Case studies→Asset Performance Management Platform
Case Study · 01

From concept to MVP
in fifteen weeks.

We partnered with a European industrial software company to architect, engineer, and deliver a cloud-native AI platform for asset-intensive industries — at roughly a quarter of the conventional timeline.

01By the numbers
15wks
Total delivery, end-to-end
9–12mo
Traditional estimate, conventional team
4
Core modules built & integrated
~75%
Time compression vs. baseline
SectorIndustrial software · asset performanceEuropean, bootstrapped
End marketsEnergy · mining · metals · chemicalsAsset-intensive, ERP/EAM-heavy
EngagementSprint-based · senior-ledEmbedded in client product workflow
02Client context

A bootstrapped European industrial software company set out to replace reactive, manual asset management with an intelligent, data-driven platform — one that reveals value, recommends action, and drives measurable outcomes for energy, mining, metals, and chemicals operators.

03The brief

Challenges

  • Translate deep domain expertise in asset management into a scalable SaaS product.
  • Build complex data pipelines from heterogeneous ERP/EAM sources — SAP, IFS, Oracle, IBM.
  • Implement AI-powered performance mining and benchmarking algorithms.
  • Deliver production-quality software on a bootstrapped budget — scope a traditional team would estimate at 9–12 months.
  • Design for enterprise-grade reliability while keeping startup agility.

Our approach

  • Sprint-based delivery with senior engineers embedded in the client's product workflow.
  • AI-accelerated development using Claude Code and modern toolchains for rapid iteration.
  • System-agnostic data connectors that unify SAP, IFS, Oracle, and IBM into a single analytics layer.
  • Proprietary performance mining, benchmarking, and recommendation engines as the intelligence layer.
  • Modular architecture — independent build & deploy, fortnightly client reviews throughout.
Architecture · 04

Four integrated modules, one platform.

01 · Ingest

Data Sourcing

ERP/EAM integration & data pipeline. Connectors for SAP, IFS, Oracle, IBM into a unified analytics database.

02 · Analyse

Performance Miner

AI analysis & benchmarking. Surfaces hidden value, anomalies, and improvement opportunities at scale.

03 · Plan

SAMP Builder

Strategic asset management planning & roadmap. Translates findings into a costed, phased programme.

04 · Execute

Optimizer

Initiative tracking & OKRs. Closes the loop between recommendation, action, and measurable outcome.

Modular by design — each step deploys independently. One layer, four loops, continuous feedback.
Outcomes · 05

What we built. How we shipped it.

A senior-led, sprint-based engagement with AI-accelerated tooling — compressing a 9–12 month build into a fortnightly cadence the client could review, steer, and trust.

06Our contributionFull-stack delivery across product, data, AI, and go-to-market readiness.
A · Product engineering

Full-stack product build

Database schema design, data pipeline architecture, front-end UI, AI algorithm implementation, and cloud deployment configuration — under one team.

B · Data engineering

Multi-source ingestion

Connectors for industrial ERP/EAM data, transformation pipelines, and a structured analytics database optimised for performance mining queries.

C · AI / ML

Intelligence layer

Proprietary performance mining algorithms, benchmarking engines, and intelligent recommendation systems for improvement initiatives.

D · Strategic consulting

Architecture & GTM readiness

Product architecture decisions, UX workflow design, and the technical readiness that underpins a defensible enterprise go-to-market.

07Delivery model15 weeks end-to-end. A 3-week architecture phase, then 6 × 2-week sprints.
Phase 01

Architecture & design

Domain immersion, technical scoping, system architecture, data model definition, detailed design documentation.

3 wks · WK 01 — 03
Phase 02

MVP build

Six fortnightly sprints. Iterative, senior-led, AI-accelerated delivery across all four modules in parallel.

12 wks · 6 × 2-wk · WK 04 — 15
Cadence

Fortnightly reviews

Integrated testing, working-software demos, continuous refinement — the client steered every two weeks.

Throughout · Every 2 wks
Total

15 weeks

End-to-end delivery of scope a conventional team would estimate at 9–12 months — a ~75% time compression.

~75% saved · vs. baseline
08Key outcomes
Production-ready MVP with four fully functional, integrated modules, ready for client deployment.
~75% time compression — 15 weeks vs. a 9–12 month traditional estimate, via AI-accelerated engineering.
Enterprise-grade data pipelines supporting multiple ERP/EAM systems with automated and manual ingestion paths.
AI-powered analytics — performance mining, benchmarking, and intelligent improvement recommendations.
Cloud-native architecture designed for scale, enabling enterprise-customer onboarding from day one.
On-track for cloud launch, positioned for market entry across oil & gas, mining, metals, and chemicals.
09Technology stack
PythonReactCloud InfrastructureAI / ML AlgorithmsAI Coding ToolsETL PipelinesREST APIsData ModellingPerformance Mining
An indicative model

The same shape, applied to your build.

Senior-led delivery, AI-accelerated tooling, a fortnightly cadence the client steers. We can apply the same model to your platform.

Emailcontact@eightgen.ai
CallBook a kickoff meeting

15 weeks, four integrated modules, one cloud-native platform — built the way modern software should be.

Eightgen AI · London & BangaloreFounded 2024