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How we work

Understand the work. Then build the system around it.

An engagement starts with the people, information and decisions involved. We make the intended change concrete before choosing the software that will deliver it.
Start a projectFollow an example ↓
Current workPeople and systemsBuild boundaryCustom behaviorRules · state · interfaceExisting toolsHuman reviewObserveTest and measureThe scope follows the work.
Illustration · The workflow defines the build boundary. Review and measurement stay part of the system.

A useful engagement leaves a trail you can follow.

The depth depends on the problem. A straightforward integration may need a short working session; a custom operations platform needs more discovery. These are decisions to work through, not a fixed package of workshops.

  1. Understand the work as it happens.

    We walk through the current process with the people doing it: the incoming request, the information they check, the tools they use and the exceptions they work around. A spreadsheet or an email thread can be more revealing than a feature list.

    Working artifact
    Current-state workflow, systems and owners.
    Your review
    Have we described the real process, including the awkward parts?

  2. Define the change and its boundaries.

    We describe the future workflow, what stays manual and how you will recognize that the system works. Dependencies and unknowns remain visible. Scope includes what the system will not take responsibility for.

    Working artifact
    Future-state map, scope and acceptance criteria.
    Your review
    Is this the change worth building?

  3. Choose what to integrate and what to build.

    We check where existing products fit, where custom behavior is necessary and which system owns each important record. AI gets a defined task, an output check and a fallback. Meaningful tradeoffs are written down.

    Working artifact
    Architecture, data ownership and decision notes.
    Your review
    Are the costs, dependencies and operating responsibilities understood?

  4. Build a complete path, then exercise the exceptions.

    We connect the interfaces, state and downstream actions into a working slice. Reviews use the actual system. We test duplicate events, unavailable integrations, invalid output and human review alongside the intended path.

    Working artifact
    Working software, review environment and test results.
    Your review
    Does it behave correctly when the expected path breaks?

  5. Put it into operation with a way to see what happens.

    Before launch, we agree on access, deployment, recovery, documentation and ownership. Instrumentation makes failures and delays visible. Further work follows observed needs and an agreed scope.

    Working artifact
    Handoff, runbook, monitoring and an improvement backlog.
    Your review
    Can the people responsible operate it after launch?

One request makes the decisions tangible.

This constructed example follows a service inquiry. The same questions apply to approvals, onboarding and other work that crosses systems.

Request R-104

Current state
Source
Web form
Saved in
Inbox
Owner
Not recorded
Next action
Someone checks the inbox
Reporting
Submission counted

The request exists. Its owner is unclear.

A successful form submission says nothing about who will handle it. The business is relying on a person to bridge the tools.

Illustrative workflow with sample data.

What the system should make observable

  • Every captured request has a saved identifier.
  • Assignment or human review is explicit.
  • A failed downstream action can be found and retried.

The architecture follows the responsibility.

Existing platforms
CRM, email and scheduling keep the capabilities they already handle well.
Custom software
Workflow rules, data ownership and interfaces cover the parts specific to your business.
Bounded AI
An optional classification or extraction step returns structured information. Validation and business rules decide what happens next.
Human checkpoint
An ambiguous request goes to a named review queue with enough context to decide and resume.
Reference architecture · Illustrative decisions, not a fixed stack or a client implementation.

Measure the capability, the system and the AI task separately.

  1. 01

    Business capability

    Can the team see an owner and the next action for every request? Define success against the work being changed.

  2. 02

    System health

    Can we detect a failed handoff, see how long it waited and recover without repeating a side effect?

  3. 03

    AI task quality

    Does extracted or classified information pass the task's checks? Track errors and correction paths where AI is actually used.

A successful API call is only one signal. Acceptance criteria should describe what the business can now do, and operating checks should show when that capability is at risk.