◉ how our platform solves this · data/ML
wire the arm. we run the loop.
The build is the point. But the lab runs on a pipeline of toil — datasets logged and labeled, runs tracked, models trained and evaluated, the registry kept current, the same experiment re-run because the config drifted. It is the work around the loop, and it never sleeps.
◉ the answer
Agentic workflows run train → evaluate → register on your own infrastructure — against your own models — with a human on the gate for anything that acts, and the record written as it goes.
◉ how we solve it · our process
- Chat — you state the goal in plain language; we shape the work with you.
- Flows — it becomes a repeatable workflow, on rails and fully auditable.
- Missions & Fleets — coordinated across as many instances as the job needs.
- Synth · Brainbow · Code Mode · Exec — the right tool spun up for each step: a throwaway utility, a driven browser, code run against your real systems.
- your ground, your gate — every step on your infrastructure, a human approving anything that acts.
◉ the mechanism · what actually happens
One request, walked end to end — every step on your infrastructure, against your own models, with a human on the gate.
- ChatMode
You state the goal in plain language — “run the training job, score the eval, register the model.” ChatMode plans the work and dispatches its built-in agents — planning, data-query, validation, synthesis — to decompose it into pull, analyze, act, assemble. No prompt engineering, no scripts.
- SmartModelRouter
Each step is routed to a model that fits it — a fast model to tag and sort data, a long-context model to read an eval report or a long training log, a strong reasoner to judge whether a run regressed or improved — across the providers you register. Bring your own models and run them on your own infrastructure; your training data, your weights, and your eval sets stays inside your network.
- MCP tools · OBO credentials
Agents reach your feature store, object storage, training clusters, and the model registry as MCP tools, each call running under your own identity. The platform forwards your scoped credentials per call — no shared token, no pooled service account, no secret pasted into a prompt. Every call is logged.
- CodeMode · Code Execution
The glue work runs against your real code. CodeMode is an enterprise-platform capability that emits telemetry to your own monitoring inside your boundary — never your data, and nothing ever leaves your network; for one-shot transforms, Code Execution will write a one-shot data-prep or eval-scoring tool, run it sandboxed, and discard it. Nothing persists, and it re-runs as the inputs change.
- AgenticWorkflows
The train → evaluate → register flow becomes a flow of named agents — repeatable, auditable, on rails. And there’s more behind sign-up: data-quality sweeps, hyperparameter search orchestration, and the drift-and-retraining triggers — the many ways the platform runs this, with more revealed after you sign in.
- HITL approval
Nothing acts until you say so. The human-in-the-loop gate is real architecture, not a setting — any step that would promote a model or kick off a training run stops and waits for a person; if no one approves, it times out and is denied. You review the plan and the scope, and approve before anything happens.
- Code Mode · build the loop tool, don’t wait for it
The data-logging tool, the eval-scoring script, the sim harness you keep deferring — Code Mode drafts it against your own repo and proves it with tests, gated by your review before it touches the loop. You stay on the hard control and perception work; the agent builds the tooling you’d otherwise wait a quarter for, so the team you have runs the loop faster.
- Audit trail · DLP · RBAC
Every model call, every tool call, every approval is written to an append-only audit log — once a decision is recorded it’s frozen, so the trail is tamper-evident. DLP keeps sensitive material inside your network, and role-based access scopes who can do what. It’s a record you can stand behind.
The toil ran itself, it acted only on your nod, and the record is already written — and that’s one flow. Data-quality sweeps, hyperparameter search orchestration, and the drift-and-retraining triggers are waiting behind sign-up.
◉ go deeper
Run it on your own infrastructure — or with us.
Talk to us to see the platform on your stack — governance, fleet, support, and the enterprise capabilities. Or self-host the platform in your own environment and run the whole thing today.
The platform self-hosts in your own environment — chat, flows, and the ops MCPs. Fleet, Mission, CodeMode, governance and support come with the enterprise platform. Designed for FedRAMP-High deployment.