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Agents that do the work.

We design and build agent systems that plan, use tools, remember what matters and hand back results you can check — deployed on the models and infrastructure you choose.

Discuss a workflow

What we build.

EventScheduleRequestScoped memorysession, project and documentsOrchestratorplans, delegates, retriesTool gatewaypermissions per agentWorker agentWorker agentWorker agentModel routerby data policySelf-hosted modelPrivate cloud modelSovereign APIMCP serversInternal APIsFunctionsHuman checkpointapprove before actionpermission checkResultEvaluation and logs, at every stepEventScheduleRequestScoped memorysession and projectTool gatewayMCP, APIs, functionsOrchestratorplans, delegates, retriesWorkerWorkerWorkerModel routerby data policySelf-hostedPrivate cloudSovereign APIHuman checkpointapprove before actionResultEvaluation and logs, at every step
  • Orchestrator

    Agent orchestration

    Task decomposition, planner and reviewer roles, retries and explicit stop conditions. Human checkpoints sit where a decision carries real cost, and every run leaves a trace you can inspect.

  • Scoped memory

    Context and memory

    Clear lines between what an agent sees in one run, what it keeps across sessions and what it must never retain. Retrieval over your documents and structured data, with memory scoped by user, team or workflow.

  • Tool gateway

    Tool integration

    Function calling, MCP servers and adapters for internal APIs. Each agent gets a declared tool set, read and write access are separated, and consequential actions can wait for approval.

  • Event, Schedule

    Automation

    Event-driven and scheduled workflows built from resumable steps, so a failed run picks up where it stopped instead of repeating side effects. Logging, evaluation and observability are in from the first prototype.

  • Model router

    Sovereign AI and API integration

    Provider-agnostic integration with model APIs pinned to the providers and regions you approve. Routing sends each request to a model cleared for that class of data, with fallbacks inside the same boundary.

  • Self-hosted model

    Self-hosted AI

    Open-weight models served on your hardware or private cloud, including environments with restricted network access. Model size, quantization and serving setup are weighed against the hardware you have.

Every step has a boundary.

One agent run, from request to result. Scope, permissions, supervision and recovery are designed in, not added after something goes wrong.

  1. Task

    A request arrives from a person, a schedule or an event, with a goal and a definition of done.

    Scope

    What the agent is allowed to attempt.

  2. Context

    The agent loads only the documents, records and memory scoped to this task and this user.

    Memory

    What it can see, and what it may keep.

  3. Plan

    The work is broken into steps, each tied to a tool or model call, within a time and spend budget.

    Limits

    Step, time and cost ceilings.

  4. Act

    Tools run through declared permissions. Reads proceed; writes and external actions can wait for approval.

    Permissions

    Granted per tool and per action.

  5. Check

    Outputs are validated against schemas, tests or a reviewer before they count as done.

    Supervision

    Automated checks and human review.

  6. Recover

    Failures retry, roll back or escalate to a person, and the full trace is kept for review.

    Recovery

    Retry, resume or hand off.

Where it runs is a design decision.

Sovereignty is a set of choices: which models, which providers, which regions, which hardware. We make those choices explicit early, build to them, and document the result so your team knows where data and inference live.

Model weights
Hosted by a provider you approve, or open-weight on your own hardware.
Prompts and context
Where requests and retrieved data are processed.
Memory and logs
Where state, traces and transcripts are stored, and for how long.
Network egress
Which outbound connections the runtime is allowed to make.

What goes in, what comes out.

You bring

  • A workflow, with examples of the work done well
  • Access to the systems, APIs and data involved, or realistic stand-ins
  • Constraints on models, providers, regions and hardware
  • Someone who can judge whether a result is right

You receive

  • Source code and deployment configuration in your repository
  • Evaluation sets built from your real tasks
  • A tool and permission map for each agent
  • Runbooks for monitoring, limits and failure handling
  • A record of where models, data and logs run

Scoped, built, handed over.

  1. Scope

    Pick one workflow, define done, and map the data, tools and approvals it involves. Model and hosting constraints are settled here.

  2. Build

    Prototype against real tasks, measure against an evaluation set, then harden orchestration, permissions, memory and recovery.

  3. Deploy

    Ship to the environment you choose, with monitoring, spend and rate limits, and a clear path for a person to take over.

  4. Hand over

    Source, runbooks and evaluation sets your team can run, extend and audit.

Have a workflow an agent should own?

Describe the task, the systems it touches and where it needs to run. An example of the work done well helps most.

Describe your workflow

Opens your email app.