Your CRM, ERP, and product database each report a different number, so every board pack starts with days of manual reconciliation in spreadsheets. Relevant Software builds custom data warehouses around your actual sources — taking you from scattered sources to one source of truth where the numbers already match.
Not sure whether you need a new warehouse, a migration or better models on the one you have? Start with consulting. We map your sources and reporting needs, look at what your current setup does well and where it breaks, and give you a recommendation with the budget and timeline behind it.
Architecture design turns your source landscape, data volumes, and reporting needs into a documented blueprint: storage and compute layers, integration patterns, and the trade-offs behind each choice.
Your business logic becomes a data model analysts can actually read: entities, metrics, and definitions agreed once and enforced everywhere, ready for data analytics and BI teams to build on.
Pipelines move data from your CRM, ERP, product databases, and third-party APIs into the warehouse on schedules your reporting can rely on. High-volume sources — event streams, IoT feeds, raw logs — run through big data services, so the warehouse stays the modeled core rather than the processing engine.
Our AI-powered testing services verify that the warehouse’s data is accurate before anyone relies on it.
Post-launch support keeps the warehouse current as sources, volumes, and questions change.
Every engagement starts with a costed, phased build roadmap — sources, architecture direction, and a realistic first release — with dates per stage instead of one distant deadline.
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, with GDPR and HIPAA compliance built into the architecture from the first design review.
99% of projects delivered on time — warehouse launches you can put in front of the board with a date attached.
96% employee retention means the engineers who design your architecture are still there to build, launch, and support it.
Data engineering, data analytics, and BI under one roof, with no subcontracting — the model, pipelines, and dashboards come from one accountable team.
Your sources, their owners, and the reports the business actually asks for go into a single inventory. You get the inventory, the gaps, and a scoped first-release backlog. If the priorities behind the warehouse are still contested, data strategy consulting settles them before the backlog is committed.
The blueprint fixes the platform, layers, and integration patterns against your data volumes and budget. Yours to challenge before a line of pipeline code is written.
Business entities and metric definitions take shape as a documented model, reviewed with the people who will query it. Sign-off happens here, while changes are still cheap.
Ingestion and transformation go live source by source through our data engineering services — each with monitoring and quality checks attached, and every merge passing senior review. Pipelines land in your version control as they’re finished.
Load and failure testing runs in a staged environment against your projected peak, and warehouse figures are reconciled against source systems line by line. The reconciliation report is part of the delivery.
Runbooks, access, and a working session with the people who take over. Support continues at whatever level you choose — from on-call to full operation.
Development closes that gap: scattered sources in, one source of truth out.
Readiness shows up in symptoms before it shows up in strategy. Run three checks today:
Three or more sources and two or more manual steps is the point where a built warehouse costs less than the spreadsheet operation it replaces.
The timeline has a fixed part and a variable part. The fixed part is predictability: the build roadmap sets dates per stage, and 99% of projects deliver on the dates it commits to. The variable part depends on three numbers you already have: how many source systems feed the warehouse, how much transformation your metrics need, and how many teams consume the output. Builds release in stages, so the first reliable dashboards arrive while later sources are still being connected. Pull those three numbers together before talking to any vendor — a partner who can’t turn them into a phased timeline with dates is guessing.
Five variables set most of the budget:
All five can be pre-computed before talking to anyone: list the sources, estimate the largest tables, count the metrics with contested definitions, name your regulator, and decide who operates the result. Those five answers turn a scoping call from a sales pitch into an estimate — contact us with them, and you’ll get a range against your numbers instead of a padded quote.
Inherited builds are a regular engagement shape, and they rarely require starting over. What happens first is an audit: the existing model, pipeline code, and documentation get assessed against what the business needs the warehouse to answer. A useful check you can run today — ask for the data model documentation; if the only documentation is the SQL itself, budget for the audit but not necessarily for a rebuild. Most inherited warehouses keep their platform and most of their pipelines, and get a corrected model and testing they never had.
A warehouse decides more about an AI roadmap than the models do. AI work needs the same things good reporting needs — documented definitions, traceable lineage, reliable freshness — plus room for unstructured data and feature pipelines when they arrive. A build that treats those as architecture requirements from day one won’t need replacing when the first ML use case lands.
Run one check today: take the AI use case closest to funding and list the data it needs; if nobody can name which system each field lives in and who owns it, the first project is the warehouse, not the model.
Trust in a warehouse dies the first time finance spots a mismatch, so reconciliation is a deliverable, not a courtesy. Warehouse figures get compared to source systems line by line before launch, the comparison is documented, and the checks stay in place as automated tests that run after every change. Run your own version any time: pick one KPI, compute it in the source system, and trace it through to the dashboard. If nobody can walk you through that path, the numbers match by luck. Reconciliation proves the numbers once; keeping them trustworthy as sources and teams change is an ongoing discipline, and data management covers the ownership rules, quality standards, and audit controls that hold after launch.
Not on day one. Handover includes runbooks and training, so a single technically-minded owner can operate a well-built warehouse day to day; what genuinely needs engineering time is change — new sources, new metrics, growing volumes. If you’d rather not build that capability in-house, ongoing AI-powered data engineering services cover pipeline maintenance, new-source onboarding, and cost tuning at whatever level of involvement you choose.
A full build pod runs five roles: a data architect on the model and platform, a data engineer on pipelines, an analytics engineer turning raw tables into the metrics your reports use, a data platform engineer on environments and provisioning, and a BI developer on the dashboard layer.
Both staffing shapes work for filling them; the signal is who owns the outcome. If your own data lead runs the architecture and needs senior hands, individual engineers embed into your squad and work in your process. If the warehouse itself is the deliverable — architecture, model, pipelines, launch — a dedicated team takes it end to end, with a roadmap and dates per stage. A practical way to decide today: name the person in your company who will answer for the warehouse in a year. If that person exists and is an engineer, embed specialists. If the answer is “nobody yet,” a dedicated team with a handover plan is what closes that gap.
Tech label
Healthcare
Tech label
Builds run on Snowflake, BigQuery, Amazon Redshift, and Databricks, engineered so cloud spend stays predictable as data volumes grow.
An enterprise data warehouse consolidates data across departments and subsidiaries into a governed platform that survives audits and reorganizations alike.
Data residency rules and sector regulation sometimes decide the architecture before preferences do — banking, healthcare, and public-sector builds often keep part of the estate on your own infrastructure.
