Solaris started with a simple frustration: AI keeps promising to transform business operations, but it can't even figure out how many active customers you have.
Every SaaS tool your company uses has its own data model, its own vocabulary, its own definition of basic concepts. "Customer" in HubSpot isn't the same as "customer" in QuickBooks.
These aren't bugs — they're design decisions each tool made independently. But they add up to a problem nobody owns: your business data doesn't speak a common language.
For years, companies worked around fragmented data with manual processes. Somebody on the team knew which spreadsheet had the right numbers.
AI breaks that workaround. AI agents need to reason across systems autonomously — and they can't call Sarah in accounting to ask which customer list is the real one.
Every company will need to solve this in the next 18 to 24 months. AI adoption isn't slowing down — but AI's effectiveness is capped by data quality.
The companies that structure their data foundations now will compound their AI advantage. The ones that wait will keep cycling through failed pilots.
Solaris sits at the intersection of consulting and infrastructure. Today, we work hands-on with companies to map their data landscape and resolve conflicts.
That cross-company pattern library — the catalog of how real businesses' data diverges — is becoming something larger. A semantic infrastructure layer any AI agent can query.
I started Solaris because I was living the problem. Running Switch, a healthcare staffing platform, I dealt with semantic fragmentation every day — credentialing data that didn't match across systems, compliance records that meant different things in different tools.
Before that, I founded Thrive Partners, a consulting firm where I learned the most valuable thing you can give a business isn't a strategy deck — it's a clear map of how their operations actually work.
My technical background includes building fraud detection systems using deep learning, architecting cloud data pipelines, and years of hands-on data analysis. I've seen firsthand how the gap between what systems store and what they mean is the root cause of most operational dysfunction.
Whether you're an ops leader tired of failed AI pilots, or a founder who knows the data isn't ready — let's talk. 30 minutes, no pitch, just diagnosis.
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