I'm Considering Building This Myself
You Can Build It. But Should You?
Your technical team deserves better than rebuilding commodity infrastructure. Start with a proven foundation, then extend, query, and customize to your heart’s content.
Let’s have an honest conversation
We Respect the Builder Mindset
We get it. You have talented engineers. You’ve built data infrastructure before. You know exactly what you want, and you’re confident you can deliver it.
We’re not going to tell you that you can’t build this yourself. You absolutely can.
But here’s the question worth asking:
Should your best engineers spend 6-12 months building (and then having to maintain) data pipelines and compliance frameworks, or should they spend that time building the ML models, custom analytics, and competitive advantages that actually differentiate your business?
Where Is Your Competitive Advantage?
Build vs. Buy
The tools to build a data warehouse are readily available. Snowflake, Databricks, dbt, Fivetran; the technology exists, and your team is capable of using it.
Every company faces building vs. buy decisions. And the smart way to make that decision is to ask: Where does our competitive advantage actually come from?
Here’s the truth: Your competitive advantage isn’t in building data pipelines. It’s not configuring integrations or creating compliance frameworks. Those are table stakes, necessary, but not differentiating.
Your competitive advantage is in what you build on top of the foundation:
The proprietary ML models that predict risk better than your competitors
The custom analytics that give your team insights no one else has
The member experiences powered by data that create loyalty
The speed at which you can act on opportunities in the market
The foundation itself isn’t the advantage. Getting to the advantage faster is.
When you spend 12-18 months building infrastructure, you’re not building competitive advantage, you’re building prerequisites. Your competitors who started with a proven foundation are already deploying the analytics that differentiate their business.
What You’re Actually Signing Up For
The Real Cost of Building
Integrations
Your core system, LOS, CRM, card processor, compliance tools, each with different APIs, data formats, and update schedules. We’ve built integrations for FI-specific systems. How many will you need to build?
Data Models
Purpose-built financial services schemas for loan-level detail, portfolio rollups, compliance reporting, and full historical tracking. Refined across hundreds of real-world implementations, these models bake in hard-won best practices, so you’re not starting from scratch. Build it yourself and you’ll spend cycles re-learning the same lessons, and shipping a “version 1.0” while we keep improving ours.
Ongoing Maintenance
Security patches, vendor API changes, regulatory updates, performance optimization. This isn’t a one-time build, it’s a permanent commitment.
Gestalt is your advantage.
Not because we build the foundation for you, but because we get you to your competitive advantage faster. Your team starts building what matters 90-days after receiving your data, not day 365.
And here’s what nobody talks about: turnover. That senior engineer who knows the whole system? Statistically, they’ll leave within 18 months. Then what?
A Different Approach for Builders
Start with a Proven Foundation. Extend from There.
What if you could skip the commodity foundation infrastructure and start with a proven foundation, but still have full flexibility to extend, query, and customize?
The Gestalt Foundation™ for Technical Teams:
Full Data Access
Query the Snowflake warehouse directly. Write SQL. Build views. It’s your data.
API Access
Integrate with your existing tools and workflows.
Extensible Architecture
Add custom data sources, build new models, create ML pipelines.
Clean Foundation
Unified, documented data ready for your team to build on.
You get 6 months back. Your engineers start building competitive advantages on day one instead of data plumbing. And you start leveraging the elusive AI you keep getting asked about.
Honest Questions to Ask Yourself
Before you commit to building, consider:
Is building a data foundation a core competency you want to develop, or a distraction from your actual business?
What happens when your lead data engineer leaves?
In 18 months, would you rather be optimizing data pipelines or optimizing your ML models?
What’s the opportunity cost of your best engineers spending a year on infrastructure?
Proof it Works
Instead of spending an hour every day on investor reports, I was able to automate all of them with Gestalt. We’ve also saved money on expensive consultants by having Gestalt provide files for our system conversion in a day.
We built a dealer visit optimization model, using our data, and brought it through to Gestalt’s dashboards, which show the U.S. and our dealer and Sales team distributions. From there, we heat mapped how to optimize our Sales visits across the country.
Gestalt is amazing! I’m trying to go slow and steady, releasing to my business. But I can’t because once someone sees it, they ask why they don’t have it yet. They really want to go quickly with what they see, because they’re so excited.
Gestalt increases our productivity and our bandwidth without us having to hire another person, which I think is a huge strength.
We were going live as a startup lender, doing it ourselves. Gestalt’s data validation process was actually quite helpful in identifying issues in our underlying systems and getting our data and process fixed. They’re our source of truth now.”
Gestalt has aligned its data infrastructure. They do all the mapping behind the scenes. So, if I have a platform and go to replace it, they have the processing already built into their platform to map the same identical fields from the new data source. That’s worth its weight in gold.