Seven stages.
Understanding comes first.
The Fresh Eyes 7D methodology, adapted for digital and AI implementation. The same discipline on both sides of the practice — here it ends in a working system rather than a recommendation.
- 01
Discover
Understand the work.
We sit with the people doing the job. What triggers the work, what information they need, what they do when the system doesn't cover the case. No technology discussion yet.
What you getA shared, written description of how the work actually happens today.
- 02
Diagnostics
Map processes, systems, data, friction, and constraints.
Process maps, information flows, system inventory, failure modes, and the places where duplicate entry, waiting, and rework are consuming hours.
What you getA process and systems map with friction and constraints identified.
- 03
Determine
Choose what should change and where technology creates leverage.
Some steps should be removed rather than automated. Some need a policy change, not software. What remains gets prioritized by leverage, effort, and risk.
What you getA prioritized recommendation with architecture direction and build/buy calls.
- 04
Deploy
Build and integrate the solution.
Design, build, integrate, and test against real data and real edge cases. Review points, fallbacks, and audit trails are part of the build, not a later phase.
What you getA working system integrated into the existing environment.
- 05
Drive
Launch, train, iterate, make it part of operations.
Adoption is where most projects quietly fail. We launch with the people who will use it, train in their context, and fix what the first two weeks expose.
What you getA system in real use, with the early friction resolved.
- 06
Document
Capture architecture, workflows, knowledge, and ownership.
Architecture, data model, workflow logic, integration points, and who owns what. You should be able to hand this to another developer and have them continue.
What you getDocumentation and a named owner for every part of the system.
- 07
Discuss
Review performance and choose the next improvement.
What the instrumentation shows, what people worked around, and what the next highest-leverage change is. Improvement is a cycle, not a launch date.
What you getA reviewed system and a decision about the next cycle.
The ones we get asked most.
Why start with process instead of just building what we asked for?
Because software encodes whatever process you hand it. If we build exactly what's requested without understanding why the request exists, we make today's friction permanent and expensive to change. Discovery is usually the shortest phase and the one that saves the most money.
How long does this take?
Discover and Diagnostics typically run one to three weeks depending on the size of the operation. Build timelines depend entirely on scope and are defined — with a fixed deliverable — before implementation starts.
What if the answer is that we don't need to build anything?
Then we say so. Removing a step, changing an approval, or using a system you already pay for is frequently the better answer. That outcome makes us more useful the next time, not less.
Do you work with our existing developers or vendors?
Yes. In many engagements the most valuable role is translation — turning business need into a clear system requirement that your team or vendor can execute against.
What happens after the system launches?
Document and Discuss are part of the engagement, so you leave with architecture documentation and named ownership. Ongoing improvement work is available for systems we've built.
What if we don't know what our real problem is?
Start with Fresh Eyes Consulting. That side of the practice exists to diagnose the operating problem before anyone decides what to build.
Start with the work, not the tool.
Bring us a messy process, a system that should exist, or a question about where AI actually fits. First conversation is about fit — nothing else.