Skip to main content
Aynitech Factory
EN ES
background-header
Work / The Analytics Foundation for an AI-Driven SaaS Company

The Analytics Foundation for an AI-Driven SaaS Company

Cloud Analytics Platform + DWH / Data Lake + Customer Dashboards · Descriptive Today, AI-Ready

Client
Challenge

ARM, a SaaS provider, set out to become a fully digital, AI-driven company — with an ambitious roadmap that includes a Zero Touch Close platform, an AI Actuary and an AI Co-pilot, and a broader digital transformation that automates manual work. None of that is possible without the foundation underneath it: a cloud analytics platform able to answer every business question about ARM’s SaaS product — both for ARM’s own teams and for ARM’s customers — and engineered so that AI can be layered on later.

Factories

Business Analytics + Data Engineering

Engagement
Project

The Approach

AYNITECH structured the work to start descriptive and set the stage for AI. The first phase deploys a cloud analytics platform that supports descriptive analytics today and autonomous (AI) analytics tomorrow: identifying the data sources, designing and building the ETL processes from them, modelling a data warehouse that answers every business question around ARM’s SaaS product, and designing the dashboards that surface those answers for ARM’s customers. It is delivered through a structured strategy — separate Development, QA and Production environments; a DWH and data-lake model; and storytelling dashboards built to be self-explanatory and easy to drill into — serving three distinct user groups: technical (data governance, infrastructure, access), analysts (cubes, datamarts, the DWH and the analytical canvas), and business users (desktop and mobile dashboards and reports). The engagement is run by a multidisciplinary team spanning project management, a subject-matter expert, data engineering, big-data architecture, senior and junior business-analytics engineering and UX/UI design.

The Analytics Maturity Path

The platform is deliberately the first rung of a four-stage climb:

  • Descriptive — what happened, and why — business intelligence and analytics (the focus of this phase).
  • Predictive — what is likely to happen, over static data — statistical modelling.
  • Prescriptive — what to do, over dynamic data — machine-learning models deciding outcomes under human supervision.
  • Autonomous — the algorithms deciding on their own — artificial intelligence.

On the Roadmap

With the foundation in place, later phases extend the platform:

  • Internal business dashboards and reports for ARM, alongside its customer-facing dashboards

  • RPA robots to automate ARM’s manual processes.

  • AI engines — including the AI Actuary and AI Co-pilot — moving ARM up the maturity curve toward predictive, prescriptive and ultimately autonomous analytics

At a Glance

  • A cloud analytics platform built to support descriptive analytics now and AI later

  • ETL from identified sources into a DWH and data-lake model covering every SaaS business question

  • Customer-facing and internal dashboards — self-explanatory, easy to drill into

  • Three user groups served (technical, analysts, business users) across Dev, QA and Production

  • A clear path from descriptive to predictive, prescriptive and autonomous analytics

Why It Matters

AI ambitions fail without the data foundation to stand on. ARM shows AYNITECH building that foundation on purpose — a governed cloud platform, a well-modelled warehouse and data lake, and dashboards that answer the real business questions — engineered from day one so predictive, prescriptive and autonomous capabilities can be added on top. For a SaaS company that wants to deliver analytics to its own customers and eventually run an AI Actuary and Co-pilot, getting this layer right first is exactly what makes the rest achievable.