Data Warehouse & BI
Agam Intelligence builds warehouses and BI that turn scattered systems into a shared picture of the business. We design repositories, semantic layers, and dashboards for the people who actually decide - with governance so two reports do not disagree on the same KPI.
Intelligence
One number the business can stand behind.
Dashboards fail when the warehouse underneath is a dump of tables nobody trusts. We start from the decisions and the metrics, then design modelling, access, and BI so finance, operations, and leadership are looking at the same definitions. Our Hadoop and enterprise BI heritage means we know how to handle volume without losing clarity.
We model facts, dimensions, and grain for how the business asks questions - on Snowflake, BigQuery, Databricks, Redshift, or a lakehouse pattern that fits cost and volume. Architecture follows the questions, not a vendor slide.
Revenue, margin, and active users should not mean three things. We capture definitions, owners, and tests so BI tools share a metric layer instead of competing Excel logic.
Leadership packs and line-of-business dashboards designed for the meeting they are used in. We cut vanity charts and keep the views that change a decision or a daily huddle.
Analysts should not wait on engineering for every cut. We build governed self-serve - certified datasets, row-level security, and documentation - so exploration does not become a second unofficial warehouse.
Warehouses hold the crown jewels. We design roles, masking, and lineage so the right people see the right grain, and so you can answer who saw what when the auditor asks.
A warehouse that is correct but unaffordable will be bypassed. We design clustering, incremental models, and workload management so reporting stays fast without surprise cloud bills.
A regional health network needed executive dashboards and predictive models grounded in governed clinical data.
A B2B SaaS vendor needed RAG-based product assistance grounded in customer docs - not a generic chatbot.
A payments processor needed anomaly detection that compliance could defend - not another black-box alert queue.
Reporting is judged on whether leadership can act - shared KPIs, timely packs, and fewer reconciling meetings.
Governed definitions, access control, and lineage reduce the risk of the wrong number in a board pack.
Move from spreadsheet islands or a legacy cube in slices, keeping the reports the business cannot live without.
Pressure to ship AI pilots is real, but teams that skip strategy stall on unclear ROI, messy data, and governance gaps. This article explains why use-case clarity, data readiness, and guardrails must come before model selection - and how governance can actually accelerate adoption.
Read the articleFuture-ready data and AI platforms are designed for change from the start - pipelines that can evolve, models that can retrain, and architecture that does not force a rewrite every time the business asks a new question.
Read the articleWhen dashboards disagree, models drift, and nobody owns the pipeline, the problem is often systemic. This article covers the warning signs and what an independent audit should examine across data, process, and platform.
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