YOUR ORGANIZATION, MULTIPLIED
Build autonomous workers for the tasks your business actually runs on. Give them your knowledge and your tools, your skills and your models, set the rules they must hold to, and put them to work around the clock. Deploy them across every channel your organization already uses.
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The factory for AI agents
Design an agent, deploy it as a managed service, watch every session it runs, and tighten it as you go — guardrails, knowledge, skills, or a model trained on your own work.
One platform for the whole life of an agent, on infrastructure your organization controls.
The platform end to end — studio, runtime, observability, training
The studio
Design agents from a model, your knowledge bases, your tools and skills — and the guardrails they must respect. No code, and no machine-learning team in the room.
Managed runtime
Deployed for you on your own cluster, on your channels, around the clock — escalating anything that needs a person.
Observe and improve
Every session recorded and replayable. Edit a skill, tighten a rule, or train an expert model the business owns outright.
Run your agents
Far more than a chatbot — a capable digital worker. Every agent you deploy lives in the same two places. Work mode is for the people using deployed agents day to day: talking to them, handing them long work, and approving whatever needs a human.
Work mode · a day with a deployed agent
Works, not just talks
Hand an agent a task and it follows a multi-step process to completion — deciding, using your systems, delivering a finished result rather than a suggestion. If a run is interrupted it resumes where it left off.
Knows your business
Point it at your documents, your sites and your repositories. They are compiled into a knowledge base it searches and cites, so answers come from your material rather than from the model’s guesswork.
Uses your tools, and the web
Attach a ready-made toolkit, a server from your library, or any MCP server by its address. It can also drive a real browser: navigate, click, type and read a page the way a person would.
Missions, sub-agents, inbox
Long-running work graded against success criteria, delegated parts handled by sub-agents, and an inbox where approvals wait for a human.
Publish to your channels
The same agent on a hosted chat page, Slack, Telegram, WhatsApp, a widget on your site, or a pipeline over the API — every channel reaching the same agent, with the same skills and the same guardrails.
Flag good and bad turns
Every message, tool call and result is recorded and replayable. Flagging a turn is what feeds the next round of training.
Guardrails that hold
Tool access and network egress are locked when a session starts and cannot be weakened mid-task. Code runs sandboxed, credentials stay in a vault the agent never reads, and every action lands in a durable event log. SSO, RBAC, dedicated compute and an SLA come with the deployment.
Build, observe, train, redeploy
Every agent moves through the same four phases, and each one has an honest cost: editing a skill lands in minutes, fine-tuning an expert takes hours.
Build
Assemble the agent from configuration rather than code: a model, a persona, skills, knowledge bases, MCP tools and guardrails.
Observe
Every message, tool call and result is recorded and replayable in the session log. Your team flags the turns that went well and the ones that did not.
Train
The cheap way: edit a skill, a persona, a knowledge base. The expensive way: fine-tune an expert on the sessions your team flagged.
Redeploy
Promote the new version behind the same endpoint, with versioned rollback if the numbers get worse instead of better.
Every day they run, the agents your business owns get better
Build and train them
Develop mode is where agents are designed, given a model, trained on your own work, evaluated and governed. Same platform, same project, one session log.
Develop mode · a walkthrough of the studio
Deploy any model
Pull one from Hugging Face by repository and revision, use a model already in your hub, point at OpenRouter, or bring any OpenAI-compatible endpoint.
Datasets from your own sessions, or generated
Filter flagged conversations into training pairs, upload your own files, import a repository — or design synthetic data column by column, with a teacher writing and a judge scoring.
Four ways to train it
Supervised fine-tuning, preference optimization, reinforcement against a reward environment you write and version yourself, or distilling a teacher’s distribution into a smaller student.
Your models, your GPUs
Serve open-weights models with quantization, autoscaling and versioned rollback — or bring your own endpoint per agent, on your own provider contract. Runs execute on whatever compute you attach, from the appliance in your rack to your own cloud.
Prove it got better, then keep it
Score against the built-in benchmarks, or a custom suite built from your own failed sessions, and read the pass-rate drift against the base. A/B two candidates and promote the one that won. Every model, dataset and run lands in your own hub as a versioned repository.
Your own work, distilled into experts no one else can build
Fine-tune a small open-weights model on traces only your business has. On a narrow task it matches a frontier model — at a fraction of the latency and the cost.
Capture production traces
Every session — prompts, tool calls, approvals, outcomes — is logged with full traces, versioned per agent.
Curate the dataset
Traces become training data: filtered for success, deduplicated, labeled and split, with lineage tracked in the Hub.
Distil into an expert
Fine-tune a small base model on your data — supervised first, then GRPO or DPO to reward the outcomes you actually want. LoRA or full.
Quantize, serve, repeat
Quantized and served behind the same gateway, the expert takes the high-volume work — and keeps logging traces for the next round.
The expert is smaller, runs on hardware you control, and never leaves your perimeter.
You describe the work, it does it for you
The Copilot drives the platform itself. It creates the agent, attaches the skill or the knowledge base, scales the deployment, starts the training run — and answers questions about what ran yesterday. Most of what this page describes clicking through, your team can ask for instead.
No forms, no config files
Ask in plain language and the Copilot calls the platform’s own tools — the same ones the API exposes.
It answers questions too
What ran yesterday, which sessions were flagged, how the expert your team trained is performing this week.
Bounded on purpose
Deleting anything or starting a training run needs a confirmation. The builder’s view lists every operation it exposes — and what it deliberately will not do.
Destructive or expensive operations show a confirmation card naming the exact resource first.
See what gets built on it
Six patterns, one platform pointed at six different parts of a business. Each is a deployment shaped by the constraints that come with it — the data boundary, the compliance regime, the approvals that have to hold.
Enterprise process automation
Agents that run a workflow end to end — document processing, extraction, routing, CRM updates, compliance reporting. Every step traced, every decision attributable.
Domain expert agents
Legal, finance, healthcare, engineering. Trained on your domain’s language, precedent and obligations rather than on an average of the internet.
Customer-facing AI products
Agents embedded in the applications your business already ships — support, sales assistance, onboarding. Same governance, same traces, same cost controls as everything behind them.
Sensitive data environments
Inference and fine-tuning on data that cannot leave the building. On-premise deployment, with full air-gap capability where the classification demands it.
Multi-department AI at scale
Legal, HR, marketing, finance and operations, each with isolated deployments, budgets and permissions — administered centrally from one platform.
Regulated industries
The audit, traceability and data governance obligations of financial services, healthcare and the public sector. Built into the platform, not bolted onto it.
None of these needed a line of code, or a machine-learning team of your own.
Ready to put agents where the consequences are real
Bring a workflow that matters and the constraints that come with it. We will design a deployment that fits your data, your compliance perimeter and your team.
- Deploy on AWS, GCP, Azure, Oracle Cloud, or on-premise
- Enterprise security, RBAC, full audit trail
- Agent lifecycle management from day one