Services — GDP Consultancy

Decision Intelligence

Definition: Connects your metrics to a single source of truth: KPI architecture, data model, dashboards and reporting automation. Power BI is the tool here; the message is the architecture.
Problems it solves: The same metric in three reports with three different values. Month-end close taking days through manual Excel consolidation. Dashboards no one opens. Reporting that depends on a single person.
Example deliverables: A KPI dictionary and hierarchy. A Power BI semantic model and dashboard set. Reporting automation with scheduled refresh and distribution. An Excel consolidation pipeline.
Example project types: A management reporting system from scratch. Modernizing existing sales and finance dashboards. Moving off Excel-based reporting.

Definition: AI systems tied to defined workflows: document analysis, research automation, internal copilots and human-approved agent workflows.
Problems it solves: Reading stacks of contracts and reports by hand. Repetitive research work. Slow access to institutional knowledge. AI pilots that stall at PoC and never reach the process.
Example deliverables: A document intelligence pipeline (classification, summary, field extraction, sourced Q&A). Research automation. An internal knowledge copilot. A human-approved agent workflow plus an eval set for quality measurement.
Example project types: A contract and document analysis system. Market research automation. A support or operations copilot.

Definition: Commercial models that tie price, customer and channel decisions to data: pricing, retention, subscription economics, forecasting and commercial performance measurement.
Problems it solves: Price and campaign decisions made on intuition, with their impact unmeasured. Churn tracked without knowing its causes. Competitor prices monitored irregularly, by hand.
Example deliverables: A pricing model and scenario tool. Retention and churn analysis with an action list. Subscription metrics (MRR, cohort, LTV). Retail price intelligence (automated competitor price and catalog tracking). Forecasting and optimization models. CRM data structure and segmentation. Digital growth measurement (including SEO and GEO; not a separate agency service, but part of the commercial measurement framework).
Example project types: Setting up a price architecture. A subscription growth program. A competitor price intelligence system.

Definition: Moves decision models that live in Excel and notebooks into real interfaces the team uses: internal tools, web and mobile apps, dashboard products.
Problems it solves: The model sitting idle in a static file while the field can't reach it. Approval flows lost in email chains.
Example deliverables: An internal decision tool (for example a price approval screen: model output and approval step on one screen). An operational web app. A mobile app for the field. A dashboard product opened to customers and external users.
Example project types: A price approval tool. A field sales app. An operations panel.

Definition: Gets teams using the systems and AI tools effectively: role-based training, workshops, a prompt library and executive-level AI literacy.
Problems it solves: Tools bought, adoption low. The team distrusts AI output, or trusts it wrongly. Management can't assess the return on the investment.
Example deliverables: A role-based training program (AI, Power BI, prompt engineering). Hands-on workshops. An in-house prompt library and usage guide. An executive briefing: which decisions to delegate to AI and which not to. Before-and-after adoption measurement.
Example project types: A company-wide AI literacy program. A Power BI deep-dive for the analytics team. An executive briefing series for the leadership team.