The harness that learns your workload and runs it for a fraction of the cost
Your teams already run Claude, GPT, Gemini, and open weights across agents, copilots, and pipelines. ML.ai routes each task to the most cost-efficient model that still clears your quality bar.
ML.ai is a product of Pixis.ai, backed by SoftBank Vision Fund and General Atlantic.
The tuned model takes the load. Frontier stays on the hard tail.
30%
Cheaper than frontier-only spend
100%
Meets or beats your quality bar
100%
Meets your compliance guardrails
30 days
Proof of concept, on your workloads
Trusted by leading teams
The problem
Your AI bill is growing faster than your revenue.
Most companies start with one AI model and never look back. It works, right up until usage takes off. Then the same model that got you to launch starts quietly eating your margin every month.
Too many choices
40+ models on the market, a new one every month. Nobody has time to test them all, so most teams pick one and stop looking.
Prices fall, bills don’t
Per-token prices keep dropping. Usage grows faster than prices fall, so the total bill climbs anyway.
Cheap can be risky
Switching to a cheaper model to save money can quietly break quality. Often nobody notices until a customer complains.
The solution
ML.ai Inference: the harness that routes every request intelligently.
One endpoint sits in front of 40+ models, and every prompt gets sent to the model that earns its cost on that task. A router only picks where a request goes; ML.ai also classifies, verifies, and audits it along the way.
Intelligent model routing
Every prompt is scored on complexity, domain, latency, and safety, then routed across 40+ models under your own tenant policy.
The tuned models
ML.ai Research, ML.ai Code, ML.ai Voice: small, fast, first-party models continually fine-tuned on your traffic until they match the frontier baseline.
Meets or beats the benchmark
Quality is held flat by design. Verification gates, structured-output guarantees, and quality-gated rollback keep output at or above the baseline while cost per request drops.
Compliance guardrails, your rules
Frontier-only, self-hosted-only, or sticky routing, enforced per tenant, per team, per use case, with a full audit trail and no change to your vendor or procurement.
What you save
See what you save on the same work.
Drag your monthly model spend to see the range. Not a one-time discount: ML.ai keeps learning your traffic, so the savings compound instead of leveling off.
Monthly model spend
$50,000
Where it goes
$27,500–$35,000 still on frontier models
You save (30–45%)
$15,000–$22,500
per month, same eval bar
$180,000–$270,000 saved a year, on the same work.
Start saving $15,000/moHow it works
A loop of four steps that keeps running after you set it up.
Nothing here is a black box: every step is inspectable in the console, gated on your evals, and can be paused or rolled back per workload.
Shadow
ML.ai mirrors your frontier calls, no user impact. Your existing SDK stays unchanged, frontier still serves every call.
Distill
ML.ai clusters traffic into recurring tasks. Frontier answers become the gold labels, and a task-tuned model is fine-tuned on your data.
Serve
Your evals are the acceptance test. Traffic shifts once the tuned model matches your quality bar: shadow parity, then partial live, then full cutover.
Watch
A drift monitor watches distribution, latency, and cost. Retune fires automatically, and every promotion lands in the audit trail.
The product line
Four products, one harness underneath.
Every product runs on the same router, tuned models, and guardrails. Each ships with a base checkpoint and gets fine-tuned against your traffic during the shadow phase.
ML.ai Research
The tuned small-model layer behind ML.ai. Classification and structured extraction, fine-tuned on your traffic until it clears the frontier baseline at a fraction of the cost.
Sizes
300M / 1B / 3B / 8B
Used for
Intent, routing, PII, extraction, sentiment, safety
ML.ai CLI
The harness from the command line. Same router, same tuned models, same guardrails as ML.ai Code, wired for CI pipelines, cron jobs, and scripted automation instead of an editor.
Sizes
Terminal agent
Used for
Scripted and headless workflows
ML.ai Code
Per-customer LoRA on private repos. Powers drop-in base_url swaps for Cursor, Continue, Zed, Cline. Verifier gates every generation.
Sizes
3B / 8B / 32B
Used for
IDE codegen, plan-execute-verify
ML.ai Voice
STT, turn-taking, and tool execution folded into one call. Deployed on dedicated capacity for banks, insurers, telcos, with per-jurisdiction PII rules built in.
Sizes
STT + LLM stack
Used for
Regulated-buyer voice AI runtime
Security
Every turn passes through five checks before it ships.
Guardrails are policy-as-code, compiled to rules and evaluated on every request. Every deny, allow, and rewrite is a signed audit event you can replay end to end.
Input PII
Redact, mask, or reject, per jurisdiction.
Prompt injection
Caught model-based, plus a regex fast-path.
Output moderation
Toxicity and regulated content, rewrite or reject.
Output PII egress
Blocks exfiltration, even from retrieval leaks.
Your data stays yours
Input data is encrypted and redacted per your policy, never used to train shared models. Shadow logs and tuned checkpoints are deletable and exportable on your schedule.
Every decision is audited
Every routing call and guardrail firing lands in WORM storage, ready for a regulator export, replayable end to end.
Proof
It is already carrying production traffic.
Targets we hold ourselves to, and one team's real numbers after switching.
30-45%
Target cost reduction
86%
SWE-bench Verified
30 days
Shadow to prod, by design
0
Regressions our gate allows
What we commit to
The thresholds we hold ourselves to on every deployment
Ranges reflect our design targets ahead of general availability, not an average across customers yet.
Real results, NeoSapien consumer voice AI
42%
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 days
To full rollout
0
Quality regressions
“We stopped picking models one by one. ML.ai learned the ones we needed and ran them cheaper than we could.”
Aryan Yadav
Co-Founder & CTO, NeoSapien
The pilot
One workload, 30 days.
If it doesn’t work, you owe us nothing.
We agree on the numbers together before anything starts. If we miss them on your own traffic, you walk away. No strings, no fee.
30 days
Pilot duration
30%
Target cost reduction
$0
Owed if we miss
How the 30 days reads
Watch
We shadow your traffic. Nothing changes for your customers, and nothing is billed yet.
Learn
We agree on cost, speed, and quality targets together, in writing, before anything moves.
Prove
We shift a slice of live traffic over and measure it against the targets you set.
Decide
Hit the targets, and we scale up together. Miss them, and you walk away owing nothing.
A 30-minute call to pick the workload and agree the numbers.
Frequently asked questions.
You stay in control, and we have to earn the business.
Stop picking a model, and start running a harness that keeps lowering your bill.
Bring one workload. We’ll run it through the shadow phase, side by side with what you have today.