend does.
01Consumption
AI usage is rising quickly and unevenly.
Usage is rising quickly and unevenly across teams, with little warning before a budget is exceeded.
02Quality
Performance varies across models and workflows.
Quality varies across models and workflows, so teams route work by habit rather than by outcome.
03Governance
Policy and accountability differ across applications.
Policy, data handling and accountability differ by application, making enterprise-wide oversight difficult.
04Outcomes
Provider spend rarely connects to the business result.
Spend is visible at the provider level, but rarely connects back to the business result it produced.
02 / How Olive works
Every request becomes a better business decision.
Olive understands the work, applies enterprise controls, selects the best-fit intelligence and learns from the outcome.
Employees keep working in the applications they already use—or use the Olive portal when a unified experience is preferred.
One work request travels through Olive in six beats. It enters from an existing application or the optional Olive portal as the request “Prepare a compliant response.” Three enterprise context signals attach. The request passes through Olive's policy boundary, where permissions, data boundary, budget and quality threshold produce an approved decision space and one intelligence option becomes ineligible. Three vendor-neutral choices are compared: the efficient model is the best fit, the advanced model costs more than the outcome requires, and the enterprise tool is excluded by policy. A compliant response is prepared and returns to the same application that made the request, at an illustrative $0.63 per outcome against $1.42 before, with quality protected, cost measured and policy satisfied. Finally quality, cost, user action and business result flow back into Olive as evidence, so the next request starts with better evidence. All figures shown are illustrative.
Olive request-to-outcome, animated visual
Existing applications
Copilots · workflows
Olive portal
Optional
“Prepare a compliant response.”
Customer contextEnterprise knowledgeData sensitivity
Olive Model Fabric
- Intent understanding
- Control plane
- Intelligent routing
- Learning
PermissionsData boundaryBudgetQuality threshold
Efficient model
Best fit
Advanced model
Costs more
Enterprise tool
Excluded by policy
Returns to Existing applications$1.42 $0.63Illustrative
Compliant response prepared
Quality protected · Cost measured · Policy satisfied
↓ Evidence (quality, cost, business result) → next request
Work enters where it already happens.
A request arrives from an existing application — a copilot, a workflow, an agent — or from the optional Olive portal. Olive receives one work object either way.
- Request
- “Prepare a compliant response.”
- Olive portal
- Optional
Employees keep working where they already work.Both entry paths merge into one request.
03 / Business value
Reduce AI cost without compromising the outcome.
01
Lower avoidable AI spend
Routine work moves to intelligence that meets the requirement at a lower cost.
02
Improve cost per outcome
Spend is measured against completed outcomes, not tokens or API calls.
03
Protect quality
Every recommended change is validated against evaluation thresholds before it applies.
04
Connect spend to results
Spend, quality and outcome data connect in one place leaders can act on.
Cost per successful outcome is the metric that matters.
Lower AI cost · Quality protected · Enterprise control
04 / Executive visibility
From AI consumption to business performance.
CIOs and executives see where AI investment is going, what value it creates, where margin is leaking and which actions can improve it safely.
See spend · Find opportunity · Control action
An animated executive console moving from economics to controlled action in four connected states, following one workflow throughout: Support classification. All figures shown are illustrative and no external action is performed.
Executive console
Observe → Recommend → Control
Illustrative analysisStages
Cost per successful outcome increased 18%.
WorkflowSupport classificationTraced to one business workflow
Total AI spend
$84,260
Month to date
Forecast
$101,400
Full month, current routing
Cost per successful outcome
$2.14
Up +18% on last month
Spend by business workflow
- Support classificationCost driver$26,180
- Document review$18,940
- Sales research$14,320
- Code assistance$13,110
Cost per outcome · 30 days
Outcome efficiency declining+18%
The rise traces to one workflow: Support classification.
AI spend$84,260
Savings identifiedAnalysing
Quality statusNot yet assessed
All figures illustrative. No customer data shown.
AI spend$84,260
Savings identifiedAnalysing
Quality statusNot yet assessed
From AI consumption to business performance.
State 1 of 4 · economics05 / Why Olive
The layer built to manage AI as an enterprise asset.
Understand. Govern. Optimize. Learn.
Model-neutral
Choose across approved commercial, open and private intelligence.
Context-aware
Bring relevant enterprise knowledge and business context to each request.
Governed by design
Apply permissions, policy, security, budget and operational limits before execution.
Continuously learning
Use measured outcomes to improve future context and execution decisions.
Built for enterprise adoption
Start in read-only mode · Keep production traffic in place · Maintain existing employee experiences · Apply policies before execution · Connect cost to completed outcomes · Support multiple deployment trust models
Make every AI request accountable to a business outcome.
What is Olive Model Fabric?
Olive Model Fabric is an enterprise intelligence layer that applies context, governance and economic optimization to AI requests across models, applications and workflows.
Does Olive replace our existing applications or copilots?
No. Olive works behind the applications, copilots and workflows your teams already use. It governs and optimizes the intelligence layer without requiring employees to change how they work.
Does Olive require all AI traffic to pass through a gateway?
No. Olive can start in an observe-only mode using usage and billing data, without sitting in the request path. Runtime routing and control are added only when an organization is ready for them.
How does Olive reduce AI cost without reducing quality?
Olive validates every cost-saving recommendation against evaluation thresholds before it applies. Savings are only recommended when quality, latency and policy requirements continue to be met.
How does Olive protect enterprise data?
Olive applies policy before sensitive data reaches an AI system. Depending on the request, data can remain inside an enterprise environment, be anonymized, or be restricted from a given model or provider.
Can Olive work across multiple models and cloud providers?
Yes. Olive is model-neutral and supports approved commercial, open and private intelligence across major cloud and API providers.
Explore Model Fabric topics