Case studies
Consider a painting company with ten employees: estimators, crew leads, and someone on the phone who is also supposed to remember which customer hated semi-gloss in 2022. Their answers live in SharePoint, email, texts from the job site, and notes nobody filed. You are not watching a software demo. You are watching what happens when scattered work finally has memory.
01 · Manual capture
Someone types the truth after a long day
A job note lands at 6pm. The color change is in a sentence, not a CRM field. That is still memory — if the system catches it.
02 · Auto capture
New files and threads become objects with receipts
Estimates, change orders, and crew texts stop disappearing into static. Each one gets structured privately and hashed so you can trust the trail later.
03 · MI™ Search
One question across the places work already lives
Q: What color did we use on the Henderson exterior?
04 · Before the callback
The rep sees the answer, not ten blue links
Mindful Gray SW 7016 · Job note + invoice line · 2 receipts · No model invented this.
Case study · narrative
Premier Coatings remembers what the crew forgot.
A composite story based on how residential painting companies actually work. Names and dates are illustrative. The failure mode is not.
Thursday morning. The Henderson job. The homeowner texts a question you have answered three times already, in three different places.
Marcus runs Premier Coatings out of a strip-mall office next to the supply house. SharePoint holds half-finished estimates. The CRM holds activity logs nobody reads. The actual color conversation lives in an email thread from March 2023, and the final spec is in a job note someone typed at 6pm after a long day on a ladder.
Step two · catch up
When they pointed MI™ at those folders, the first pass was ugly in the best way. Years of PDFs, photos, and half-scanned invoices became structured memory. Not summarized away. Receipted. The system knew that Sarah Henderson first asked for Accessible Beige, then walked it back after she saw the neighbor’s siding. That fact had never lived in a CRM field. It lived in sentences people wrote when they were tired.
Step three · always on
After catch-up, the listener stayed on. New estimates, change orders, and crew texts stopped disappearing into static. Every new file and thread became another object with a hash. The office did not get more disciplined overnight. The memory layer did the discipline for them.
The search that should have taken an hour
The text came in while Marcus was in the truck: What color did we end up using on our exterior? Old workflow: search SharePoint, search the CRM, call the foreman, hope someone remembers. New workflow: one question in MI™ Search. Two receipts. The answer named Mindful Gray SW 7016, cited the job note, cited the invoice line for materials. No model inventing a friendly paragraph. Just memory that points to files.
Not a list of files · a story before the call
Later that week, a bigger ask. A full Johnson-family profile before a kitchen upsell call. MI™ did not return ten blue links. It returned a narrative: first contact, quote to close, who actually decides, the one boundary about call times, the five-star review, the open opportunity sitting in email from October with a dollar figure attached. Every paragraph footed to sources. The rep walked in like they had read the file. Because the file finally read as one story.
That is the second and third beat of the product in one company: catch up the past, capture the future, then search and narrate like the data always meant something.
A question worth asking
Does a painting company even need AI?
A painting company does not want AI making things up about a customer. They want facts. And the facts are already in your data.
MI™ Search finds them without inventing anything.
What a painting crew actually needs to know
What color did we use on the Henderson exterior last time? They want to touch up a section and cannot find the invoice.
What did we quote the Riverside job at before the scope changed? The customer is disputing the final number.
Which crew was on the Martinez job in March and what notes did they leave? The customer is calling about something that happened during the job.
How many square feet did we estimate for the Thompson house and did we account for the garage doors?
What did the customer say when they requested a callback last fall? Nobody wrote it down but someone definitely talked to them.
None of these questions need AI. They need facts.
The answers already exist. A job note. An invoice. An email. A text from the job site. MI™ structures it and makes it searchable.
Zero fabrication. Not a fluent guess that sounds right. The actual color code, from the actual invoice, traced to the actual source file with a receipt.
No model required. MI™ Search works without an LLM. Add a conversational layer later if you want one. The facts are already there without it.
See what MI™ actually returns
Receipted narrative
Not a search result. A story about your customer.
This is what it looks like when you ask MI™ for everything about a customer before you call. Not a list of files. A brief, with every source attached.
Johnson Family · Full customer profile
MI™ RECEIPTED
Generated from 6 sources · 8 receipts · Last updated Oct 12 2024
First contact
Tom Johnson reached out on March 14, 2023, after a referral from Mike Hendricks. Bathroom remodel. Scope and budget discussed in the first thread.
PROJECT DURATION
Apr 3 — Apr 28
What to know before you call
Sarah is the decision-maker. Do not call after 4pm. Five-star review posted May 2023. They care about punctuality and clean edges.
The kitchen opportunity
Follow-up in August 2024 for kitchen remodel. Rough estimate $34,000. Last email October 12. They are waiting on cabinet lead times before committing.
For the interactive LLM vs MI™ vs both comparison (same 50-memory setup), open How it works.
Want to be the first case study? We are lining up pilot partners now. If you are building something and want to measure before and after with MI™ installed, get in touch.
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