Saint-Gobain Romania has just completed a significant milestone: the migration of 13 Romanian plants to S/4HANA, Snowflake, and Power BI. This modern infrastructure is now in place — the next step is to extract its full strategic value.
The data team's ambition goes beyond operational reporting. The goal is to generate insights that drive sales and market share — understanding competitors, anticipating market dynamics, and automating manual back-office processes that slow down the business.
Intelactsoft is an ~80-person Romanian tech firm with two divisions — Software Engineering and Data Management — and direct, hands-on experience connecting SAP data to Snowflake, Power BI, and Qlik. We understand both the technology and the business context.
Saint-Gobain Romania has completed an S/4HANA migration across 13 Romanian plants. The new data infrastructure is live and operational:
| Layer | Platform | Status |
|---|---|---|
| ERP | SAP S/4HANA | Fully migrated across all 13 Romanian plants. Core operational system of record. |
| Data Warehouse | Snowflake | Modern cloud data platform in place. Ready to serve as the foundation for advanced analytics. |
| BI / Reporting | Power BI | Deployed for operational reporting. The next step is moving beyond standard reports to strategic insights. |
The migration is complete — but the data team's ambition is not yet matched by the analytics outputs being produced. The infrastructure can support much more than it currently delivers.
Current reporting is largely operational — tracking what happened. The goal is to answer "why" and "what next": market share, competitive dynamics, growth opportunities.
Order entry still involves manual processing of unstructured inputs (e.g., handwritten scans), creating delays, errors, and high manual effort in back-office teams.
No systematic process for monitoring competitor pricing, promotions, or market activity. This information exists online but is not being captured or integrated into decision-making.
Saint-Gobain operates in a market where pricing, promotions, and product availability shift frequently. Currently, there is no systematic way to capture this market intelligence. Two concrete techniques were discussed:
Automated collection of competitor pricing and promotional data from public web sources — enabling regular, structured comparison against Saint-Gobain's own pricing strategy.
Structured monitoring of competitor activity and market perception across social channels — surfacing trends, sentiment, and product feedback before they become visible in sales data.
Competitor sites, marketplaces, social media
Centralised, structured competitor data
Pricing trends, market signals, actionable alerts
A significant portion of order entry involves processing unstructured inputs — handwritten order scans, PDF documents, and non-standard formats — that require manual transcription into SAP. This is slow, error-prone, and costly at scale.
Back-office staff manually interpret and re-enter unstructured order data into SAP. High error rate, slow processing time, and difficult to scale during peak periods.
Large Language Models extract structured data from unstructured inputs (scanned orders, emails, PDFs) and populate SAP fields automatically — reducing human error and processing time dramatically.
Handwritten scans, PDFs, emails
Extracts structured order fields from raw documents
Automated order entry — validated, traceable, fast
One of the most common failure modes in data projects is building technically before the business problem is precisely defined. The model separates these two phases deliberately — Saint-Gobain defines what needs to be built, Intelactsoft builds it well.
Gabriel / Saint-Gobain defines the business problem, KPIs, and expected ROI
Joint alignment on scope, success criteria, and timeline
Intelactsoft builds the solution — pipelines, models, dashboards
Review results, measure impact, define the broader roadmap
Gabriel and the Saint-Gobain team own the strategic direction. They define the business problem, prioritise use cases, and set the success criteria.
~80-person Romanian data consultancy with two divisions: Software Engineering and Data Management.
Intelactsoft is a technology services company specialising in Data Management, Digitalization, Software Modernization, and Staff Augmentation. We partner with leading organisations to build next-generation digital experiences — combining elite engineering talent with a culture of precision, agility, and ownership.
While top companies digitally transform to make better performance possible, we use cutting-edge technology to push the boundaries of what's possible. In 2026, that means putting AI at the core of everything we build and deliver.
Based in Romania — same time zone, direct communication, no travel overhead. Familiar with the realities of operating within a multinational group structure.
25 Data Engineers covering the full data management and BI stack — SAP extraction, Snowflake, Power BI, Qlik, Talend, Databricks and more.
Our engineers are equipped with Cursor AI, GitHub Copilot, and custom AI workflows — delivering faster, leaner, and at higher quality than traditional outsourcing.
Two divisions — Software Engineering and Data Management — with sufficient scale to staff a dedicated team without subcontractors.
We partner with top global corporations and companies that dream big — delivering measurable outcomes through modern data engineering and AI-augmented delivery.
A team of 25 Data Engineers covering the full data management and BI stack:
| Area | Technologies |
|---|---|
| Business Intelligence | Qlik View, Qlik Sense, Qlik Sense Cloud, Power BI |
| Cloud Data Warehouse | Snowflake, Databricks |
| Data Integration & ETL | Talend, Denodo, custom ETL pipelines, SAP extraction |
| Data Modelling & Query | SQL, dimensional modelling, data vault |
| Operational Apps | Streamlit (on Snowflake), lightweight web apps |
| Source Connectivity | SAP, OPC UA, Splunk, REST APIs, flat files |
| Cloud & Infrastructure | Azure, AWS, Docker, GitHub |
| AI-Augmented Delivery | Cursor AI, GitHub Copilot, custom AI workflows |
Intelactsoft can engage in different ways depending on what makes most sense at each stage. The PoC is the natural starting point — but beyond that, the collaboration can be structured to fit the local team's workflow, budget cycle, and internal approval processes.
Intelactsoft can be engaged in three ways, which can be combined or adjusted as the partnership evolves.
For defined deliverables with clear scope. The right starting model — fast to engage, low risk, concrete output. Ideal for the production report PoC.
Skilled consultants working alongside the local team on a sustained basis — each equipped with AI co-pilots that multiply individual output.
For architecture decisions and roadmap planning. Useful when preparing internal business cases or defining the data strategy for the next phase of investment.
For our clients: faster delivery, lower cost, same quality bar — validated by our senior engineers.
Reduction in time spent on maintenance & legacy code — Data Engineering
Feature velocity on custom software projects — Software Dev
QA and debugging time with AI-assisted testing — Quality Assurance
Migration speed on Qlik → Snowflake programs — Data Migration
| Dimension | Classic Outsourcing | Intelactsoft AI-Augmented |
|---|---|---|
| What you buy | Developers per day | ✓ Accelerated delivery outcomes |
| Team composition | Many juniors + a few seniors | ✓ Lean, senior-heavy + AI co-pilots |
| Speed | Linear with headcount | ✓ Non-linear — AI multiplies output |
| Cost | Fixed cost per FTE | ✓ Lower cost per feature delivered |
| Data migrations | Manual, slow, error-prone | ✓ AI-accelerated pipeline & model generation |
| Knowledge dependency | Key-person risk | ✓ AI-documented code, transferable knowledge |
Complex, high-stakes architectural choices require human expertise. Our seniors own these.
Discovery, negotiation, and trust are built by people. Our account leads are your partners.
Supply chain, finance, healthcare — complex domain rules are validated by our specialists.
Every AI-generated output is reviewed and validated. We define what AI can and cannot do unsupervised.
Our meeting concluded with a clear path forward. The immediate priority is to define the POC use case(s) so we can arrive at the follow-up meeting ready to align on scope, timeline, and success criteria.
Gabriel to identify and prioritise the specific use case(s) Saint-Gobain would like to validate through a Proof of Concept — from the two areas discussed (competitive intelligence or back-office automation), or another priority area.
All parties to align on a date for the follow-up meeting — targeting approximately two weeks from the initial discussion. Intelactsoft will come prepared with a POC scope proposal based on the defined use cases.
Once use cases are confirmed, Intelactsoft will prepare a structured POC scope document — covering objectives, deliverables, timeline, team, and success criteria — for review at the follow-up meeting.
