Our approach
Diagnose, quantify, design, deliver
The same four stages, every time, regardless of whether the answer turns out to be an AI agent, a better website, an integration, or nothing new at all.
Diagnose
Observe how the work happens, not just how it's documented.
- Talk to the people doing the work, not the people managing it
- Watch the workflow directly wherever that's practical
- Note every handoff, wait, and workaround. Workarounds are usually where the real story is
- Separate the process from the tool. A bad process on good software is still a bad process
Quantify
Estimate the time, cost, risk, delays, and customer impact, conservatively.
- Estimate hours lost per week, using real numbers from the people involved
- Translate hours into cost using your actual wage or opportunity cost
- Account for downstream effects too: errors, delays, and customer impact, not only raw hours
- Round down, not up. A credible conservative number beats an impressive inflated one
Design
Select the smallest responsible solution that can produce the outcome you need.
- Start from build, buy, improve, integrate, automate, or do nothing, in that order of humility
- Prefer configuration of what you already own over new software
- Scope AI in only where it's the best-fit tool for a well-defined task
- Design for your team's actual technical capacity, not an idealized one
Deliver
Build quickly, document clearly, train thoughtfully, and avoid creating new overhead.
- Ship in phases where possible, so value shows up before the engagement ends
- Document decisions and setup clearly enough that your team, or another vendor, could maintain it
- Train the people who'll use the system, not just the person who approved the budget
- Flag the maintenance reality up front. Nothing ships pretending to be maintenance-free
Diagnose before prescribing
We start with the work, not the tool
A lot of AI and automation vendors start with what they sell. We start with where your work gets stuck. The recommendation follows from that, whatever it turns out to be.
The generic approach
“Here’s the tool we sell.”
- Starts from a platform, then looks for a use case to justify it
- Assumes AI or automation is the answer before understanding the question
- Success is measured by whether you adopted the platform
The Snag Labs approach
“Show us where the work gets stuck.”
- Starts from the actual workflow, observed directly
- Recommends the smallest responsible fix: AI, software, or neither
- Success is measured by hours and money returned to you
See it applied