Technology in service of outcomes
Custom AI coworkers that do real work inside the tools your team already uses. We build them, run them in production, and stay responsible for how they perform — measured in your numbers — so you add operating capacity without adding headcount.
Managed AI operations does not end at deployment.
Getting a prototype working is the beginning. Production AI needs someone accountable for it every week after that.
- 001Models and APIs change
- 002Integrations break
- 003Outputs and performance drift
- 004Permissions must be governed
- 005Edge cases and failures need handling
- 006Costs must be monitored
- 007Employees need support
- 008Workflows evolve
- 009High-stakes actions need approval
leaves you with a strategy.
leaves you with a repository.
stays responsible for operating and improving the coworker in production.
Built around decisions, not clicks.
We break a workflow into the decisions required to complete it, then rate each one on two separate dimensions: how much judgment it takes, and what happens if it is wrong. Clerical-looking work can carry high stakes; some expert work is safe for AI to prepare.
Runs independently
The coworker has the context and the permission, and the action is safe or recoverable.
Drafts for approval
The AI prepares the work; a person reviews or authorizes it before anything happens.
Remains human
Too ambiguous, sensitive, consequential, or irreversible to delegate.
We do not chase maximum automation. The goal is autonomy that is useful, measurable, and appropriately governed.
Three ways the work can run.
Decision-level autonomy settles who owns each judgment. Combined, those decisions determine who leads the work: the coworker, the coworker with your approval, or your own people — with agents expanding what they can do.
Fully agentic
The coworker leads and runs.
It gathers context, makes the decisions, takes the actions, and handles routine exceptions. People set boundaries, watch performance, and step in when it meets something outside its authority. Right where the decisions are reliable, observable, and low-stakes.
Human-in-the-loop
The coworker leads; you approve.
The work happens in the background, and selected decisions surface for approval or judgment in the queue or inbox you already use. The question is never whether a human is in the loop — it is which decisions need one, and why.
Agent-in-the-loop
You lead; coworkers extend you.
For high-judgment work where the expert is the point. Agents research, compare, prepare, and execute bounded pieces while the person decides what matters. Sometimes the best system is a better instrument in the hands of your best people.
The same workflow can contain all three, and a process moves between them as the coworker earns a record — in either direction. Fully agentic is not the destination. The right mode is the one that produces the best outcome.
Start with one workflow.
We begin with one well-defined operational workflow where AI can create measurable value. Once it is producing, the same operating model and governance framework extends to the next one.
- 01
Start with the outcome
Establish what the business wants to be different, what prevents it today, and how success will be measured — then select the workflow that carries that constraint.
- 02
Decompose the work
Break the workflow into the individual decisions required to complete it.
- 03
Determine autonomy
Establish what the coworker performs on its own, what it prepares for approval, and what stays human.
- 04
Build and integrate
Connect it to your existing tools, systems, and data, with permissions, evaluations, observability, and approval controls.
- 05
Deploy into production
The coworker enters the real operating environment. Not a demonstration, not a prototype.
- 06
Operate and improve
Ongoing — this is the serviceMonitor performance and cost, handle failures and exceptions, support the people using it, adapt as the workflow changes, and gradually expand what the coworker is trusted to do.
Add operating capacity without building an internal AI team.
Traditional automation makes existing work cheaper. AI also makes work economical that was skipped before: the deep research only your top accounts received, the discrepancies only investigated when large, the follow-up that depended on someone remembering. That is new capacity, not just saved time.
- /High-volume or frequently repeated operational workflows
- /Experienced people spending their time on admin and coordination
- /Important processes spread across several systems
- /Backlogs, slow response times, operational bottlenecks
- /Interest in AI, no appetite for staffing an AI operations function
- /A need for custom implementation rather than generic software
Usually bought by COOs, heads of operations, revenue and finance operations leaders, client-service leaders, sales leaders, and founders responsible for scaling
- Researching accounts before sales meetings
- Updating CRM records after calls
- Drafting follow-ups for rep approval
- Preparing account plans and meeting briefs
- Processing claims or service requests
- Reconciling invoices and purchase orders
- Chasing missing supplier documents
- Routine compliance checks
- Categorizing and routing incoming requests
- Resolving routine support cases
- Escalating exceptions with a prepared summary
- Producing recurring operational reports
Illustrative workflows — not client results
Operated, not handed over.
Discovery
Workflow selected, opportunity sized, decision map created.
Implementation
The coworker is built, integrated, tested, and deployed for an agreed fee.
Managed operation
A monthly fee covering hosting, monitoring, support, governance, exception handling, reporting, and continuous improvement. Volume-based pricing where it makes sense.
The decision map, the implementation code, the prompts, and the recorded operational reasoning are yours. If you later want to run the system in-house, you can take it in-house.
What is happening in the business that you want to change?
A first call starts there — the outcome and the constraint, not a technology. From it we find the workflow, take it apart into decisions, and tell you what could reasonably be delegated. If the honest answer is nothing yet, we will say so.