ML.ai

ML.ai helps teams ship more
without giving up control

Ship more with the same headcount: every change approved before it lands, at one flat monthly price.

Install ML.aiBook a call
“Give it software work, not token budgets.”
Our mission, in one line
  • Ship more with the same teamTasks run end to end and land as one pull request.
  • Every change under your controlNothing is written without approval, nothing merges on its own.
  • Never run outOne flat price, no token meter, no stalled work.

Why ML.ai exists

Teams hesitate to hand real work to an agent for two reasons. Everything we build starts from them.

Proposed change · nothing written yet

session.ts+2 −1
40export async function getSession(id) {
41- const s = store.get(id)
41+ const s = await store.get(id)
42+ if (!s) throw new SessionNotFound(id)
43 return s
Apply this change?
AllowAlwaysDeny

An agent changing code nobody approved

Nothing changes without your say

Before anything is applied, ML.ai shows the exact lines it plans to change. Nothing is written to disk without approval, and nothing merges on its own. The agent does the work; the developer decides what ships.

How approvals work

Your plan

Jobs running in parallel 4

  • Token meterNone
  • Credits to run outNone
  • BillingOne flat monthly fee

A bill nobody can predict

One flat price, no surprise bills

One flat monthly fee, with no token meter. Teams choose how many jobs run in parallel, so they scale throughput, not spend, and work never stalls because credits ran out.

See pricing

What we won’t compromise on

  1. The developer decides what ships

    The agent does the work. Approval is a step in the product, not a setting to remember.

    Permission prompts

  2. Predictable beats metered

    A flat fee and a concurrency limit you choose. You scale throughput, not spend.

    Pricing

  3. Evidence over claims

    Every setup runs the same real tasks, scored by each benchmark’s own tests, and the method is published.

    Benchmarks

  4. The right model for each step

    Routine steps run on lighter models and hard steps get more, so cost follows difficulty.

    ML.ai Inference

  5. Secure by default

    Each session in its own VM, zero data retention with model providers, and an audit trail for compliance.

    Engineering and research
Benchmarks

HowML.aiflagship model stacks up

Coding-agent benchmarks from ML.ai evaluation runs, scored on the flagship model inside Infinite's routing pool.

See the full results

Also scored: MBPP+ 85.4%, SWE-bench Multimodal 84.8%, SWE-Lancer Diamond 82.4%, Terminal-Bench 66.7% and AgentBench-OS 60.0%.

See the full results

SWE-bench Verified

95.0%

Real GitHub issues fixed in the repo

SWE-bench Pro

92.6%

691 harder, multi-file tasks

LiveCodeBench v6

83.4%

Fresh competitive programming

Built for the enterprise

The controls security, platform and procurement teams ask for, in place from the first session.

  • SOC 2

  • ISO 27001

  • FedRAMP High

  • Inputs and outputs are always your IP
  • Zero data retention enforced with model providers
  • Comprehensive audit logs
  • Every session runs in its own VM
  • RBAC / SSO / SCIM / OIDC
  • Private networking available
Talk to our team

The research behind it

ML.ai Inference

Routes, verifies and optimizes AI workloads

Every request is scored against cost and quality, then routed to whichever model meets your acceptance bar for the least money.

Read about Inference
Engineering and research

The runtime for verified software work

A code graph, a fixed contract, twelve gates, a sandbox per attempt and evidence behind every claim: how ML.ai turns an issue into a tested pull request.

Read the architecture

FAQs

What is ML.ai?

A coding agent that takes a task, reads your repository, edits the code in a sandbox, runs your tests and opens a pull request. It shows every change before it lands and routes each step to the model best suited to it.

Who is it for?

Software teams that want to ship more without growing headcount, and the security and platform teams who have to sign off on an agent touching their code.

How is our code protected?

Each session runs in its own VM, inputs and outputs are always the customer’s IP, and zero data retention is enforced with model providers. Access is managed with RBAC, SSO and SCIM, with comprehensive audit logs and private networking available.

Where can I see how it performs?

On the benchmarks page: the same 1,344 real tasks run through four setups, with scores, costs and the method behind them.

TryML.ainow

No waitlist. Sign in and Infinite is ready to run your first task.

Download for macOSBook a call