Most companies that buy AI tools end up with three power users and a lot of people rewriting emails. The work that would actually pay off never gets touched, because nobody has dug out how it really gets done. Shovel does the digging first. Then it decides what to automate.
A chatbot makes a few people faster. Quoting, billing, and intake still run the way they always have, because nobody redesigned them.
How your company actually works lives in people's heads. Before anything can be automated, someone has to dig it out: the steps, the exceptions, and the judgment calls.
We sit with the people who do the work, decide together what a machine should take, and hand back a plan somebody can build. Not a rollout, and not a mandate to figure it out yourselves.
Every engagement produces two things: one workflow redesigned around what is now possible, and a working memory of how your business runs that makes the next one faster. Built for manufacturing, engineering, healthcare, finance, and other companies whose most valuable workflows have never been written down.
How the dig worksFour phases. One workflow at a time.
Should we automate this at all?
Before anyone builds anything, we answer four questions. Can the workflow be defined, start to finish? Do you have access to the data it runs on? Can that data be used with AI or automation? Is the return large enough to justify the build? You get a straight answer: proceed, proceed with conditions, or stop.
How does this actually happen today?
We sit with the people who do the work and ask them to show us, not tell us. The steps, the exceptions, the spreadsheet only one person understands. One company handed us estimating documentation that turned out to be wildly wrong, because nobody had asked the estimators. This is the part companies skip, and the reason their automations fail.
What should it look like instead?
We redesign the workflow with generative AI where language and judgment are involved, traditional automation where the rules are fixed, and the tools you already own wherever they will do the job. Then we pick the simplest solution with the best return. If a Power BI dashboard solves it, we will not recommend a custom app.
Who builds it, and how do we know it works?
You leave with a plan and a spec that your team, our team, or an AI coding agent can execute. Every requirement is written as a pass-or-fail scenario, and your stakeholders are the testers. Whoever builds it, human or agent, has to pass them.
From preparing quotes to processing invoices, AI and automation can help your team spend less time on repetitive work. Explore these examples to see where the opportunities are in your business, how each workflow could run, and where your team stays involved.
The right AI investment starts with a clear understanding of the work. We assess your workflow, data, and potential return to identify where automation can help and where human judgment matters. You get a practical recommendation before committing to a larger build.
Shovel is human-led on purpose. Interviews, judgment calls, and recommendations come from people who have done this before. Behind them, a small set of agents does the paperwork, and everything they learn is kept.
Each agent has one job. None of them talks to your team. They prepare us, keep the record straight, and learn from every engagement.
Reads everything available about your company and the workflow before a session, so we walk in with real questions instead of a blank page.
Turns interviews and working sessions into maps, requirements, risks, and decisions while they are still fresh. People review and correct everything.
Keeps the plan consistent when a decision changes. Flags what else has to move, so the spec never quietly contradicts itself.
Records what worked, what did not, and what we wish we had known, so the next workflow starts smarter.
Two layers. Your details stay yours. The patterns are shared.
Systems, people, decisions, and constraints live in your engagement and nowhere else. The second workflow starts from everything the first one learned.
Reviewed, anonymized lessons from every workflow we finish. Which quoting problems turn out to be data problems. Which approvals cannot be automated. Your problem is rarely as unique as it feels.
We dig out the real workflow, constraints, and outcomes.
Facts, decisions, patterns, and risks are written down as memory.
Memory shapes better questions, sharper specs, and earlier warnings.
Outcomes and feedback update the memory for the next workflow.
Living knowledge of your business: systems, people, processes, and decisions.
We dig out the real workflow, constraints, and outcomes.
Facts, decisions, patterns, and risks are written down as memory.
Memory shapes better questions, sharper specs, and earlier warnings.
Outcomes and feedback update the memory for the next workflow.
Step 1
$3,500fixed
About two weeks
A clear answer on whether this workflow is worth automating: proceed, proceed with conditions, or stop. If the answer is no, you spent $3,500 to avoid spending far more.
You get
Step 2
$12,000 to $15,000per workflow
About three more weeks
Phases 2 through 4. We dig out how the work happens today, redesign it around what is now possible, and hand you a build-ready package.
You get
