30DAY OPTIMIZATION
Build. Launch. Observe. Refine.
The promise applies to the written scope, acceptance criteria, client responsibilities, and production conditions agreed before launch.
Responsible risk reversal
Launch with a clear 30-Day Optimization Promise.
After launch, Chatoner monitors the delivered system for 30 days and refines the workflow at no additional build fee so it performs according to the agreed implementation scope. The promise reduces delivery risk without making unrealistic revenue claims.
Scope-backedObserved in productionNo revenue guarantee
What the promise means
A professional delivery commitment—not a marketing shortcut.
The purpose is to remove uncertainty around early production behavior. It does not convert an operational implementation into a guaranteed investment return.
Optimization within the agreed implementation scope.
- Thirty calendar days of post-launch monitoring for the agreed workflow scope
- Review of workflow runs, failures, retries, approval handoffs, and integration behavior
- Reasonable refinements to prompts, routing rules, validation, alerts, and documented edge cases
- Correction of implementation defects attributable to the delivered Chatoner build
- A launch baseline and optimization summary showing what changed during the period
- Staff clarification and handover support for the delivered operating procedure
Outcomes controlled by the market, client, or third-party providers.
- A guarantee of revenue, conversion rate, cost savings, customer behavior, or commercial outcome
- Unlimited redesign, new integrations, new departments, or workflows outside the agreed scope
- Third-party provider uptime, API behavior, price changes, account restrictions, or policy changes
- Results affected by incomplete data, delayed access, unapproved copy, or client-side process changes
- Professional legal, medical, employment, credit, accounting, or regulated advice
- Continuous 24/7 support unless a separate service level is written into the agreement
Launch monitoring
The first 30 days are managed as an evidence-driven operating period.
Changes are based on observed system behavior, agreed business rules, and measurable acceptance criteria—not subjective prompt tinkering.
Controlled release
The agreed workflow moves live with monitoring, named owners, escalation rules, and a documented baseline.
Observe real operating behavior
We inspect runs, edge cases, handoffs, and adoption without expanding scope prematurely.
Refine the workflow
We tune logic, prompts, validations, notifications, and exception handling where the evidence supports a change.
Stabilize and hand over
We confirm the operating procedure, record remaining recommendations, and summarize the optimization period.
Refinement process
Every adjustment should trace back to a real condition.
We distinguish a defect, an optimization, a new requirement, and a third-party incident so the client always knows what is being changed and why.
01
Compare with acceptance criteria
Confirm whether the observed behavior differs from the documented trigger, output, timing, approval, error, or reporting requirement.
02
Classify the issue
Implementation defect, expected edge case, data quality issue, provider incident, client process gap, or new scope.
03
Apply the smallest reliable correction
Update the relevant rule, prompt, mapping, validation, retry, notification, or operating instruction.
04
Retest and record
Verify the scenario, document the change, and maintain a clear launch optimization log.
01
AssessAudit operations
02
ProveValidate a quick win
03
BuildDeploy the agreed scope
04
Hand overOperate and support by scope
What success looks like
Performance is assessed against agreed operational measures.
The exact measures depend on the workflow. They should be written before build and visible after launch.
Reliability
Run success, retry rate, unresolved errors, uptime, and recovery behavior.
Operational result
Response time, records updated, follow-ups completed, tickets routed, or hours redirected.
Human control
Approval speed, escalation quality, staff adoption, and clarity of ownership.
Maintainability
Documentation quality, monitoring coverage, provider dependencies, and change procedure.
No revenue guarantee. Chatoner can implement and optimize the agreed system. It cannot control market demand, customer decisions, staff behavior, data accuracy, third-party services, or the client’s sales and operating execution.
Reduce implementation uncertainty
Begin with a clear workflow, baseline, scope, and definition of done.
The AI Operations Audit identifies the process, risk, owner, data, integrations, measures, and approval boundaries before a larger build begins.
