AI programs move only as fast as their data.
Agencies and enterprises have more data than ever, spread across warehouses, file shares and legacy systems with uneven quality and unclear ownership. Before AI can be trusted, that data has to be cataloged, cleaned, protected and governed.
Our data engineers build lakehouse platforms on Databricks, Snowflake and the major clouds, set up quality and lineage controls, and prepare sensitive data for AI use through masking, tokenization and synthetic data. Our AI engineers then build retrieval, agent and document intelligence applications on that foundation, with evaluation and human review built in.
- Managed project with defined deliverables
- Embedded team with a Meritore delivery lead
- Specialist consultant for a single role
- Offshore development and support from Hyderabad
Services in this practice
Data platform engineering
Lakehouse and warehouse builds, pipelines and migration from legacy databases on Databricks, Snowflake, Azure and AWS.
Data governance and quality
Cataloging, classification, quality scoring and lineage tied to the identity and access rules you already run.
AI data readiness
Masking, de-identification, tokenization and synthetic data so sensitive records can be used safely for AI.
AI applications
Retrieval-augmented generation, agent workflows and document intelligence on Azure OpenAI, Amazon Bedrock and Vertex AI.
MLOps and evaluation
Model registries, monitoring, evaluation sets and human-in-the-loop review for production AI.
Analytics and reporting
Semantic models and dashboards in Power BI and Tableau for program and executive reporting.
Where clients usually begin.
Data assessment
A short review of sources, quality, sensitivity and access, ending with a prioritized roadmap.
Platform migration
Moving a legacy warehouse or set of databases onto a governed lakehouse.
AI pilot on safe data
A working AI use case built on masked or synthetic data, so no sensitive records leave your boundary.
Platforms and tools our engineers use.
Discuss your requirement with a practitioner.
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