A major healthcare supply organization under WHO struggled to understand and respond to fluctuating medical demand. Their demand signals were scattered across documents and exports, making forecasting and supply planning slow, inconsistent, and reactive.
The problem
Key challenges included:
- Fragmented demand data across PDFs, Excel files, hospital MIS exports, and other formats
- Inconsistent table structures, drug naming conventions, and demand units
- No unified view for forecasting or supply chain analytics
- Limited ability to perform deep analysis or extract strategic insights from raw data
The solution
We built an AI system designed to unify, standardize, and analyze medical demand data with high precision, while enabling stakeholders to generate insights instantly.
High-accuracy table extraction and schema standardization
A custom extraction pipeline delivered 97%+ accuracy for demand tables across multi-format inputs. Extracted data was normalized into a single schema that consistently mapped:
- salt / generic names
- medicine / brand names
- demand quantities
- dosage and unit conventions
- facility / department / time-period metadata
This standardization removed ambiguity and made cross-source analytics reliable.
Unified aggregation and instant visualization
An AI-powered backend enabled multi-source aggregation of demand data across departments, facilities, and time periods. On top of it, a responsive dashboard delivered real-time analytics so teams could quickly spot:
- emerging shortages
- regional spikes
- seasonal trends
- facility-level anomalies
Analytical co-pilot for deep insights
We integrated a quantitative + qualitative AI Co-Pilot that supports natural-language querying for complex planning and forecasting workflows, including:
- stockout risk forecasting
- regional demand variation analysis
- optimal procurement planning and reorder strategy
- trend explanations with contextual evidence
The co-pilot was built using LangChain + Tensorflow + SciKit, enabling interactive analytics without requiring users to write queries or build manual reports.
Results
The platform delivered measurable improvements in operational speed and strategic decision-making:
- Unified analytics dashboard for instant, aggregated visibility
- Dramatic efficiency gains in procurement planning through AI-driven insights
- More informed decisions with contextual, data-backed recommendations
- 80% faster demand processing and competitive analysis