Data

Every AI initiative, every dashboard, every automated decision your business will ever make rests on the same foundation: the data underneath it. We work on that foundation in three connected stages — freeing it from the legacy systems holding it hostage, running it well day to day, and making it genuinely ready for AI to use. Jump to a section below.

Legacy Data

Legacy data silos form when information gets trapped inside isolated applications, departments, or aging technology — unable to move freely across the business. Decades of organic growth, uncoordinated tool adoption, and rigid legacy architecture (vintage mainframes, closed relational databases, discontinued desktop platforms) leave organizations unable to form one unified view of their operations, customers, or assets. The data usually isn't gone. It's just locked somewhere nobody wants to go looking.

The AI and Analytics Bottleneck

Predictive analytics, agentic AI, and LLM deployments all depend on data that's cross-functional, clean, and rich with context. Legacy silos starve them of exactly that. When core business logic is locked away in decades-old formats, AI models underperform, hallucinate from missing context, or simply fail to operationalize across the last mile of daily business use.

Escalating Technical Debt

Maintaining legacy environments consumes a disproportionate share of the IT budget — routine upkeep, custom point-to-point integration scripts, and manual reconciliation eat resources that should fund innovation instead. Every isolated data store compounds the debt, making the next integration more fragile and more expensive than the last.

Operational Friction and "Blind" Decision-Making

Fragmented data forces leaders to decide on partial truths or stale reports. A simple cross-departmental request can mean weeks of manual extraction and reconciliation — slow enough to stall go-to-market plans and blunt customer responsiveness.

Governance, Compliance and Risk Exposure

Enforcing consistent governance, lineage, and security across a fragmented legacy landscape is exceptionally hard. Disconnected systems raise the risk of data drift, shadow reporting, and non-compliance with regulations like GDPR and CCPA, simply because nobody can reliably say where sensitive data lives or who's touched it.

Legacy Platforms We Regularly Migrate

We've moved data out of mainframe and legacy environments including DB2, IDMS, VSAM, Sybase, Visual FoxPro, Informix, Adabas, and COBOL/CICS-based applications — along with the discontinued desktop and departmental databases (dBase, Btrieve, and similar) that tend to accumulate in finance, operations, and back-office teams over the years.

How We Help

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Data Management

Most "data problems" people complain about are actually plumbing problems — a database that was never tuned, an integration job that silently fails on Tuesdays, a warehouse nobody's cleaned up since last year. None of it is glamorous, and all of it determines whether the rest of your data and AI investments hold up. Once data is off legacy platforms, this is the plumbing we run so it stays invisible, in the good way.

End-to-End Database Services

Setup, installation, configuration, upgrades and performance tuning across your database estate, so systems stay fast and stable as they age rather than degrading quietly.

Data Integration & ETL

We consolidate data from scattered sources into a unified, accurate repository, building ETL/ELT processes that keep information consistent across every system that touches it.

Database Monitoring, Maintenance & Managed Services

24/7 monitoring, backup and recovery, and proactive maintenance that catches a failing disk or a runaway query before it becomes a 2am phone call.

Data Storage & Structuring

Centralized, cleaned and properly structured warehousing that turns "we have a lot of data somewhere" into "we know exactly where our data is and can trust it."

Data Readiness for AI

Circling back to the AI and analytics bottleneck we opened with: models don't fail because the algorithms are wrong. They fail because the data feeding them isn't ready — ungoverned, poorly cataloged, or impossible to trust at the level AI decisions demand. Data governance used to be the department that said no. Today it's the reason AI initiatives get funded instead of stuck in review, because a model built on data nobody can vouch for isn't an asset, it's a liability waiting for an audit.

Governance Maturity Assessment

We benchmark where your governance actually stands today — not where the policy documents say it stands — and build a scalable, prioritized roadmap to close the gaps that matter most.

Data Catalog & Lineage for AI Context

Centralized cataloging with metadata-driven search and full lineage visibility, so a model — or the person accountable for it — can always trace an answer back to its source.

Data Protection, Security & Compliance

Encryption, anonymization, masking, role-based access and real-time threat detection, aligned to the regulatory standards your industry actually gets audited against.

Data Quality & Observability

Automated cleansing and validation plus continuous monitoring of data health, so quality issues get caught as anomalies, not discovered three prompts downstream.

From Governed Data to Production AI

Clean, secure, well-governed, well-cataloged data is what separates an AI pilot that works in a demo from one your compliance team will actually let you put into production.