Every AI initiative you’ve approved is waiting on the same warehouse. It was built for scheduled reports, not for the AI, streaming, and real-time questions the business asks now. Relevant Software modernizes it around what you’re trying to do next: a diagnosis first, then the strategy that fixes what limits you and leaves the rest alone.
The assessment maps your estate — schemas, pipelines, workloads, and costs — and recovers the dependencies that were never documented, through AI-driven legacy modernization with senior review.
Strategy turns the diagnosis into a decision: which workloads get rehosted, replatformed, refactored, or rebuilt — and which stay where they are.
Re-platforming moves the warehouse onto a cloud-native engine — Snowflake, BigQuery, Databricks, or Microsoft Fabric — where compute and storage scale independently and the maintenance burden drops.
Re-engineering rebuilds brittle, hand-maintained ETL into version-controlled ELT the warehouse runs itself.
Streaming re-architecture brings the warehouse within minutes of your operations, for the decisions overnight batch can’t serve — built through big data services where volumes call for it.
A lakehouse serves BI and data science from one platform, for estates where unstructured data and ML now sit beside reporting.
Modernization is the point to build governance and cost control into the platform itself, with ownership and quality rules through data management.
Your estate, its costs, and its constraints, measured and put in writing. You see which problems are the platform’s and which are the design’s.
Each workload gets a rehost, replatform, refactor, rebuild, or leave-as-is decision, with the reasoning recorded beside it. Yours to challenge before anything is committed.
Phases priced and dated against a cost-of-doing-nothing baseline, so the business sees both numbers.
Workloads re-architect one at a time through our data engineering services, each validated against current outputs, with reporting live throughout.
Lineage, access, and FinOps monitoring shipped inside the platform, so governance is part of the build.
Documentation, runbooks, and training, handed over. Support continues at the level you choose — from advisory to full operation.
Each workload gets its own rehost / replatform / refactor / rebuild decision — with the reasoning recorded and the option to leave it alone.
Platform recommendations scored on your workloads’ merits — no reseller margins or partnership quotas behind the advice.
The Relevant AI Delivery Framework™ cuts delivery time by up to 50% with up to 20% fewer bugs through AI-assisted development — with every release still passing senior code review.
ISO 27001-certified processes; GDPR and HIPAA controls designed into the modernized platform from the first workload.
99% of projects delivered on time — modernization phased so value arrives before the last phase does.
96% employee retention — the engineers who diagnose your estate are the ones who re-architect it.
The assessment answers it, workload by workload — modernize what limits you, keep what works.
“Works” and “worth keeping” are different tests. A warehouse can produce correct reports and still cost more every quarter, block every AI initiative, and consume the team in maintenance. Decide it with two numbers side by side:
A rough version you can run today: add up the engineering hours spent last quarter keeping the warehouse alive versus improving it. If maintenance won, the platform has already become the project.
They often run in one program, but the decision differs. If your reports are right and you just need them faster and cheaper on a new engine, that’s data warehouse migration services. If the architecture itself can’t serve what the business now asks, that’s modernization.
Decide it per workload. For each major workload, name the complaint:
Most estates are a mix, plus a rebuild for the parts beyond repair and a leave-alone for the parts that work. If sorting your own workloads raises more questions than it settles, that first call is exactly what the assessment resolves — contact us and it comes back as a decision per workload, with the reasoning attached.
No, because it’s phased. Workloads modernize one at a time, each validated against its current outputs before anyone depends on the new version, with existing reporting live throughout — there’s no overnight switch where the whole warehouse changes at once. A question worth putting to any modernization partner: which report moves first, and how will we know it still matches? A partner who can answer that per phase is running a controlled program.
Cost follows the strategy — a rehost is priced like an infrastructure move, a refactor like an engineering project, and most estates are a mix. The justification comes from two computable numbers:
Bring your last four quarters of warehouse run-cost to a scoping call and the trade-off gets concrete — contact us and the first thing you’ll get back is a diagnosis read.
Yes — undocumented estates are the normal starting point. The assessment reconstructs what the warehouse actually does from its code, schedules, and query history before any strategy is set, so the plan rests on how the system behaves rather than on documentation that was never written. Most estates keep their data and much of their logic; what they gain is a platform that can finally be reasoned about.
Often, and it’s diagnosable. AI workloads need what modern reporting needs, plus more:
A legacy warehouse usually offers none of these. The test: take the AI use case closest to funding and list the data it needs and how fresh. If that data can’t be served at the required freshness, the first project is the platform underneath the model, not the model.
Tech label
Tech label
Tech label
