Inference learns your workload and gets more out of every request.
Every request is scored against cost and quality, then sent to whichever model, frontier or a cheaper tuned one, meets your acceptance bar for the least money. Nothing ships until it's verified.
workload.jsonAuto routing
Your request
Routing tier Auto
ResponseAwaiting request
RESULT
Output meets the example acceptance check
Selected routeML.ai StandardQuality gateBaselinePolicyTenant rules
Trusted by leading teams
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NeoSapien, Pepsi, Heineken, American Express, Adidas, McKesson, American Electric Power, A. O. Smith, FanDuel, AWS, Tata Motors, Stellantis, Iron Mountain, Suntory, BT Group, Nissan, Breitling, América Móvil, DISH Network, McDonald's.
What is Inference
ML.ai Inference sits between your app and the model stack.
Every step gets routed to whichever model clears your acceptance bar for the least cost, and nothing ships until it's verified against that bar.
Your application stays yours: no rewrite, no new SDK. ML.ai only takes on the three jobs in the middle of the stack.
- RouteMatch effort to the task
- VerifyHold the acceptance bar
- LearnTune what repeats
Request pathIn flight
Your application
tasksupport_summary
ML.ai InferenceSits between the app and the models
RouteVerifyLearn
Model stack
StandardRoutine work
HighHard reasoning
TunedYour patterns
Waiting on the quality gateNothing ships until it clears the bar
A tuned model that gets better the more your workload runs.
Run it in shadow, check the results against your acceptance bar, then move traffic once it clears that bar. The route keeps improving after that too.
We mirror your traffic first.
Every request still goes to your current provider. A copy of each call is routed to ML.ai in parallel, so we can measure cost, quality and latency before anything changes for your users.
Nothing about your production path changes yet.
Live traffic Unchanged
Your application
Existing provider
Mirrored, non-blocking
support_summaryShadowed
structured_extractionShadowed
code_reasoningShadowed
Baseline reportCost · quality · latency
Then we train on your patterns.
Requests that repeat, like the same classification or extraction task, become training data for a smaller model tuned to your workload. It's cheaper to run, but has to pass evaluation before it touches real traffic.
A tuned candidate still has to earn its spot.
Recurring work becomes reusable knowledge
Classify
Summarize
Extract
Summarize
Classify
Tuned candidate
Task-specificSmaller
by design.
by design.
Only the winners get traffic.
A candidate that clears your acceptance bar starts taking a small slice of real requests, a canary, not a full switch. Your existing route stays wired up the whole time in case it needs to take back over.
You can roll back a shift as easily as you started it.
Controlled promotion Evaluation gated
Acceptance checks passedA route changes only after the evidence.
Existing route
Preserved
Validated route
Canary slice
And we keep watching it.
Cost, quality and latency stay visible after the shift, not just during evaluation. When your workload changes shape, the route adjusts instead of quietly drifting out of spec.
The tuning loop doesn't stop at deployment.
Workload health Monitoring
QualityWithin baseline
PolicyChecks active
DriftUnder observation
Acceptance baseline
Every request earns its own decision.
Nothing ships on a hunch. Each request is routed, checked and logged against rules your team set.
Incoming request
StandardRoutine task
HighComplex reasoning
Task-appropriate effort
Routing that matches effort to the task
A support summary and a checkout bug don't need the same model. ML.ai reads the request and sends it to the cheapest route that can still handle it.
Evaluation suitePromotion gate
Accepted baselineReference
Candidate A Pass
Candidate BHold
Only accepted results move forward
Nothing ships until it clears your bar
New routes run against your accepted baseline first. Only the ones that meet it get to serve real traffic.
Input boundary
Email alex@example.com••••••••••••
Order #1042
Task Replacement request
Tenant policy applied before routing
Your data rules, applied before routing
Redaction, retention and access boundaries run at the input, not as an afterthought once the request has already left.
Request trace#req_1042
Classify
Complete
Route
Standard
Generate
Complete
Verify
Accepted
Decision, route and checks recorded
A trace for every answer, not a black box
See which route it took, what checks it passed, and why — down to the individual request.
Most of your traffic doesn't need your best model.
ML.ai finds the requests that can run cheaper and moves them, without touching the ones that can't.
Where the savings come from
30–45%Typical cost reduction
Routine requests go to a cheaper route. Hard ones still get the frontier model.
Both are checked against the same acceptance bar before anything ships.
Lower cost per request
Reserve the frontier model for the work that actually needs it.
Quality, never traded away
Every cheaper route still has to clear your acceptance bar first.
Faster time to response
Lighter routes finish quicker, so simple requests come back sooner.
The business case
What could your workload save?
Your workload sets the starting point. Your pilot establishes the result.
Real results · NeoSapien42%
lower monthly AI bill, zero drop in quality
NeoSapien runs a voice-first consumer assistant with five moving parts, from understanding what a customer wants to summarizing the conversation afterward. ML.ai took over the routine parts of that pipeline and left the hardest parts on the best models.
28%Faster responses
21 daysTo full rollout
0Quality regressions
Run this on your workload"We stopped picking models one by one. ML.ai learned the ones we needed and ran them cheaper than we could."
Aryan YadavCo-Founder & CTO, NeoSapien
$5,000$250,000
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, everywhereControl follows
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.
The same routing principle, three different workloads.
The task determines the effort in each of the examples below.
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.
Pick one workload.
Prove it in thirty days.
Cost, quality and rollout targets are agreed up front, before a single real request moves.
Week 0101
Cost / request$0.18
Baseline
Instrument your current workload and log its cost, latency and quality as-is.
A number to beatWeek 0202
Cost targets Quality criteria Rollout boundaries
Targets
Set the cost, quality and rollout numbers that would make this a pass.
Pass or fail, agreed in advanceWeek 0303
BaselineLive slice
Live slice
Route a small share of real traffic through ML.ai and compare it against baseline.
Real traffic, not a demoWeek 0404
Review the evidence
Go / no-go
Check the live slice against your targets and decide whether to roll out.
Your data, not our pitchFrequently asked questions.
You stay in control, and ML.ai has to earn the business.
Is Inference the same thing as ML.ai?
ML.ai is the company. Inference is this specific product: the router that watches your traffic, learns it, and shifts work to cheaper models once they pass your quality bar. Say "ML.ai" when you mean the company, "Inference" when you mean this router.
Is the demo on this page hitting a real model?
No, it's a mockup. Nothing you type here calls an API, and no key is required. It's there to show you what the routing and the trace look like, not to run your prompt.
Will I need to rewrite how my app talks to models?
No. Shadow mode runs on top of your existing calls and SDK, so nothing changes in your code until you decide to move traffic. Where the production handoff plugs in gets worked out with your team during the pilot.
Can I pick the exact model behind a route?
Not directly. You pick a tier, Standard or High, and ML.ai decides which model handles it and swaps it if a better one becomes available. That's the point: you stop babysitting model choice.
What if the savings don't show up for my workload?
Then you don't pay for a route that isn't earning its keep. The 30-45% figure is a target we design toward, not a promise, and the pilot exists specifically to test it against your real traffic before you commit to anything.