The unglamorous truth of enterprise AI: the model is the easy part. DataPattern built a company on the hard part - the tangled, ungoverned data underneath.
Everyone wants AI. Almost nobody wants to fix their data first. That gap is, more or less, the entire business at DataPattern - a San Ramon, California IT services and consulting firm that has spent since 2019 doing the work most enterprises would rather skip.
DataPattern was, in its own words, "formed by a group of industry managers and technology experts" to evolve purpose-driven IoT use cases and combine domain experience with machine learning and AI. In practice, that means the company gets hired when an organization's ambitions - predictive maintenance, generative AI, real-time analytics - run headlong into a decade of legacy systems and messy, ungoverned data.
The firm's stated priority is unusual for a shop that sells AI: data governance, quality and lineage. DataPattern treats those - normally filed under compliance chores - as the real infrastructure that makes an AI's output trustworthy. Fix the plumbing, the reasoning goes, and the models take care of themselves.
That philosophy runs across a broad service line. DataPattern builds data engineering pipelines and ELT workflows, stands up cloud data platforms on Databricks, Snowflake and Microsoft Azure, engineers IoT solutions driven by device data, and wraps it all in DevOps and MLOps practices so the work survives contact with production. A dedicated generative AI development team handles the newer demand.
Leadership is founder-led. Jennifer Thangam Easwaramurthi, a co-founder who has held roles at Bank of the West, Accenture and Tata Consultancy Services, serves as chief executive and chief digital officer; Stanley Moses Sathianthan is co-founder and chief development officer. The company operates a US-India delivery model, with its headquarters in San Ramon, a satellite presence in Natick, Massachusetts, and engineering teams in India.
It is not a household name, and it is not trying to be. At roughly 120 people, DataPattern is a mid-market consultancy competing in a crowded field of data and AI specialists. Its wager is that the boring, careful work - governance, lineage, clean pipelines - is exactly what keeps enterprise AI projects alive.
Modern pipelines, ELT workflows, and data management with governance, quality and lineage at the core - across Snowflake, Databricks and Azure Data Factory.
Predictive maintenance, anomaly detection, NLP and generative AI - described by the firm as the fusion of data and creativity.
Secure, standards-compliant IoT solutions driven by device-generated data, pairing domain experts with big-data, security and AI engineers.
Cloud enablement and migration across AWS, Azure and multi-cloud data estates, unifying sprawling stacks into working platforms.
CI/CD, ML lifecycle management and production deployment pipelines that keep data and AI workloads reliable in the wild.
Transformation strategy, use-case validation, roadmaps, change management and training built for regulated industries.
Where many AI shops lead with models, DataPattern leads with data quality, governance and lineage - the layer that makes AI output trustworthy.
Engagements pair the person who understands the factory or the ledger with the person who understands the neural net.
Its footprint spans mainframe-era tools (IBM Db2, CICS, VSAM) and modern stacks (Databricks, Snowflake, Azure) - the exact bridge regulated firms need.
DataPattern earns through project-based engagements and managed services rather than a packaged software subscription. Revenue comes from consulting delivery, solution engineering and technology implementation.
A staffing and recruitment arm provides contract and direct-hire data and engineering talent - extending the consulting relationship into people, not just projects.
Alternatives: Tredence · Tiger Analytics · LatentView · boutique Databricks/Snowflake partners
Industry managers and technology experts launch the firm in San Ramon, California to build purpose-driven IoT and AI use cases.
Data engineering, IoT, cloud and DevOps/MLOps service lines built out for enterprise clients.
Deepens governance-first work on Databricks, Snowflake and Azure data platforms.
Adds a dedicated generative AI development practice as enterprise AI demand accelerates.
Leadership spotlights growth of India operations, reinforcing the cross-border model.
Operating across manufacturing, healthcare, finance and energy with US and India teams.
It is an IT services and consulting firm that builds data engineering, AI and generative AI, IoT, cloud, and DevOps/MLOps solutions for enterprises, with a strong emphasis on data governance and quality.
It is headquartered at 2603 Camino Ramon, San Ramon, California, with additional presence in Natick, Massachusetts and engineering teams in India.
It was founded in 2019 by a team including Jennifer Thangam Easwaramurthi (CEO / Chief Digital Officer) and Stanley Moses Sathianthan (Chief Development Officer).
Primarily manufacturing, healthcare, finance and energy/utilities - regulated and data-intensive sectors modernizing legacy systems.
Its stack spans Databricks, Snowflake, Azure Data Factory and Synapse, AWS and Salesforce, alongside legacy systems such as IBM Db2, CICS and VSAM.
Video: DataPattern has not published a public YouTube channel or product-demo video at the time of writing - check datapattern.ai and LinkedIn for the latest talks and demos.
Profile compiled from public sources · Figures approximate where noted · San Ramon, California