The race to sync AI and enterprise data
When SWIRL approached Cheesecake Labs in 2024, they were already clearing one of the final hurdles to enterprise AI adoption: data silos. Many companies had no way to connect their internal data to advanced language models.
Information stored in Confluence, JIRA, Snowflake, Tableau, Salesforce, and SharePoint couldn’t flow securely into LLMs like ChatGPT, Gemini, and Claude.
So, SWIRL set out to bridge that divide. Their AI-powered search and productivity tool could tap into internal knowledge without exposing sensitive data to outside platforms.
All that remained was getting to market before anyone else.
1. Re-architecting for speed and trust
SWIRL saw staff augmentation as the fastest way to grow without the drag of long hiring cycles. Bringing in external developers would allow them to:
Free up internal teams
to stay focused on partnerships, strategy, and go-to-market execution.
Speed up development
without sacrificing quality or reliability.
Expand backend infrastructure
to handle complex, multi-system deployments.
Improve platform performance
for real-time, enterprise-grade use cases.
Embed security best practices
into every layer.
But more than just extra hands, SWIRL needed experts who knew how to design expandable systems that could protect sensitive data. That’s when they found us.
2. Staying on track
Cheesecake Labs stepped in to support SWIRL’s backend development and DevOps capabilities.
Our team brought Python, Django, and cloud pipeline expertise to help SWIRL deliver a faster and more flexible platform for AI-driven enterprise search.
3. Running in sync
We embedded directly into their workflows to shorten release cycles while upholding their technical and security requirements.
Together, SWIRL and Cheesecake Labs:
Built high-capacity integration pipelines using Python and Django
Optimized DevOps workflows and cloud infrastructure to speed up deployments and strengthen system resilience across private cloud, on-premise, and fully air-gapped environments
Connected SWIRL to 100+ enterprise platforms to expand reach and scalability
Removed the need for data migration to reduce costs and implementation time for enterprise clients
Developed a natural language search interface and enhanced metadata engines for intuitive, accurate, code-free AI-powered search
Enabled real-time data access without vector databases or heavy ETL pipelines
Embedded security measures, including role-based access, zero-trust architecture, and compliance with strict enterprise privacy standards
Deployed Chain-of-Thought AI to map relationships across enterprise data
4. Built to lead, built to last
The upgraded SWIRL platform now supports seamless enterprise AI connectivity:
Secure enterprise integration
Flexible pipelines built with Python and Django connected 100+ enterprise platforms (Snowflake, Tableau, Salesforce, and more), enabling real-time data access without migration.
Crossing the finish line
Cheesecake Labs gave SWIRL’s lean team the engineering firepower they needed to bring installations online in a fraction of the time enterprise rollouts usually require.
The result is a faster and more intuitive AI search experience for enterprise use — one that doesn’t force users to compromise between speed, accuracy, and security.
Surging ahead
SWIRL’s clients reported major gains, including:
Up to 80% less time spent searching internal data
Faster decisions powered by real-time, context-rich results
Higher team productivity and lower operational friction
Tangible business impacts, like faster product launches, sharper marketing, and new revenue opportunities
Our collaboration continues — and with every release, SWIRL is pushing the boundaries of secure, enterprise-grade AI search.