Data Solutions Services
to Power AI Initiatives
and the Next Gen BI

We combine 10+ years of full-stack data expertise with an AI-powered SDLC that accelerates delivery. Our approach focuses on building AI-ready, governed data platforms that integrate seamlessly with modern Conversational BI tools. We're vendor-agnostic, working across AWS, Azure, and hybrid environments, and we're active members of CNCF and Linux Foundation AI & Data Committee, giving us early access to emerging technologies.

The timeline varies based on complexity and scope. Our initial discovery and architecture phase typically takes 2 weeks to identify quick wins and establish a roadmap. A proof-of-value can be delivered in 30-45 days. Full modernization projects range from 3-9 months depending on data volumes, system complexity, and organizational readiness. We prioritize incremental value delivery, so you'll see measurable improvements throughout the engagement.

We work with technology-powered companies across various industries including media & broadcasting (Sinclair), life sciences (TetraScience), financial services, and enterprise SaaS. Our clients range from high-growth startups to Fortune 500 enterprises. What they share is a strategic focus on data and AI as competitive advantages, and a need for scalable, governed, and AI-ready data platforms.

We offer both. Many clients start with implementation and design, then transition to our Managed Data Engineering Services for ongoing operations, monitoring, optimization, and support. Our managed services cover the entire data ecosystem — from infrastructure to insights — with proactive monitoring, cost governance, performance tuning, and SLA-driven reliability. This allows your team to focus on analytics and insights while we handle the data platform operations.

Governance and security are embedded into every layer from day one. We implement access controls, data lineage tracking, audit trails, and compliance frameworks (GDPR, HIPAA, SOC 2, etc.) as core platform capabilities, not afterthoughts. We use AI-powered tools like Collibra Protect AI for automated PII detection, DataHub AI for policy validation, and Azure Purview AI for compliance scanning across hybrid environments. This approach maintains trust and regulatory compliance while keeping analytics agile.

We leverage a comprehensive suite of AI-powered accelerators across the entire data lifecycle: automated topology analysis (dbt Cloud AI, Databricks Unity Catalog AI), data quality validation (Great Expectations AI, DataFold AI), code generation (GitHub Copilot, AWS Code Whisperer), governance (Collibra Protect AI, DataHub AI), feature engineering (Featureform AI), and monitoring (Monte Carlo AI, AWS CloudWatch AI). These tools dramatically reduce time-to-value while improving quality and reliability.

We always start by assessing your current data stack to identify what can be optimized and extended versus what needs replacement. Many of our engagements involve hybrid approaches — modernizing incrementally while maintaining existing systems that still deliver value. We're platform-agnostic and work with Databricks, Snowflake, AWS, Azure, on-prem systems, and open-source frameworks. Our goal is to maximize your existing investments while addressing gaps in scalability, governance, and AI readiness.

Cost optimization is built into our approach from architecture through operations. We implement FinOps guardrails, use AI-powered cost modeling tools (FinOps AI Dashboard, Snowflake/Databricks Copilot), optimize storage and compute separation, implement proper data lifecycle policies, and continuously monitor for waste and inefficiencies. Typical outcomes include 30-50% cost reductions without sacrificing performance or reliability. Our Managed Services include ongoing cost governance and optimization.