Faster daily and intraday market interpretation
Scheduled ingestion and analysis bring daily and intraday market inputs into one review workflow.
AI product case study
Commodity Forecasting & Market Intelligence
A desktop first intelligence platform combining market data, technical signals and AI assisted forecasting.
A commodity intelligence platform was built for professional market participants, starting with the sugar market. The product is designed for traders, brokers, sugar mills, procurement teams, commodity analysts, advisory teams, and institutional commodity desks that need faster interpretation of market signals.
Project classification: This project showcases a desktop first commodity intelligence product for professional market participants.
Consolidated technical, macro and scenario signals
Technical indicators, macro drivers and scenario outputs are consolidated in the desktop product.
Clearer decision support for professional commodity users
Reports and scenario views are designed to support professional interpretation rather than replace trading judgement.
01 / Business context
Who needed the solution
A commodity intelligence platform was built for professional market participants, starting with the sugar market. The product is designed for traders, brokers, sugar mills, procurement teams, commodity analysts, advisory teams, and institutional commodity desks that need faster interpretation of market signals. The pitch deck positions the product as an AI powered forecasting and market intelligence system for commodity decision makers, starting with sugar, with structured insights, technical signals, and decision ready analytics. The platform was built as a premium desktop first analytics product with controlled user access, licensing, company level usage, concurrent user plans, and a recurring subscription model.
02 / Challenge
What needed to change
Commodity teams do not suffer from a lack of data. They suffer from scattered signals and slow interpretation.
Market signals were spread across several sourcesMarket data spread across multiple sources.
Charts and technical indicators required manual interpretationManual chart and indicator interpretation.
Daily and intraday forecasts took time to prepareSlow daily and intraday forecast preparation.
Teams lacked one structured scenario viewLack of structured scenario based market guidance.
Commercial access required licensing, sessions and device controlsNeed for commercial access control, licenses, sessions, and device control for paid users The goal was to build a product that converts raw market signals into decision ready commodity intelligence.
03 / Workflow transformation
Before and after
Previous workflow
- Market signals were spread across several sources
- Charts and technical indicators required manual interpretation
- Daily and intraday forecasts took time to prepare
- Teams lacked one structured scenario view
- Commercial access required licensing, sessions and device controls
Structured workflow
- Ingest market signals
- Compute forecasts and indicators
- Generate decision ready intelligence
- Control commercial access
- Review structured outputs
04 / Solution
How we approached it
The solution was designed as a three layer product architecture: 1. Desktop Product Experience A desktop first application gives users a premium analytics experience with dashboards, live indicators, forecast cards, reports, scenario panels, and structured market summaries.
Ingest market signals
Bring price history, indicators, macro drivers and reports into one analytics workflow.
Compute forecasts and indicators
Run scheduled forecasting, support/resistance and directional bias calculations.
Generate decision ready intelligence
Present scenarios, tactical summaries and forecast versus actual reviews.
Control commercial access
Manage tenants, licences, devices, sessions and subscription ready access.
05 / Example workflow
From input to reviewable output
1Ingest market signals
2Compute forecasts and indicators
3Generate decision ready intelligence
4Control commercial access
5Review forecast against actuals
6Professional decision review
06 / Delivery scope
What the delivery covered
Business and product workflow
- Daily And Intraday Forecasting
- Technical Indicator Snapshots
- Support And Resistance Tracking
AI, data and automation
- Azure OpenAI
- LangGraph
- LangChain
Application and cloud engineering
- React
- Tauri
- Python
- FastAPI
- NestJS
- PostgreSQL
- Docker
Scope boundary
Forecasts and scenario outputs are decision support inputs for professional users. The case study does not claim a verified accuracy rate or financial outcome.
07 / Architecture and controls
How the system is organised
Azure OpenAI
LangGraph
LangChain
React
Tauri
Python
FastAPI
NestJS
Forecast evaluation
Forecast versus actual review supports ongoing evaluation rather than presenting model output as certain.
Role and licence controls
Tenant, device, session and licence controls protect commercial access to the desktop product.
Decision support boundary
The platform supports professional analysis and does not replace independent trading or financial judgement.
08 / Business value
Expected operational value
1Projected
Faster daily and intraday market interpretation
Scheduled ingestion and analysis bring daily and intraday market inputs into one review workflow.
2Projected
Consolidated technical, macro and scenario signals
Technical indicators, macro drivers and scenario outputs are consolidated in the desktop product.
3Projected
Clearer decision support for professional commodity users
Reports and scenario views are designed to support professional interpretation rather than replace trading judgement.
4Projected
Forecast versus actual review for ongoing evaluation
Forecast versus actual views allow teams to review model behaviour over time.
5Projected
A scalable commercial platform foundation
Licensing, tenant, device and session controls provide a foundation for controlled commercial delivery.
09 / Technology
Every component, with its role in the delivery.
Technology choices from the supplied project brief, mapped to the workflow each component supports.
Azure OpenAI
Managed LLM inference and reasoning
LangGraph
Stateful multi step AI workflow orchestration
LangChain
RAG and LLM application orchestration
React
Reviewer or product user interface
Tauri
Desktop application shell
Python
AI, data processing and backend logic
FastAPI
Backend APIs and workflow orchestration
NestJS
Platform, authentication and licensing APIs
PostgreSQL
Relational operational and analytics data
Docker
Containerised deployment
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