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How a pharmaceutical wholesaler cut collection delays and stock issues

Pharmaceutical wholesaler

saved per week by the administrative team

6h

customer collection delays after 3 months

−20%

customer collection delays after 3 months

−20%

stockouts and overstock from month 1

−35%

stockouts and overstock from month 1

−35%

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Pharmaceutical wholesale depends on coordinating commercial activity, administration and stock planning. In this project, the information needed to support those decisions was available, but the connections between sales, billing and outstanding receivables were insufficient.

The administrative team needed visibility into billing and collections, while inventory planning needed more reliable forecasts. The project brought these needs together around a shared data foundation and business-specific applications.

Challenge

Billing was prone to errors and lacked a dedicated monitoring dashboard. Outstanding customer receivables were difficult to follow, leaving collection work without a clear order of priority. Unreliable stock forecasts contributed to recurring stockouts and overstock.

These issues shared a common cause: the data existed, but it was not connected in a way that supported timely decisions. Finance and inventory teams needed a consistent foundation for understanding what had been sold, what remained outstanding and what stock would be needed.

Solution

The project established a centralized ETL process to bring sales, billing and customer receivables into a single source. On top of that foundation, the team introduced a real-time billing dashboard and a dedicated business application for forecasting.

  • Centralized sales, billing and outstanding receivables through an ETL pipeline.

  • Introduced a real-time dashboard for monitoring billing activity.

  • Made outstanding receivables easier to follow and collection priorities easier to establish.

  • Built a dedicated business application for sales and inventory forecasting.

Results

The administrative team saved six hours per week. After three months, customer collection delays had fallen by 20%. The source also reports a 35% reduction in stockouts and overstock from the first month.

The improvements addressed both administrative workload and operational visibility. Connected financial data supported more focused collection work, while the forecasting application helped teams plan inventory with a clearer view of expected sales.

These figures are the project results reported in the supplied case material. The source does not specify the detailed measurement methodology or separate the stockout and overstock measures.

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An open-source foundation for building your own agents.

ApowerB is the open-source agentic framework developed by thaink² to build, orchestrate, and operate AI agents on your own stack, using your models, tools, and data.

What if your next analysis were ready before your next meeting?

Show us your environment and a use case. See how thaink² can query, monitor, and act on your data.

Our mission
Our vision

Put the power of data in the hands of decision-makers.

Today, too many business questions still depend on an export, a dashboard, or the availability of a Data team. thaink² changes that by letting teams query their data directly, while agents handle the analysis and recurring work.

Image

What is an Agentic Data Platform?

An Agentic Data Platform uses specialized AI agents to work with enterprise data. Unlike a static dashboard or a simple chatbot, agents can explore multiple sources, run analyses, generate visualizations, produce deliverables, and execute recurring missions.

What’s the difference between Proactive Mode and Exploratory Mode?

In Exploratory Mode, the user asks a question and thaink² runs the analysis on demand. In Proactive Mode, a mission is defined in advance: agents monitor the data based on a set schedule or specific conditions and automatically deliver the relevant results.

Do I need to know SQL to use thaink²?

Not for business use cases. Users can ask questions in natural language and get analyses, tables, charts, and dashboards without writing SQL queries themselves.

What data sources can thaink² connect to?

The platform is designed to work with the systems already in place across your organization: databases, data warehouses, ERP, CRM, APIs, files, and document repositories. Available connectors depend on your environment and use case.

What types of outputs can the agents produce?

Depending on the agent and use case, outputs can include analyses, tables, charts, dashboards, forecasts, alerts, summaries, and reports. The goal is to deliver an actionable result, not just a text response.

Can thaink² be deployed on our own infrastructure?

thaink² supports deployment options designed around enterprise requirements, including organizations that need greater control over their infrastructure, models, and data. Our teams help define the architecture that best fits the project.

Which part of the thaink² ecosystem is open source?

ApowerB is the open-source agentic framework developed by thaink². It enables technical teams to build and operate their own agents, with support for RAG, Text-to-SQL, multi-LLM setups, and APIs.

How do I get started with thaink²?

The easiest way to start is with a concrete use case: reporting, analysis, forecasting, document search, or monitoring. An initial session helps define the available data, expected outcomes, and the most suitable deployment approach.

Join our Discord community

Connect with builders, Data teams, and AI practitioners. Share ideas, get technical help, discuss agentic architectures, and follow the latest developments around aPowerB.

Our newsletter (Soon)

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Field insights, agentic architectures, benchmarks, use cases, and the latest from ApowerB. Only what’s worth reading.

© 2026 thaink² SAS — All rights reserved