AI coworkers built around the decisions your business already makes.
Instrumentality translates existing workflows into the right mix of AI autonomy and human judgment — then builds, deploys, and operates the resulting systems inside the tools your team already uses.
- 09:12Prepared the account brief for the Northwind renewal call.Ran independently
- 09:24Matched 42 supplier invoices to purchase orders; 3 exceptions queued.Ran independently
- 09:41Drafted the follow-up to Ridgeline Health. Waiting on the rep to send.Drafted for approval
- 10:07A credit request above the $5,000 threshold. Escalated with a prepared summary.Stayed human
We adapt AI to your business. We do not reshape your business around AI.
Your operation already contains the knowledge, judgment, and controls that make it work. We translate that operating reality into decisions AI can participate in — without forcing a new platform, a blank-sheet process redesign, or a new way for your team to work.
- 01
Start with the operation as it is
We learn how the work actually gets done — including the judgment, exceptions, and informal context that a workflow diagram usually misses.
- 02
Translate the work into decisions
We make each decision explicit: the context it needs, the consequence of getting it wrong, and the boundary between AI autonomy and human judgment.
- 03
Put the coworker where work happens
The resulting system operates inside the tools your team already uses. The technology adapts to the operation — not the other way around.
We do not transform your business to fit AI. We translate the way it already works into decisions AI can participate in.
The workflow is not the unit of autonomy. The decision is.
For every decision, we determine whether AI should run independently, prepare the work for approval, or stay out of the way. We rate each one on two separate dimensions: how much judgment it takes, and what happens if it is wrong.
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.
Production AI requires ongoing ownership.
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.
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
Identify the opportunity
Select the workflow, establish the current cost or constraint, and define how success will be measured.
- 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.
- /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.
Bring us the workflow that is costing you the most.
A first call is a conversation about one workflow: what it costs today, which decisions it contains, and what could reasonably be delegated. We will tell you if it is not a fit.