Workspace
Where your team and agents
do the work, side by side
Pull the platform triage board.
Three under triage, two in repair, one verified. Momentum factor decay is the top item; FactSet schema drift is downstream of it.
Workspace is where people and agents complete tasks together, on the same files in the same environment. Runbooks define the work in plain English, and every action becomes a trace your team owns.
Plain-English runbooks
One surface for people and agents
A trace on every run
Side by side
People stay in control
A person and an agent work the same task in the same place. The agent does the legwork; the human approves the calls that matter.
- Live cursors and shared files, so a handoff is a glance, not a re-brief.
- Sensitive actions wait for a person; routine ones run on their own.
- Every step is on the record, so review is reading, not reconstructing.
Reviewing 12 strategic accounts to flag ARR at risk this quarter.
Built for work, not just chat
The surface carries the whole job, from the runbook that defines it to the trace it leaves behind.
Plain-English runbooks
Anyone can describe a workflow in plain English. It becomes a durable team artifact others can run, inspect, and improve.
A computer per agent
Each agent gets its own sandboxed environment to run commands, navigate files, and execute code, not just a chat box.
The same files
People and agents work the documents, sheets, and decks together, with a clean handoff in either direction.
Any model or agent
Claude, GPT, Gemini, Kimi, or open weights. Bring your own agent framework, or use ours.
A trace on every run
Every action is captured as a complete, replayable trace, so the work is auditable by the team accountable for it.
Applets for the task
Agents generate a fit-for-purpose interface for a workflow, so the team works in the view the task needs.
Model-agnostic
Use any model, switch anytime
You are not locked into one AI model. Pick the best one for each task, and routine work runs on the cheapest model that still does it well.
- Claude, GPT, Gemini, Kimi, and open weights, side by side.
- Per-task model choice, with the run recording which model handled it.
- Swap models without rewriting the runbook the team relies on.
Tighten step 4. The vendor-schema check wasn't catching column-type changes.
Drafted the change in the right pane. Adds a type-equality assertion before imputation. Save & redeploy when you're ready.
Built for production work.
Run anywhere.
Hosted. Your VPC. Air-gapped. The on-prem Context appliance.
Use any model or agent.
Claude, GPT, Gemini, Kimi, or open weights. Bring your own agent framework, or use ours.
Enterprise-grade authorization.
Identity through your IdP. Customer-managed keys. Audit on every action. Permissions inherited at every connector call.
A complete working environment.
Documents, spreadsheets, decks, kanbans, and file viewers built in. Your team and agents work on the same files in the same environment.
Faster, cheaper, better
Custom models trained on your work
Your team's accepted outputs become training data for models you own and serve, and they beat general-purpose agents on your specific tasks.
Evals gate every change
Rubrics and golden sets validate every runbook, model, and context change against past work before it ships. Regressions are caught automatically.
Step-level model routing
Each step routes to the cheapest model that clears your rubric. Frontier models handle only the genuinely novel, so cost falls without losing quality.
Talk to us.
Bring a workflow your team runs today and see it run in your environment.