Source connectivity at scale
Databases, files, message buses, partners, and SaaS connected with agreed freshness and schema contracts. We prefer change data capture and well-defined extracts over nightly mystery dumps.
Big Data Integration
Connect disparate sources into governed platforms ready for analytics and AI.
Agam Intelligence integrates the systems that never agreed with each other: operational databases, files, events, partners, and SaaS - into governed platforms analytics and AI can use. The work is contracts, quality, identity, and ownership, not another copy of the same chaos in a new lake.
Intelligence
What we do
One platform the sources can actually feed.
AI programmes stall when every source has a different customer id and a different idea of “active.” We start with the entities and the decisions, then design integration - batch, stream, and API - with quality rules and lineage so downstream models and dashboards are not joining folklore. Connecting sources is only useful if someone can trust the result.
Enabling outcome-based big data integration and governed platforms.
Canonical models and identity
Customer, product, asset, and account need a way to match across systems. We design keys, survivorship, and golden-record rules that are explicit - so AI is not trained on duplicated people.
Governance and quality
Stewards, quality rules, and issue workflows so a bad feed is a ticket, not a quiet corruption of the lake. Governance is how integration stays honest after the project team leaves.
Platforms ready for analytics and AI
Integrated data is modelled for consumption: warehouses, feature stores, and access patterns ML and BI can use without another round of heroics. We design the last mile, not only the landing zone.
Real-time and batch together
Not every source needs a stream. We combine CDC, batch, and events where each earns its place, with a clear story of what is “current” for each entity.
Lineage and audit
When a number is wrong, you need to know which source and which rule. We keep lineage and audit for integration so trust is reconstructable, not tribal knowledge.
Case studies
See the work
Clinical analytics leadership could finally trust
A regional health network needed executive dashboards and predictive models grounded in governed clinical data.
An enterprise copilot that survived legal and security review
A B2B SaaS vendor needed RAG-based product assistance grounded in customer docs - not a generic chatbot.
Fraud signals surfaced before losses hit the ledger
A payments processor needed anomaly detection that compliance could defend - not another black-box alert queue.
Why work with us?
Predictable business outcomes
Analytics and AI see complete, timely entities - fewer reconciling meetings and fewer models trained on duplicates.
Lower operating risk
Contracts, quality, and lineage make a source-system change an operable event instead of a silent break in the lake.
Modernise with confidence
Onboard sources in priority order against the decisions they feed, instead of a big-bang “ingest everything” programme.
Trends and insights
Big data integration news and perspectives
AI Adoption: Strategy Before Implementation
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 article
Is Your Technology Stack Future Ready?
Future-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 article
Signs Your Engineering Team Needs a Technical Audit
When 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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