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
Experience Our AI Capabilities
Try our AI-powered tools to see how we can bring value to your business.
An Interactive Agentic AI Prototyper-BayesAgent
Build and test your own AI agents. Our prototyper allows you to design, configure, and deploy autonomous agents to tackle complex tasks, all through an intuitive, interactive interface.
Try BayesAgent PrototyperArticles & 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.