PASS
The target is met.
Accept the slice and decide what to harden or expand.
Get repeat work
off your team’s plate.
Give your people more time for customers, decisions, and work that needs their judgment. We build and test one AI workflow, then measure whether it helps.
One workflow.
One owner. One metric.
Agree on a measurable problem, build one production slice, and decide whether the evidence supports going further.
WEEK 1
Define and baseline
Choose one workflow and owner. Use representative client data to agree on the baseline, target, permissions, and acceptance criteria.
You getA one-page Success Contract and an initial evaluation set.
WEEK 2
Build the working slice
Connect input, processing, decision, and output. Use the smallest architecture that can prove the workflow.
You getOne end-to-end system with clear human approval points.
WEEK 3
Test against real work
Run historical and current examples. Check accuracy, exceptions, cost, processing time, and human review time. Test failure and escalation paths.
You getMeasured results, repeatable evaluations, and a record of what fails.
WEEK 4
Run and measure
With client approval, run against incoming work with access controls, logs, cost monitoring, and human escalation. Compare the result with the baseline.
You getA pass, learn, or stop decision based on the evidence, plus a documented handoff.
We agree on the start date once the workflow, owner, representative data, and access are ready. The four-week scope and any dependencies are documented before work starts. A missed target can lead to a stop decision; results are not guaranteed.
What we agree on before building
No meaningful build begins until the Success Contract defines:
- Workflow and owner
- The exact process, its boundaries, and the accountable client and Hiatt Co owners.
- Baseline and target
- What happens today, the desired change, and the quality threshold that must hold.
- Measurement and data
- The sample, data source, measurement window, and calculation both parties will use.
- Authority and approval
- What AI may do, what a person must approve, and how exceptions escalate.
- Acceptance and stop rules
- What counts as pass, learn, or stop, and who makes that decision.
Example: reduce invoice handling from 18 minutes to below 8 while maintaining an agreed accuracy rate. This is an illustrative target, not a client result.
Download the Success Contract templateLEARN
Useful, but short.
Explain the gap and decide whether another scoped iteration is worthwhile.
STOP
Further work is not justified.
Document what we learned and stop the investment.
A repeat task.
A measurable problem.
For businesses with established processes and limited internal AI expertise.
A good starting point
- Work repeats daily or weekly.
- One person owns the process.
- You can describe the current time, cost, or error rate.
- Representative examples and approved access are available.
- Your team can review a narrow production experiment.
If there is no owner, usable data, or way to measure success, we resolve that before building. An uncontrolled high-risk decision is not a first workflow.
Where could AI help?
Illustrative starting points. We qualify each workflow and agree on access, review, and scope before building.
Revenue
Give sales a clear next step for each inquiry. Prepare lead briefs, proposals, and follow-up drafts for review.
Finance
Put attention on invoices that need a decision. Match records and prepare collections follow-ups for review.
Operations
Keep work moving when documents arrive. Extract details, check them, and route exceptions to the right person.
Knowledge
Help people find the answer they need. Search approved company information and show the supporting sources.
Management
Start the day knowing what needs your attention. Prepare priority briefs from approved email, calendars, and reports.
Technology
Focus your team on issues that need their judgment. Triage support requests and gather evidence for troubleshooting.
Explore interactive workflows and familiar tools
How we define an AI role
Give each agent one clear job. An invoice matching agent and an invoice exception agent have different responsibilities, tools, and checks.
Role → Input → Knowledge → Tools → Authority → Output → Evaluation → Escalation
When independent review adds value, give reviewers different jobs: challenge assumptions, check sources, or flag risk. Keep final approval with the named human owner. Agreement between agents does not prove correctness.
Your system.
Your choice of support.
Keep running and improving the system with your own team or a provider you choose.
Where practical, we build in your cloud account, Git repository, identity system, and vendor accounts.
You own the project source code, prompts, agent instructions, evaluations, infrastructure definitions, architecture, and documentation. Your data stays yours. Third-party software and services retain their own license terms and fees.
Handoff includes access, operating instructions, failure recovery, and a named system owner. Continued Hiatt Co support is optional.
Clear limits.
Evidence at every step.
One workflow first.
No second use case until the first is accepted.
Start with real data.
Representative client data enters the process in week one.
Define success before writing code.
Agree on one baseline, one target, and one measurement method.
Build for production.
A successful demo is not a successful implementation.
Keep humans where judgment matters.
Increase autonomy only when measured performance supports it.
Client ownership from the start.
Use client accounts where practical and hand over the project assets.
Keep the delivery team small.
Use automation to reduce implementation work and cost.
Protect your experts’ time.
Ask for focused validation and decisions.
Measure every release.
Use evaluations and business metrics to decide what improved.
Be willing to stop.
If the economics, data, or reliability do not justify more work, say so.
Expand what works.
Accept the first slice before adding more use cases. Each next step has its own scope and written quote.
PRODUCTION EXPANSION
Build on the result.
Add integrations, users, edge cases, availability, security, or further workflows when the first result supports it.
OPTIONAL AI OPERATIONS
Keep the system useful.
Month-to-month support can cover monitoring, evaluations, model changes, cost reviews, security updates, and incident support. We agree on coverage and response expectations in writing.
Find the next
useful opportunity.
Choose what to handle yourself, what to give an assistant, and what is worth building into a system. A private workshop can capture those choices in an AI Opportunity Map.
Start with what repeats, what gets missed, where people move information manually, and what has a clear measure of good versus bad. Then qualify one workflow.
Download the Opportunity Map templateWhat could your team
do with that time?
Start with one process that keeps pulling them away. Tell us what happens today and what a better workday would look like.
Start with a scope conversation. Share a process description, not confidential records.