Ingestion and integration
We connect operational systems, files, events, and SaaS sources with contracts on freshness and schema. Breaking changes are detected; silent drift is not an acceptable status.
Data Engineering
Modern pipelines, ETL, and large-scale processing that stay reliable in production.
Agam Intelligence builds the unglamorous path data has to travel: ingestion, transformation, orchestration, and quality checks with owners. Pipelines are software - monitored, tested, and handed over - so analytics and AI are not waiting on a job that failed at 2am.
Intelligence
What we do
Pipelines you can run on a Monday morning.
AI and BI only look smart when the data arrived. We design ETL/ELT, streaming where it is warranted, and orchestration with clear SLAs - plus data quality tests and lineage so a break is visible before a dashboard goes quiet. Our large-scale processing heritage is why we treat pipelines as production systems, not a folder of scripts.
Enabling outcome-based data engineering and production pipelines.
ETL, ELT, and transformation
dbt, Spark, and SQL modelling chosen for volume and team skill. Transformations are reviewed, tested, and documented so a metric is not a mystery stored procedure.
Orchestration and SLAs
Airflow and similar schedulers with retries, alerting, and dependency graphs that match how the business expects data to land. A pipeline without an owner and an SLA is a hobby.
Data quality and observability
Volume, nulls, uniqueness, and freshness checks that page the right team. We add lineage so you can see what broke downstream of a source, not only that “the dashboard is empty.”
Batch and stream processing
Most organisations need reliable batch first. We add Kafka, Flink, or similar when latency actually matters - not because a diagram looked more modern.
Handover and ownership
Documentation, runbooks, and pairing so your team can operate the platform. We do not design pipelines that only we can debug.
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
Freshness SLAs and quality gates mean analytics and AI see data when they expected it - not after a silent weekend failure.
Lower operating risk
Tests, lineage, and alerting turn pipeline failure into an operable incident instead of a mysterious empty dashboard.
Modernise with confidence
Replace brittle scripts and unowned jobs in slices, keeping the feeds the business cannot pause.
Trends and insights
Data engineering 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.
Read more