The challenge: integration work was eating QoreAI's engineering capacity
QoreAI is a Software-as-a-Service (SaaS) and Artificial Intelligence (AI) company building a data warehousing and data mining platform for the automotive retail industry. The product lets non-technical users at car dealerships and dealer groups query their own data in natural language, run aggregations, perform hygiene and enrichment, and turn that data into actionable signals for marketing, sales, and operations.
For a data warehousing product, though, the data is only half the value. The other half is what happens after the query.
"Data on its own has almost no utility. You need to be able to action off of it in some way," explained Maarten Sundman, Chief Technology Officer at QoreAI. "And to action on it, usually that means syndication or replication to other systems. If you're doing marketing, you need to send data over to a marketing system. If you're doing business intelligence, you need to send it over to your BI system."
Before Prismatic, QoreAI had built that action layer in-house. The team had shipped 130-plus custom integrations to MarTech, AdTech, communication, and automotive API tools. The cost of operating that many integrations had become unsustainable.
About 40 percent of total engineering time was going to integration work. Each new integration took 4-12 weeks from product ideation to a live release. And 10-25 percent of recurring development cycles were spent maintaining what already existed, because, as Maarten put it, "tools and services you integrate with, they're not static. They keep on evolving and changing. No matter your team size, you'll be caught unaware of changes that have happened in those other systems."
The math eventually broke. "We got to a point where we realized we had to almost have it where we had an equal number of engineers just working on integrations as we had in our core platform, which isn't a good use of time," Maarten said. "Having something that is maybe five percent of the product taking up 50 percent of your budget and time and your primary customer support avenue, it just doesn't work or scale."
Why integrations are mission-critical for an AI-native data platform
QoreAI's customers are not engineers. They are dealership staff, sales managers, and dealer group operators who expect software to behave like a consumer app.
"With a non-technical user base, we have to make it so that our interface is as easy as possible. They're used to the app-style model of, I click a button and it just kind of magically does something," Maarten said. "If you ask them to go and configure APIs and servers to give you access to something, it's not just a non-starter. They're not going to be happy with you."
Layer in the structure of the automotive retail industry, where a single dealer group can have dozens of locations across different states and regions, each with its own services and accounts, and the integration problem becomes a hierarchical, multi-tenant one. QoreAI needed a foundation where integrations could be configured once at the corporate level and inherited down to every retail location, with room for one-off exceptions where a single store ran its own marketing or held a pre-existing vendor agreement.
The internal build was not going to scale to that.
Why Prismatic won the evaluation
QoreAI evaluated several embedded Integration Platform as a Service (iPaaS) vendors before settling on Prismatic. The decision came down to the build experience.
Most platforms QoreAI evaluated forced a binary choice: drag-and-drop simplicity that broke down on anything custom, or full custom development for everything, entirely erasing the time savings.
"What we'd found from competitors is they tended to have either very simple configuration but no option for more complicated ones, or it was all the extreme end of custom dev each time, in which case the time savings was largely negated because you ended up having to use dev time either way," Maarten said. "Prismatic seemed like it had the best of both worlds."
In Prismatic, common configurations could be handed to power users on QoreAI's team, while devs retained full control to drop into code for the parts that made QoreAI's product unique. That hybrid model, low-code for the common cases plus code-native flexibility where the product needed to stand out, became the foundation for how QoreAI now builds and ships every integration. And because Prismatic's tooling makes building custom connectors straightforward, the team was never blocked by whether a third-party app already existed in the library.
A pre-built connector library was the bonus. Prismatic shipped with hundreds of ready-to-use connectors QoreAI could pull from on day one, and that catalog has only grown since. "Over 200 were available at the time we initially evaluated, and that number has grown," Maarten said.
The implementation: Prismatic as QoreAI's action layer
Today, Prismatic sits at the heart of QoreAI's product. After a customer queries the AI data warehouse and finds the information they want, the export and activation steps occur entirely on Prismatic's infrastructure.
"Our action layer is completely dependent on Prismatic," Maarten said. "As soon as they find the information they want and they wanna go and execute on it, they immediately export it using Prismatic through a custom interface that we've built on top of Prismatic."
QoreAI now runs 62 live integrations on Prismatic, covering the systems dealerships need. ActiveCampaign, Meta Ads, Google Ads, Bing Ads, MailChimp, and Google Analytics 4 (GA4) for marketing. Microsoft Teams, Slack, and email for communication. Amazon Ads for connected TV campaigns through Fire TV and Prime Video. File Transfer Protocol (FTP) and Extensible Markup Language (XML) feeds for data exchange. And a growing set of automotive-specific SaaS tools.
For the small percentage of QoreAI-specific logic, the team relies on Prismatic's code blocks. "Maybe two to five percent of it is custom code, but that two to five percent is what makes our tool stand out," Maarten said.
QoreAI has also pulled Prismatic deep into its development pipeline. The team uses Prismatic's Model Context Protocol (MCP) tools along with other AI coding tools as part of its planning and execution flow.
"We have Prismatic's MCP tools integrated into our normal development pipeline, along with other AI tools," Maarten said. "It's made it so we can largely get a first pass of an integration done on the dev side with a single prompt. We go through our Claude Code planning phase, include the Prismatic tools as part of that plan, and when it executes, it gets us to the 80 percent mark before we've even necessarily touched a line of code."
For dealership customers, the activation experience is what changed most. Through Prismatic's embedded marketplace, customers self-serve new integrations directly inside the QoreAI product. "If we're activating a new integration that's already been set up, it's a five-minute process at most," Maarten said.
The results: from 5% of the product eating half the budget to a scalable platform
The shift to Prismatic produced compounding gains across speed, capacity, support load, and downstream customer outcomes.
Up to 6x faster integration delivery
Integration timelines, which had been 4-12 weeks per release, were reduced to 1-2 weeks. "Now with Prismatic, we're able to take on pretty much as many integrations as customers want and ask for," Maarten said. "Prismatic has so many built in that it makes it even easier for us to say yes to most customer requests."
Maintenance overhead reduced to near zero
The 10 to 25 percent of dev time previously spent on integration maintenance is gone. "Once an integration is live on Prismatic and we've verified it's working how we want, we generally don't have to touch it again," Maarten said. "That's effectively gone down to zero."
40 percent of engineering capacity recovered
By offloading the boilerplate scaffolding that used to require a custom stack for every connector, the engineering team reclaimed the capacity it had been burning on integration plumbing. That capacity now goes to the core product, the AI data warehouse and predictive analytics layer that differentiates QoreAI in the automotive market.
Up to 500 percent increase in customer satisfaction for QoreAI's dealer group clients
The most compelling result is downstream. QoreAI's platform combines the data warehouse with predictive enrichment signals such as which households are likely in market for a new vehicle, then exports that data through Prismatic to MarTech and AdTech destinations.
"By having that additional information within our platform and then being able to export it and action on it using our Prismatic connectors, we've seen our dealer group clients dramatically increase customer satisfaction, open rates, and customer return rates, because they're able to focus in on the right person at the right time with the right message," Maarten said.
The numbers QoreAI's clients are reporting are striking. "Some customers hit a 500 percent increase in customer satisfaction," Maarten said. "We've also seen the predictive analytics of our platform account for about 86 percent of all sales they've done before they happen."
Five-minute customer self-service
The embedded marketplace also doubles as a sales asset. "It's not only a tool for self-service by a dealership or dealer group, but also a sales vehicle, because we're able to immediately show value to a customer before they've even started using the platform," Maarten said. "Without the embedded marketplace, we wouldn't be able to have that kind of personal empowerment of our customers."
The strategic impact: integrations are how an AI data platform delivers value
For an AI-native data warehouse, integrations are not a feature checklist. They are the moment the product delivers a result to the customer.
The same dataset QoreAI builds for a dealer group can drive higher conversion on a Meta Ads campaign, smarter routing of inbound leads to Microsoft Teams, more relevant offers through ActiveCampaign and MailChimp, and connected TV reach through Fire TV and Prime Video. None of that happens without a reliable, customer-configurable action layer underneath.
QoreAI's broader mission, giving automotive businesses ownership and execution control over their own data, depends on that action layer being dependable. In an industry where dealership data has historically been locked inside a small number of legacy software providers, the ability to enrich, predict, and then activate that data is what makes the platform meaningful to a dealer group. Prismatic is what lets QoreAI focus on the part of the product that differentiates it: the AI, the data, the predictive signals, the natural-language interface for non-technical dealership users. Everything that moves data out the door runs on Prismatic.
Looking forward: AI-driven, self-service integration building for end users
QoreAI's roadmap is leaning further into self-service. Over the next 6 to 12 months, the team plans to bring Prismatic's AI tooling directly into the natural-language chat experience dealership users already rely on.
"We're actively looking at ways we can integrate Prismatic's AI tools into our existing AI chat so that users who need an integration or a particular behavior can easily self-service build that out in a way that doesn't feel intimidating, using natural language," Maarten said. "Customers can say, 'I need this to go here in this way for this reason,' and have it just work."
The team is also expanding integration coverage in MarTech and AdTech, with a focus on the channels dealer groups buy in, and evolving the marketplace experience to surface integration history, usage, and compliance signals alongside activation, building on the foundation Prismatic provides.
Asked whether he would recommend Prismatic to peers, Maarten was direct.
"I would absolutely recommend Prismatic to peers needing a vehicle for handling integrations in a way that's easier for them to take dev time for," he said. "We saw a massive decrease in dev time for creating and maintaining connectivity with all these different services. If one of my peers had a similar use case, I would absolutely recommend Prismatic to them."
About QoreAI
QoreAI is a Software-as-a-Service and Artificial Intelligence company building a data warehousing and data mining platform for the automotive retail industry. QoreAI lets non-technical users at car dealerships and dealer groups query their own data in natural language, enrich it with predictive signals such as in-market intent, and action it through marketing, advertising, and operational systems. The company's mission is to give automotive businesses ownership and control over their own data, breaking the model where dealership data is locked inside a small number of legacy software providers. QoreAI's engineering team builds with a hybrid of code-native and low-code approaches and uses Prismatic as the action layer that powers data export and activation across its customer base.
Key takeaways
- Integrations went from 5 percent of the product eating 50 percent of the budget to a scalable, productized layer. QoreAI now runs 62 live integrations on Prismatic, with delivery time cut from 4-12 weeks down to 1-2 weeks.
- Maintenance overhead is effectively gone. The 10-25 percent of engineering cycles that used to go to integration upkeep are now back on core product work.
- The embedded marketplace turned activation into a five-minute customer self-service flow. Dealerships activate integrations themselves, and the marketplace doubles as a sales asset that shows prospects what is possible before they even start using the platform.
- Prismatic's MCP tools and AI integration push first-pass development to 80 percent. QoreAI's developers use Prismatic's MCP server alongside other AI coding tools to scaffold integrations from a single prompt.
- Customer outcomes scale with the action layer. QoreAI's dealer group clients are reporting up to 500 percent increases in customer satisfaction and 86 percent predictive accuracy on sales when their data flows through Prismatic-powered integrations.
