ML.ai Inference

Inference that learns your workload.

ML.ai Inference, powered by ML.ai, helps you choose the outcome—not a model for every step. Route, verify and optimize your AI workload without losing sight of quality.

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30–45%Target reduction

≥ baselineAcceptance bar

15–40%Target improvement

ML.ai playgroundML.aiReady

workload.jsonAuto routing

{
  "task": "support_summary",
  "quality_gate": "baseline",
  "policy": "tenant_default"
}

Your request

Routing tier Auto

ResponseAwaiting request

RESULT

Output meets the example acceptance check

Selected routeML.ai StandardQuality gateBaselinePolicyTenant rules

Trusted by leading teams

What is Inference

A layer of intelligence.
Not another model.

ML.ai Inference is powered by ML.ai: it directs each step to a suitable route and keeps your acceptance criteria in the loop.

Your application stays yours. ML.ai sits between your workflow and the model stack, handling routing, verification, learning and policy.

It learns. It grows.
It keeps improving.

Shadow first. Evaluate before a shift. Watch what happens after it.

Observe without disruption.

Mirror existing calls while the original provider continues serving your users. Establish a baseline before changing a route.

Your current integration stays in place.

Live traffic Unchanged

Your application

Current provider

User response

Mirrored requests

request_042Support summaryobserved

request_043Invoice extractionobserved

request_044Support summaryobserved

request_045Code reasoningobserved

Workload baselineCost · quality · latency

Production control

Every request.
A considered decision.

See the work behind the response, not just the model name.

Incoming request

StandardRoutine task

HighComplex reasoning

Task-appropriate effort

Intelligent routing

Match model effort to the task, instead of paying for the most capable route every time.

Evaluation suitePromotion gate

Accepted baselineReference

Candidate A Pass

Candidate BHold

Only accepted results move forward

Quality gates

Evaluate candidates against your acceptance criteria before promoting a route.

Input boundary

Email alex@example.com••••••••••••

Order #1042

Task Replacement request

Tenant policy applied before routing

Tenant policy

Apply your input, output and data-handling boundaries to the request.

Request trace#req_1042

Classify

Complete

Route

Standard

Generate

Complete

Verify

Accepted

Decision, route and checks recorded

Observability

Inspect the decision and its checks in a trace, not just a final answer.

The same quality bar.
A more efficient route.

Keep cost, quality and latency in the same frame.

Continuous optimization

Less wasted capability.
More work completed.

30–45%

Target reduction

Reserve expensive capability for the work that needs it.

≥ baseline

Your acceptance bar

The target is parity or better, not cheaper but worse.

15–40%

Target improvement

Reduce the work required to produce an accepted answer.

ML.ai design targets, not averages across customers. Validate them on your workload during the pilot.

The business case

What could your
workload save?

Your workload sets the starting point. Your pilot establishes the result.

Reported customer result · NeoSapien42%

Lower monthly AI spend
28% faster responses · 21-day rollout

Read ML.ai’s customer account

Monthly model spend$50,000

$5,000$250,000

Current baseline

$50,000

Target range

$27,500 – $35,000

Target monthly savings$15,000 – $22,500$180,000 – $270,000 per year

Prove it on your workload

Policy-aware routing Within your boundaries

Approved routes onlyPolicy travels with the request

Your rules, everywhere

Control follows
every request.

Keep routing within the boundaries you define.

AutomaticTask-appropriate routes within the approved pool.

Frontier onlyConstrain the workload to approved frontier providers.

Self-hosted onlyKeep the route within the agreed private environment.

Input PII controls Prompt-injection checks Output moderation PII egress & audit trail

Private deployment is scoped with the ML.ai team. The globe is a conceptual routing illustration, not a service-region map.

Different workloads.
One way to run them.

Three examples of the same principle: the task determines the effort.

Customer support

My order arrived damaged. Can I get a replacement?

Replacement requestConfirm the order details and replacement process.

Understand the request.

Classify and summarize a support message into a clear next step.

Document extraction

InvoiceINV-1042USD 248.00

invoice_number INV-1042amount 248.00currency USD

Make information usable.

Return clean fields that can be checked against a defined schema.

Code assistance

Double-submit race condition

– submitCharge(payload)
+ enforceIdempotency(key)
+ submitCharge(payload, key)

Verify the double-submit path

Reason through complexity.

Use a higher-effort route when the problem needs more reasoning.

Built for your stack

Start in shadow.
Keep your application.

Observe an existing workload first. Define acceptance tests, then agree the integration and promotion path with ML.ai.

Existing callsShadow & evaluateApprove a shift

Explore the documentation

Workload configuration

{
  "workload": "support_summary",
  "mode": "shadow",
  "routing_tier": "auto",
  "quality_gate": "baseline",
  "policy": "tenant_default"
}

Conceptual configuration. Confirm the production integration with ML.ai.

One workload.
Thirty days to prove it.

Agree the cost, quality and rollout criteria before moving traffic.

Week 0101

WORKLOAD REPORT

Watch

Observe the current workload.

Your starting baseline

Week 0202

ACCEPTANCE PLAN

Cost targets Quality criteria Rollout boundaries

Learn

Agree the acceptance targets.

A measurable definition of success

Week 0303

CONTROLLED TRIAL

BaselineLive slice

Prove

Measure a controlled live slice.

Evidence from your workload

Week 0404

PILOT DECISION

Review the evidence

Decide

Scale when the evidence earns it.

A decision you can defend

Discuss a pilot

ML.ai states no fee is owed if agreed pilot targets are missed. Confirm the pilot terms with the team.

A few things worth knowing.

What is the difference between Inference and ML.ai?

Inference is the workflow experience and runtime on this page. ML.ai is the orchestration and decision layer underneath it—the system that routes work, applies policy, verifies quality and keeps learning. Use “Inference” when talking about the experience, and “ML.ai” when talking about the intelligence layer that powers it.

Does the demonstration call a real model?

No. It is a browser-only product demonstration. The route, response, charts and map are illustrative; no API key is needed and no prompt is sent to a model.

Do I need to rewrite my integration?

The published Shadow stage keeps existing calls and the current SDK in place. The production handoff is agreed for your workload rather than assumed from the demonstration code here.

Can I choose a particular underlying model?

The current documentation describes ML.ai Standard and ML.ai High routing tiers. ML.ai chooses the model behind each tier. This preview uses those names rather than inventing a public model catalog.

Are the savings guaranteed?

The published reduction ranges are design targets, not averages across deployments. A pilot establishes results against your own baseline and agreed acceptance criteria.

ML.ai

Bring the workload.
Let the evidence decide.

Start a pilot Explore ML.ai Infinite

Visuals demonstrate product concepts. Design targets are not a performance guarantee.

Inference that learns your workload.

A layer of intelligence.
Not another model.

It learns. It grows.
It keeps improving.

Observe without disruption.

Learn the work you repeat.

Shift only what proves itself.

Keep the outcome in view.

Every request.
A considered decision.

Intelligent routing

Quality gates

Tenant policy

Observability

The same quality bar.
A more efficient route.

Less wasted capability.
More work completed.

Target reduction

Your acceptance bar

Target improvement

What could your
workload save?

Control follows
every request.

Different workloads.
One way to run them.

Understand the request.

Make information usable.

Reason through complexity.

Start in shadow.
Keep your application.

One workload.
Thirty days to prove it.

Watch

Learn

Prove

Decide

A few things worth knowing.

Bring the workload.
Let the evidence decide.

Inference that learns your workload.

A layer of intelligence.Not another model.

It learns. It grows.It keeps improving.

Observe without disruption.

Learn the work you repeat.

Shift only what proves itself.

Keep the outcome in view.

Every request.A considered decision.

Intelligent routing

Quality gates

Tenant policy

Observability

The same quality bar.A more efficient route.

Less wasted capability.More work completed.

Target reduction

Your acceptance bar

Target improvement

What could yourworkload save?

Control followsevery request.

Different workloads.One way to run them.

Understand the request.

Make information usable.

Reason through complexity.

Start in shadow.Keep your application.

One workload.Thirty days to prove it.

Watch

Learn

Prove

Decide

A few things worth knowing.

Bring the workload.Let the evidence decide.

A layer of intelligence.
Not another model.

It learns. It grows.
It keeps improving.

Every request.
A considered decision.

The same quality bar.
A more efficient route.

Less wasted capability.
More work completed.

What could your
workload save?

Control follows
every request.

Different workloads.
One way to run them.

Start in shadow.
Keep your application.

One workload.
Thirty days to prove it.

Bring the workload.
Let the evidence decide.