Your method, with the context it needs
Only the information this workflow needs, with a clear owner and an agreed expectation for keeping it current.
Workflow transformation and implementation
Turn manual workflows into production systems.
Adding AI to a fragmented workflow does not repair the process underneath it.
We work with an internal process champion and the people who run the work to redesign one workflow, build the production system, and measure the result.
- Less waiting
- Move work through review and approval sooner.
- More time for judgment
- Take preparation and repeat handling off experts' plates.
- Room for more work
- Increase output without adding more manual coordination.
- Lower effort per case
- Reduce the work behind each completed case or deliverable.
Start with the work
We redesign your workflow with your team. You run it where it works best.
- 01
We redesign your workflow with your team.
Layers works with your process champion and the people who run the work.
- 02
You run it in your approved AI platform.
Workflow Launch turns the first workstream into a tested working version for Claude, ChatGPT Work, Codex, Gemini, or your internal assistant.
- 03
Use Amplio if you need one.
Amplio gives your team a place to run and share AI work.
Close to the work
The work tells us what to build.
We work directly with the people who run the process. Their real cases show us where it breaks and what the new system has to handle.
- 01
See the work as it runs
We follow a case from request to decision and watch where it slows down. The real process often lives in workarounds that no document captured.
- 02
Build for the difficult cases
We use the systems and policies already in place. Exceptions are part of the test from the beginning.
- 03
Carry it into use
The team that learns the workflow also builds it and proves it in use. Handover includes the checks and documentation your team needs.
What you get
Working systems your team can take over.
The work includes everything needed to move from today's process to a system your team can own.
AI and software, each with a role
AI handles language and context. Software handles calculations and hard rules.
A clear before-and-after
Agree the measures that matter: cycle time, quality, errors, rework, expert intervention, satisfaction, and business impact.
Your team, ready to run it
A named maintainer receives source ownership and freshness expectations, tests, controls, decision log, release history, and operating documentation.
Workflow Launch
Turn one repeatable workstream into a working AI skill or plugin.
We map how the work runs, remove unnecessary steps, and build the smallest reliable solution inside the AI environment your team already uses.
Run it in Amplio or your approved AI environment
- Amplio
- Claude
- ChatGPT Work
- Codex
- Gemini
Examples include Claude, ChatGPT Work, Codex, Gemini, and internal assistants.
Skill
Package the methodology, templates, examples, and review points inside an approved AI workspace.
Plugin
Connect files, systems, and actions, with deterministic checks around exact work.
Application
Add dedicated software only when shared state, queues, or permissions require it.
A first version can be tested in as little as two weeks. Timing depends on the workflow, data, integrations, and approvals.
See how Workflow Launch worksWhere this applies
The same problems show up in very different work.
An intake queue and a monthly report may look unrelated. Both can break when information is missing, rules live in someone's head, or approval has no clear owner.
- 01
Intake and case handling
Requests arrive through email, forms, and documents. We turn them into complete cases and send each one to the right owner. Everyone can see what is still open.
- 02
Data preparation and reconciliation
We bring spreadsheet and system data into one place. Then we apply the business rules and send gaps back to the owner before anyone relies on the data.
- 03
Document and evidence analysis
We pull the relevant evidence from source material and send uncertain cases to the expert responsible.
- 04
Deliverable production and approval
We turn a repeatable method into a production flow for reports, assessments, proposals, or other client deliverables. Review stays part of the process.
- 05
Reporting and monitoring
We collect updates and calculate the measures people use. When a change needs a decision, we flag it before the next review.
A good first workflow is frequent, painful, has a known output, a clear owner, representative examples, and a quick human review.
Discuss one workflowWorkflow opportunity estimate
Put a number on the opportunity.
Three inputs show how much team capacity a well-suited workflow could return each year.
No contact details required
Current workflow
Use the work as it runs today.
Count regular contributors, not occasional approvers.
Use an average across the people counted above.
Salary plus employer taxes and benefits. A blended estimate is fine.
Working estimate
Estimated annual capacity value
4,140 hours could move from repeat handling to higher-value work each year.
- Current workflow value
- $360,000
- Hours returned
- 4,140
- Working weeks returned
- 104
This estimates capacity, not guaranteed cash or headcount savings. It excludes implementation and model costs.
This uses the planning scenario: 50% of repeat effort returned for a suitable workflow redesigned end to end. We validate the real result against your workflow.
Method: 12 people × 15/40 of a workweek × $80,000 annual cost × 50% · Hours use 46 working weeks
Keep the inputs, result, and calculation method for your review.
How we work
Prove the result before expanding the work.
We establish the baseline first, then measure what changes. The evidence tells us where further investment will pay off.
- 01
Measure and test
We record what happens today, then run the new flow on real examples.
Did it improve the result without lowering quality?
- 02
Make it ready for production
We connect the systems and handle exceptions. Human review stays where the work needs it.
Can your team use it safely in the real environment?
- 03
See how it holds up
Once it is running, we watch the agreed measure and fix what breaks. Your team then takes over.
Is it worth expanding?
Before we start
Questions worth asking up front.
Tell us about the work
Show us the work you want to improve.
Tell us how it runs today, who owns it, and what a better result looks like. A few real examples are enough to see where AI or software can help.
START WITH THE WORK. THEN DECIDE WHAT TO BUILD.