Clear acceptance authority
Review findings stay separate from acceptance. People keep authority, and any decision they delegate to an agent has an explicit scope.
For engineering teams
Your team sets the standard. The AI team meets it.
Experienced developers define requirements, evaluate architecture, and keep decision authority. AceLM makes that judgment explicit, enforceable, and inspectable across all of the work an AI team does for you.
Built for teams that have to explain, later, why something shipped.
Governance
Authority you can point to.
When something ships, you should be able to say who accepted it, what was checked, and why. These are the controls AceLM gives you.
Trace a decision to its evidence
Requirements, plans, revisions, and verification records are connected, so a reviewer can inspect what was checked and why work was accepted.
Required checks and release blockers
Specify the functional, testing, regression, security, and other evidence the work needs. Unresolved findings stay visible, and release reassessment is bound to the candidate.
Review the candidate being shipped
Release acceptance names a revision, a target environment, and a set of actions. Accepting a development plan does not authorize deployment.
Include the people using the software
Accessibility and usability belong in release assessment, with the scope and limits of the evidence recorded.
Connect findings to your obligations
Assessment findings can inform your compliance work. Repository evidence stays distinct from operational controls and formal certification.
Policy and visibility
Tune the rigor. See the cost.
Rigor, enforcement, and auditing
Policy controls let teams choose review depth, assessment scope, evidence requirements, and retry limits. AceLM records the effective policy and every authorized change; configuration cannot silently waive mandatory gates or authority.
Effort and resources across projects
Reporting brings together progress, tokens, costs, and time within and across projects. It distinguishes measured usage from estimates, missing data, and outstanding work, so the numbers stay honest.
Working with AceLM
What working with AceLM looks like.
Before you put a team on anything new, you want to know what it asks of you and what you get back. Here it is in plain terms.
| Area | How it works |
|---|---|
| Getting started | Access is by invitation. We start with one real project of yours, and the checks your team already trusts. |
| Your tools | Works with several model providers and agent runtimes, alongside the editors and pipelines you already use. The project record lives in Git with your code. |
| Your code and data | Deck runs where your code lives. Model calls use your own provider keys and go through Dealer, AceLM's central services, so prompts, code, and tool calls sent to models pass through our servers, as they do with any vendor's coding agent. Retention is yours to configure. We always keep de-identified telemetry with no prompts or code. Enterprise teams can host AceLM themselves. |
| People's time | Plans are approved by a person, reviewers and verifiers are separate agents, and releases stay a human decision. How much else you hand off is your setting. |
| Cost | In our own work, short-term development cost is about the same as without AceLM. The savings come in the mid to long term, from better architecture, design, and code quality and less rework. Reporting tracks tokens, costs, and time per project, with estimates kept separate from measured usage. |
| Transparency | The design is written down, including the lifecycle rules and the 31-lens assessment catalog, and invited teams can read it in full. Every run leaves a record you can inspect. |
Early access
Tell us what your release checklist looks like.
Send it with one task you would hand to an AI team. We start with one real project, write down what counts as success before anything touches your systems, and show you where each check lives in AceLM and what evidence comes back.