Talex
One AI Engineer. One Non-Tech Company. One Platform That Worked.

Agriculture Β· APAC Β· Staff Augmentation

One AI Engineer. One Non-Tech Company. One Platform That Worked.

A global agricultural company needed faster commercial decisions in the field. One senior AI engineer, embedded in a non-tech environment, built the answer in three months.

Specialists deployed

1

Duration

3 mo

Engagement model

Staff Augmentation

Response Time

45 seconds

"We didn't expect someone technical to adapt this quickly to a non-technical environment. "

β€” Agricultural Company, Tech Lead

Every sales question had an answer. Getting to it might take days though

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.

Relying on BI teams for every query creates bottlenecks.Complex data structures discourage self-service.Delayed insights lead to reactive rather than proactive decision-making.

One engineer. Inside the team. Answering business questions in 45 seconds.

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.

1

AI Developer

Senior

Stack & Compliance

Azure AD SSOMSALTeams Bot Framework JWT

A four-month pilot. 642 questions answered. The focus has shifted from validation to scaling.

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.

Improved (Sales Efficiency)

Sales teams can now make informed decisions quickly.

Reduced (Operational Costs)

Cost-effective solution with low query costs.

Optimized (Development Time)

Rapid deployment and integration into existing systems.

Enhanced (System Scalability)

Designed to integrate additional data sources seamlessly.

Timeline

1

Integration Β· 1 week

AI developer joined the team and began work immediately.

2

Development Β· 2 months

Built and optimized the AI conversational agent.

3

Pilot Testing Β· 1 month

Deployed agent for real-world testing and feedback.

Business Outcomes

  • β†’$0.49 per question (Unit Economics) : 642 questions answered across the pilot. Total infrastructure and model cost under $320 for four months. Weekly sales review workflow at $0.37 per user per week.
  • β†’10 to 34 active users (Adoption Growth) : User base grew organically across the pilot period β€” without a formal rollout campaign - driven by the utility of having answers inside the tool the team already used.

Engineering Excellence

  • β†’45-second responses : Query Speed : Response time improved from over three minutes at launch to around 45 seconds after prompt and model optimisation, fast enough to use mid-conversation.
  • β†’100% UAT pass rate : Accuracy : Every real business use case tested during the pilot - distributor volumes, farmer counts, sales performance, product GMV - passed quality assurance.
  • β†’Four response modes : Adaptive Output : One assistant handles simple lookups, business analysis with charts, and raw data exports with CSV download - without users changing tools or workflows.

Why Talex

Expertise in Rapid Deployment 7 days

Talex provided a senior AI developer who quickly integrated into the team and delivered results.

Focus on Cost Efficiency

Custom-built solutions ensured control over query costs and optimized user experience.

SPEED: Time-to-Team RiskDOMAIN FIT: Generic Talent Risk

Services

BackendDevOps