Build Data and AI systems that deliver measurable ROI
We help teams design, build, and operate production-grade AI and data platforms— from lakehouse modernization and MLOps to retrieval-augmented generation and LLM strategy.
AI & Data Engineering Consulting
Strategy to production: we design for measurable outcomes, operational excellence, and governance by default.
AI Strategy & Use-Case Discovery
- ✓Value mapping & ROI models
- ✓Responsible-AI guardrails
- ✓Roadmaps & pilot selection
Data Engineering & Lakehouse
- ✓Medallion/Delta/Iceberg architectures
- ✓High-throughput ingestion & CDC
- ✓Cost-aware performance tuning
MLOps & LLMOps
- ✓CI/CD for models & prompts
- ✓Feature stores & eval harnesses
- ✓Observability, testing & rollout
GenAI & Retrieval-Augmented Apps
- ✓RAG pipelines, agents & function calling
- ✓Safety, privacy & governance
- ✓Human-in-the-loop workflows
Cloud Migration & Modernization
- ✓On-prem to cloud data estates
- ✓Platform hardening & controls
- ✓Cost governance & tagging
Data Governance & Security
- ✓Catalogs, lineage, and policies
- ✓PII handling, RBAC/ABAC & audit
- ✓Compliance by design
AI-Powered Solutions
Beyond consulting, we build and deploy our own AI-powered solutions to solve specific industry challenges.
Legacy Code to Databricks Converter
Seamlessly convert your legacy Hadoop PySpark and PL/SQL scripts to modern Databricks notebooks. Our tool automates the migration process, saving you time and reducing errors.
Key Features:
- ✓Automated PySpark Conversion
- ✓Automated PL/SQL to Spark SQL
- ✓Dependency Mapping & Analysis
- ✓One-click Notebook Generation
BayesDeltaBridge
Accelerate your migration from legacy databases to the Databricks Lakehouse with our automated solution. BayesDeltaBridge handles schema conversion, data migration, and validation, ensuring a seamless transition to DLT and Unity Catalog.
Key Features:
- ✓Automated Schema Conversion
- ✓DLT Pipeline Generation
- ✓Unity Catalog Integration
- ✓Data Validation & Testing
A practical, outcomes-first delivery model
We combine senior architecture with hands-on engineering to move from idea to production quickly and safely.
Discover
Align on high-value use cases, success metrics, and guardrails. Establish the north star and a clear ROI hypothesis.
Design
Target architecture, data contracts, and a release plan. Pick the minimum lovable scope that proves value fast.
Deliver
Build rapidly with CI/CD, IaC, and observability. Ship usable increments every 1-2 weeks.
Operate
Hardening, cost controls, monitoring & support. Transfer knowledge to your team to sustain momentum.
Representative outcomes
Illustrative examples of the kind of results organizations achieve with a strong architecture-first approach.
Data platform modernization
Migrated legacy pipelines to a lakehouse pattern (Delta/Iceberg), improving reliability while reducing compute cost.
- ✓40% faster ingestion
- ✓30% lower run costs
- ✓10x lineage coverage
GenAI document assistance
Built a retrieval-augmented assistant for unstructured docs with evaluation harness and safety filters.
- ✓2x faster case processing
- ✓0.8 answer faithfulness (eval)
- ✓ISO-aligned data handling
MLOps & observability
Introduced CI/CD for models/prompts, feature store, canary releases, and telemetry for model health & drift.
- ✓Weekly releases -> daily
- ✓90th-pct latency down 45%
- ✓Rollback in < 5 min
Architects who ship
We blend big-picture strategy with hands-on engineering to deliver durable systems—not demos.
Outcome-first
We anchor every engagement on business KPIs and time-to-value, not just model accuracy.
Secure & compliant
Least privilege, data minimization, lineage and auditability are built in from day one.
Cloud-agnostic
Azure, AWS, GCP—meet you where you are and design for portability to reduce lock-in risk.
Proven Expertise with Industry Leaders
Banking
Telecom
Life Sciences
Articles & Insights
Explore our thoughts on the latest trends in AI, data engineering, and MLOps.
Unity Catalog Migration Playbook
A step-by-step guide to migrating your data to Databricks Unity Catalog.
Read Article →Databricks vs. Snowflake
A comparative analysis of two leading data platforms for your business needs.
Read Article →Data Modeling Guide
An in-depth look at the Medallion Architecture for organizing data in a lakehouse.
Read Article →Banking AI Strategy
An overview of strategic AI implementation for corporate and institutional banking.
Read Article →Migration Plan: Informatica to Databricks
A strategic playbook for migrating legacy ETL workloads to a modern Databricks lakehouse.
Read Article →Agentic AI Adoption Framework For Fintech
A framework for adopting agentic AI in the financial technology sector.
Read Article →Databricks Feature Store Analysis
An analysis of the Databricks Feature Store and its role in MLOps.
Read Article →The Generative AI Revolution in Fintech & Insurance
Exploring the transformative impact of generative AI on the financial and insurance industries.
Read Article →The AI Bill of Materials: Governing Data, Features, Models, and Prompts
A guide to governing the components of your AI systems for transparency and control.
Read Article →Tell us about your goals
Share a few details and we’ll propose a path from idea to production—complete with a timeline and value model.
Engagement models
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Discovery Sprint (2–3 weeks): use-case selection, target architecture, backlog & value model.
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Launch (6–10 weeks): pilot to production with CI/CD, telemetry, and operational runbooks.
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Scale (ongoing): platform evolution, cost optimization, governance, and enablement.