Solutions — GDP Consultancy

Decision Intelligence

Excel and report chaos

Problem: 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.
Expected business value: Typical target: close time dropping from days to hours, a single source of truth.

Decision Intelligence

Problem: Dashboards exist but definitions are inconsistent, the model is slow, adoption is low.
What we build: A KPI dictionary, a rebuilt semantic model, a simplified dashboard set.
Expected business value: Typical target: the same metric giving the same value in every report, query times dropping noticeably.

AI Systems & Agents

Problem: 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.
Expected business value: Typical target: search and reading time dropping from days to minutes, answers with traceable sources.

Commercial Intelligence

Problem: Price and campaign decisions are made on intuition; their impact is not measured.
What we build: Price architecture + scenario model + margin and elasticity analysis + performance tracking.
Expected business value: Typical target: price decisions becoming traceable and justified.

Commercial Intelligence

Problem: Catalog, cost and inflation data is scattered; there is no regular, reliable reporting for pricing decisions.
What we build: A reporting application that brings catalog and pricing data together; periodic reports and dashboards showing inflation and cost impact.
Expected business value: Typical target: regular, single-screen visibility into pricing and inflation impact.
Legal note: Reporting is done only with data sources the company is legally entitled to access; no data collection is done in violation of regulations.

Decision Applications

Problem: The model in Excel, approval in email; the decision trail is lost.
What we build: An internal tool that brings model output and the approval step onto one screen.
Expected business value: Typical target: faster decisions, an auditable approval trail.

AI Systems & Agents

Problem: Repetitive multi-step tasks (collecting, summarizing, data entry) eat up people's time.
What we build: Human-approved agent workflows + a monitoring and eval layer.
Expected business value: Typical target: a measurable reduction in operational load, controlled automation.

AI & Analytics Enablement

Problem: Tools bought, adoption low; the team is unsure.
What we build: A role-based program: workshops, a prompt library, an internal guide, before/after measurement.
Expected business value: Typical target: the tool investment turning into real adoption.

Commercial Intelligence

Problem: New, returning and multi-channel customers aren't retained in the early period; product and service usage stays low.
What we build: Onboarding flows by customer segment, campaign setups and periodic tracking reports.
Expected business value: Typical target: higher customer retention, rising revenue and product count per customer.

Decision Applications

Problem: The decision model or analytics output is ready but doesn't become a real product the team can use; development drags on, delivery slips.
What we build: A clarified product spec, a working MVP that carries the decision model (internal tool, web or mobile), a repeatable build and test flow.
Expected business value: Typical target: a first release in weeks, predictable iterations afterward.