We design data lakes, lakehouses and pipelines on AWS, Azure Databricks and Microsoft Fabric, then build the AI on top: LLM applications, RAG and agents grounded on data you can trust.
Consulting and delivery projects, each built on a well-architected cloud foundation: governed, tested and versioned from day one.
Data lakes on AWS, lakehouses on Databricks, ingestion with Data Factory and Glue, and Microsoft Fabric: medallion layers, streaming, data quality and governance.
AI consulting and delivery: readiness assessments, LLM applications, RAG and agents grounded on your governed data, evaluated before they ship.
When you also need the product on top: full-stack web apps, internal tools and automations with AI built in.
Cloud architecture is not a separate service with us. It runs through every engagement, led by an AWS Certified Solutions Architect:
Most AI problems are data problems. We fix the foundation before we put a model on top of it.
The people you talk to are the people who design and build your system. No hand-offs to a junior bench.
Pipelines and AI features ship with tests, CI/CD and infrastructure. Nothing is configured by hand in production.
We explain what something does before we name it, so your leadership always knows where the work stands.
We audit your current estate — workspaces, factories, capacities — and agree on what "done" means.
Reference architecture, wave plan, and cost model — written down, reviewed with your team.
Short sprints, working software every week, everything deployed through CI/CD — never by hand.
Runbooks, dashboards and documentation, then a clean handover to your engineers, or ongoing support if you want it.
What we can show today: our certifications and our government registrations.
Projects we built to test data and AI ideas in practice, each with an architecture write-up. Experiments, not client engagements.
One governed gate for every LLM call — models, MCP servers, guardrails, budgets, and audit logs in a single control plane.
Plain language in, structured JSON out — one engine that plans, executes, and schema-validates across SQL, NoSQL, and the warehouse.
Every data source in one knowledge base — chat on the left, dynamic views on the right that reshape to each question.
Spec-driven development platform: write the spec, and the platform carries it through to working, reviewable software.
A catalog layout that survives real org charts: environment-first catalogs, domain schemas, access through groups only, and governance applied by CI rather than by hand.
Delta Live Tables and Databricks Workflows overlap less than people think. The deciding question is ownership: does the pipeline own the table, or does a job just run some tasks?
One databricks.yml, three targets, no clicking in production. The promotion pattern we recommend for every Databricks project, with a working GitHub Actions setup.
We are a data engineering and AI consultancy. We design and build data lakes, lakehouses and pipelines, and the AI systems that run on them: LLM applications, retrieval (RAG) and agents. We also build full-stack products with AI built in when a client needs the application on top.
AWS (S3, Glue, Lake Formation, Athena, Redshift), Azure Databricks with Unity Catalog, Azure Data Factory and Microsoft Fabric for data; MLflow, Vector Search, Model Serving and major LLM providers for AI.
Yes, as part of every engagement. Data platforms, AI systems and products all run on cloud infrastructure we design: landing zones, networking, identity, security, cost controls and infrastructure as code on AWS and Azure. The work is led by an AWS Certified Solutions Architect.
With a discovery call. We review what you run today, agree what “done” means, and propose either a packaged offer with a fixed scope or a scoped project.
The engineers you speak to in the discovery call are the engineers who design and build your system. We do not hand work off to a junior bench.
Hyderabad, Telangana, India.
Tell us what you run today and what you want it to do. We will give you an honest view of the work involved.