We turn data into working decision systems
We turn scattered reports, models and AI capabilities into the dashboards, internal tools, agent workflows and reporting automation your teams use every day.
15+ years of FMCG commercial leadership · Data science rooted in banking and consulting · One team, one architecture
Data, AI, commercial analytics and decision interfaces: one team, one architecture.
Plenty of data. No decision system.
In most companies the problem is not missing data; it is the missing structure that connects the pieces into one system.
- Reports exist; a single source of truth does not.
- Models exist; they are not wired into daily work.
- AI pilots exist; they never reach the process.
services
Five service areas, one goal: working decision systems.
Every area starts with the same question: which decision, with which data, made by whom, how often?
Decision Intelligence
Data modeling, KPI architecture, dashboards and reporting automation. From scattered spreadsheets to a single source of truth.
- KPI dictionary
- Single source of truth
- Management report
AI Systems & Agents
Document analysis, research automation and human-approved agent workflows. Systems that live in the process, not demos.
- Document analysis
- Research automation
- Human-approved workflows
Commercial Intelligence
Revenue Growth Management (RGM), pricing, retention and subscription economics models. Marketing and growth analytics included.
- Price architecture
- Churn analysis
- Forecasting
Decision Applications
We turn decision models into internal tools, web and mobile apps. Decision interfaces, not generic app development.
- Internal tools
- Field app
- Dashboard product
AI & Analytics Enablement
Role-based AI and Power BI training, prompt libraries. Executive-level AI literacy programs.
- Role-based training
- Prompt library
- Executive briefing
Five steps, one map.
- Map the decision problem Which decision, with which data, made by whom: this comes first.
- Structure the data Sources are connected; the data model and KPI dictionary are built.
- Build the intelligence layer Dashboard, model or agent: the system is built and tested by measurement.
- Design the decision interface The output becomes the screen the team uses every day.
- Enable the team Training and handover leave the capability with the team.
Solution areas
The problem areas we work in.
From Excel and report chaos to a single source of truth
The month-end report is compiled by hand from five people's Excel files; it takes days and is error-prone.
What we build: A consolidation pipeline + KPI dictionary + automated management report.
AI document analysis and research automation
Stacks of contracts, reports and regulations are read by hand; finding an answer takes days.
What we build: A document intelligence pipeline: classification, summary, field extraction, sourced Q&A.
Pricing and commercial performance models
Price and campaign decisions are made on intuition; their impact is not measured.
What we build: Price architecture + scenario model + margin and price elasticity analysis + promotion ROI and performance tracking.
Decisions first, technology second.
GDP is built on two complementary strengths: 15+ years of FMCG commercial and marketing leadership (pricing, channel, customer growth, management reporting) and data science with technical architecture experience from banking and management consulting. The commercial question and the technical solution are built at the same table.
- Not a demo, a system in use.
- We don't promise outcomes we can't prove.
Lab
Decision simulation for mid-sized companies: less room for error
Decision simulation means testing a commercial decision against historical data and scenarios before implementing it in the real world: “What would have happened if we had made this decision?” For mid-sized companies, this is even more critical than it is for large enterprises, because the relative cost of a bad decision is much heavier for a smaller business. While large corporations focus on the most advanced tools, mid-sized FMCG and retail companies are often left in a blind spot.
Advisory · 5 min readConsultancy as code: not a consulting report, but a decision prescription
Consultancy as code means designing the consulting deliverable as an executable decision prescription rather than a static report. A traditional consulting report offers an impartial analysis and leaves the reader alone with the question, “So what should we do?” This Lab does something different: each article is not merely an analysis, but an actionable decision framework. Not an idea, but a prescription.
Decision Simulation · 5 min readMulti-agent decision lab: a simulation in which agents review one another
A multi-agent decision lab does not leave a decision to a single artificial intelligence model. Instead, agents with different roles, a proposer, a challenger, and a judge, test one another’s reasoning. The decision is not the output of one model, but the result of a debate. The reason is simple: a single AI agent cannot see its own blind spot.
Let's talk.
A 30-minute intro call. No sales deck: we listen to how your data and decisions work today, and share a clear first read on where to start.
Book an intro call