IMAGE: Databricks
Imagine running an accessories retail business where profits are razor-thin and every customer is hunting for the lowest price. For CASETiFY, the challenge isn’t just selling more—it’s about understanding customers deeply enough to keep them coming back, growing their loyalty and increasing wallet share. Enter Databricks, the data intelligence platform helping CASETiFY turn sprawling, chaotic data into business gold.
CASETiFY, known for its custom phone cases and tech accessories, grappled with the fragmentation that plagues fast-scaling e-commerce brands. As the company expanded to serve millions worldwide, data complexity mushroomed: marketing metrics were siloed in ad platforms like Meta and TikTok, transactional records lived in internal systems, and behavioural data was locked away in Google Analytics. The outcome? Disjointed teams, a fractured view of the customer journey, and a near-impossible task connecting marketing spend to tangible results.
Legacy infrastructure wasn’t helping. CASETiFY’s original systems, designed for simple sales data, buckled under the strain of modern e-commerce: semi-structured JSON from APIs and clickstream logs created brittle pipelines prone to failure—especially during high-stakes events like new iPhone launches. Analysts and engineers were stuck in a perpetual game of catch-up, wrangling stale datasets in siloed environments, making predictive modelling and agile business decisions a pipe dream.
“Answering a seemingly simple question like, what was the true ROI of our latest influencer campaign, required a monumental manual effort of exporting and stitching together data in spreadsheets,” Jason Tse, CASETiFY’s senior data manager recalled. “It was slow, error-prone, and left us with unreliable insights.”
Databricks was the inflection point. Migrating to the Databricks lakehouse unified CASETiFY’s data landscape, providing a single source of truth and unlocking advanced analytics and AI-driven personalisation. The results are hard to ignore: CASETiFY slashed data transformation costs by 20 per cent, created over 5,000 new dashboard views per week, and saw double-digit year-over-year growth in repeat customer revenue—all driven by precise, one-to-one marketing.
“With Databricks, we finally have one trusted source of truth and can see the full customer journey. Decisions are faster, collaboration is smoother and access control is governed—empowering us to focus on creating experiences our customers love,” Tse said.
Aside from CASETiFY, Databricks has seen strong results from other enterprises across APAC using Databricks to unlock ROI from AI, including:
- Cycle & Carriage, a leading Southeast Asian automotive group, that used Mosaic AI to build internal and customer-facing chatbots through RAG (retrieval augmented generation), achieving 99 per cent model accuracy and 100 per cent improvement in user experience by deploying AI agents optimised across multiple models.
- GetGo, Singapore’s largest carsharing company, that transitioned from fragmented legacy systems to Databricks and now leverages telemetry, image and payment data for real-time decision making. This has resulted in 66 per cent faster time to insights for vehicle analytics and a 50 per cent reduction in fuel theft via geospatial and behavioural analysis.
The company has been growing its APAC customers, and it is laser-focused on efficient topline growth with a credible path to profitability. Said Adam Conway, SVP of products at Databricks. “Our latest financial growth numbers reflect this focus. We surpassed a US$4B revenue run rate in Q2, growing over 50 per cent year over year. Our AI products alone recently exceeded a US$1B revenue run-rate.”
Databricks have achieved positive free cash flow over the last 12 months and intend to remain cash flow positive. “Internally, we use AI and our own Databricks’ products to drive efficiencies and keep our operating costs down while focusing on topline growth,” Conway added.
A cultural principle at Databricks is being customer-obsessed. “I was in Singapore for our product advisory board, where we met with selected customers from around the APAC and Japan region to hear from them on what they need from our product roadmap for their success. This information guides us in our continuous product evolution and innovation.”
One such product is Agent Bricks, which was launched based on customer feedback and cutting-edge AI research. “With Agent Bricks, we are reinventing the way to build AI agents that are focused on accuracy and quality,” Conway explained.
APAC is a dynamic and fast-moving market for AI and data technologies, Conway added. Enterprises are nimble and quick to adopt new technology to stay competitive. The region also has a unique opportunity to leapfrog in AI adoption by building on fresh infrastructure and learning from global precedents, enabling innovations that are difficult to achieve in markets restrained by legacy infrastructure.
However, APAC also presented unique challenges, particularly around complex and evolving data sovereignty regulations that vary by country.
“To address this, we’re investing in both infrastructure and people. We are expanding into local and regional cloud regions to ensure our offerings are accessible and compliant within each market,” said Conway. “We’ve also grown our APJ go-to-market team to over 1,000 people and are working closely with local and regional consulting and system integrator partners, reflecting our commitment to local expertise and support.”
“Our unified governance layer, powered by Unity Catalog, also gives enterprises confidence in managing data securely and compliantly across jurisdictions.”
Databricks’ open-source foundation is a key differentiator, said Conway. “Many companies are hamstrung by fragmented database infrastructure. Our openness is a key differentiator. We are the original creators of open-source technologies like Apache Spark, Delta Lake and MLflow, and we continue to push the frontier with offerings like Delta Sharing, the industry’s first open protocol for secure data sharing.”
“We’ve seen entire teams transform once they consolidate onto Databricks—they’re freed up to do more meaningful, impactful work.”
But Databricks isn’t resting on its lakehouse laurels. In the last two years, the company has doubled down on AI, acquiring Tabular (data management) and MosaicML (open source LLM training). This year, Databricks announced an approximately US$1 billion acquisition of Neon, a startup with a managed, cloud-based, open-source alternative to AWS Aurora Postgres. Neon’s tech, which enables scalable, usage-based database management and features like cloning and point-in-time recovery, is tailor-made for AI agent workloads, according to Databricks, a staggering 80 per cent of databases provisioned on Neon are created by AI agents, not humans.
Databricks also announced Lakebase in June 2025. While Agent Bricks promises production-ready AI agents optimised for enterprise data, Lakebase introduces a new category of operational databases—built on open-source Postgres and engineered for AI agents. In September 2025, Databricks closed its Series K funding, securing another US$1 billion. The fresh capital is earmarked for accelerating Agent Bricks, launching, driving global growth, and fuelling future AI research and acquisitions.
The message is clear: in the race to harness data and AI, companies like CASETiFY and others, and the platforms powering them, are only getting started.
