Unlocking APAC Agriculture Potential with AI-Driven Insights

A global agricultural company faced a significant hurdle: their sales team, distributed across Southeast Asia, struggled with data access. Field agronomists and sales managers needed real-time insights to make informed decisions, yet every query required a request to the BI team, often resulting in days of delay. The complexity of their data warehouse, with its intricate fact tables and non-intuitive column names, rendered self-service impractical. The sales cycle's dynamic nature meant that by the time answers were received, the information was often obsolete, leading to missed opportunities and stalled initiatives.

The obvious approach, relying on manual BI requests, was painfully slow and hindered decision-making velocity. Fast access to data wasn't just a technical requirement but a critical business need that directly influenced the company's ability to remain competitive.

Talex deployed one senior AI developer directly into the client's team - taking over from a previous engineer and picking up the project mid-flight. The client was a global agricultural company with a small, specialised digital team and no dedicated AI function. There was no internal AI lead to align with, no established technical direction to follow. The engineer had to assess what had been built, identify what needed to change, and propose a path forward independently.

That kind of environment requires more than technical skill. It requires someone who can read a non-technical stakeholder, translate business questions into engineering decisions, and earn trust with a team that does not speak the same language. The Talex engineer did all of that - working cross-functionally with the customer experience team, the digital growth team, and the data analyst to understand what the sales team actually needed before writing a line of new code.

The solution built was a conversational AI agent native to Microsoft Teams - the tool the sales team was already using every day. Sales managers and field agronomists type questions in plain business language. The agent queries the live sales warehouse in Amazon Redshift, searches a knowledge base of product specs and SOPs, and returns answers as text, tables, or charts directly inside Teams. One assistant, four response modes, no change to existing workflows.

The pilot ran across the Indonesia sales team with a small initial group, designed to validate quality, speed, and unit economics before any decision to invest in wider rollout. It cleared every bar.

UAT quality reached 100% pass rate across real business use cases - top distributors by volume, field activity counts, sales performance, product GMV. Response time improved from over three minutes at launch to around 45 seconds after prompt and model optimisation. The platform answered 642 questions across the pilot at an average cost of $0.49 per query. Total infrastructure and model spend for the full pilot stayed under $320. The weekly sales review workflow ran at $12.50 per session - $0.37 per user per week.

User adoption grew from 10 active users at launch to 34 by the end of the pilot. The pilot is over. The focus has shifted to scaling.