We deliver modern data engineering, analytics, and applied AI with the discipline enterprise teams require. Clear definitions, clean ownership, measurable outcomes — built to last beyond the engagement.
The problem with most data consultancies
Dashboards built fast but maintained by nobody — breaking silently in production
KPI definitions that differ by team, making every leadership meeting a negotiation
Consultants who disappear after delivery, leaving code nobody understands
AI models that impress in demos but never make it to trusted production use
How Analytika Systems is different
Built to be owned, not depended on
Every engagement ends with documented runbooks, clean ownership models, and a team that understands what was built.
One definition of truth across every team
We build metric catalogues and semantic layers that give finance, product, and leadership the same numbers.
AI that ships to production, not just pilots
Feature pipelines, evaluation frameworks, drift monitoring — everything needed for models to make real decisions reliably.
Enterprise security and governance from day one
Access design, PII handling, lineage, and audit readiness are first-class requirements — not retrofit work.
Core capabilities
We build end-to-end data capabilities with strong engineering habits and clear consulting deliverables. Each engagement is scoped around outcomes and long-term maintainability.
Ingestion, transformation, orchestration, and serving layers built to be observable, recoverable, and cost-aware from the ground up.
Governed KPI layers, semantic models, and executive-ready reporting that stays consistent across teams — however the business evolves.
Feature pipelines, model evaluation, deployment patterns, and monitoring so AI improves actual decisions — in production, not just in pilots.
Practical governance that reduces risk without slowing your delivery teams. Data quality, lineage, access design, and audit readiness that fits your culture.
How we work
Engagements stay structured and transparent, with clear checkpoints that reduce risk and create decision points for your team.
Discover
We map stakeholder needs to measurable goals, then identify the smallest path that delivers real value — before writing a line of code.
Design
We align on contracts, ownership, and controls so your delivery teams can scale without constant firefighting and rework.
Deliver
Pipelines, dashboards, and models ship with monitoring, documentation, and a stewardship plan your team can actually follow.
Proof
Client details are generalised for confidentiality. These patterns reflect what we actually deliver across different industries.
Reduction in month-end reconciliation
A national financial services group had separate reporting stacks across business lines. KPI definitions differed by team. We defined a metric catalogue, built a semantic layer, and introduced quality gates on key dimensions.
Faster experiment analysis cycles
An ecommerce brand scaling internationally had fragmented customer identity across events, orders, and support. We implemented event schema governance, built identity resolution, and delivered a curated customer dataset.
Improvement in retention outreach precision
A subscription business lacked prioritisation in retention outreach. We built a feature pipeline, trained a churn risk model, and designed a scoring workflow with drift monitoring and controlled score delivery.
Dashboard reliability during peak periods
A logistics organisation had data latency and missing events making operational dashboards unreliable at peak. We introduced event contracts, handled late-arriving data, and added SLA monitoring with incident runbooks.
What clients say
The team delivered a metric catalogue we can actually maintain. Every definition is documented, every pipeline has a runbook. Our engineers understood everything that was built — the handover was the cleanest we've experienced with any partner.
Head of Data
National financial services group
Before this engagement, every experiment was a debate. Now we have a single customer dataset everyone trusts. The identity resolution work alone saved us weeks of analyst time every month.
VP of Product Analytics
International ecommerce brand
We'd tried twice to ship a churn model into production. Analytika was the first team that asked about monitoring, rollback, and operations before writing any model code. That's the difference between a pilot and a product.
Director of Data & Insights
Subscription business, 2M+ customers
Insights
When finance and product define "active user" differently, every leadership meeting becomes a negotiation instead of a decision.
Read articleSchema drift between producers and consumers breaks pipelines silently. Here's how contracts prevent it before it costs you an outage.
Read articleSemantic layers solve real problems, but only when the underlying data models are stable enough to justify the investment.
Read articleSend us a short message with your goals and constraints. We respond with a clear plan, realistic timeline, and concrete next steps — not a sales deck.
info@analytikasystems.com · Remote delivery · Enterprise time zones