Designing an AI Assistant for Smarter Sales Workflows

Design a modular AI platform to empower teams, starting with CGX’s sales team, by automating repetitive tasks, surfacing timely insights, and supporting strategic decision-making.

Client

Clear Grain Exchange

Services

Product strategy, product design, end-to-end flows, research, prototyping

Industries

Agritech, Supply, Wholesale, Retain SaaS

Overview

Design a modular AI platform to empower teams, starting with CGX’s sales team, by automating repetitive tasks, surfacing timely insights, and supporting strategic decision-making.

Problem

Sales reps at CGX are responsible for managing hundreds of sellers relationships.

Key challenges included:

  • Manually tracking who to contact and when

  • Following up across inconsistent channels (calls, SMS, email, Slack)

  • Drafting repetitive emails and messages

  • Lack of shared visibility across the team

  • Missed opportunities due to timing, pricing, or offer gaps

Goals

Build an Agentic AI assistant that’s proactive, context-aware, and deeply integrated, helping teams uncover opportunities, act faster, and stay ahead of market shifts.


It empowers teams to:

  • Instantly spot what matters in sales and support conversations

  • Get smart follow-up suggestions and timely nudges

  • Auto-draft emails, SMS, and call scripts tailored to each client

  • Detect trends in offers, grower behavior, and pricing

  • Eliminate repetitive tasks and sync action items across tools

  • Align teams with shared, real-time insights

Solution

We set out to build an Agentic AI, a central, intelligent assistant designed to transform how sales teams work.


Instead of just reacting, this AI acts: listening, learning, and proactively surfacing what matters most.


The solution enables teams to:

  • Real-time nudges and summaries to stay on top of deals

  • Smart follow-ups, SMS, and call scripts from client sentiment

  • Unified feed from Slack, CRM, and calls

  • Early signals on risks, shifts, and intent

🔗 See interactive prototype here

🔗 See interactive prototype here

🔗 See interactive prototype here

🔗 See interactive prototype here

🔗 See interactive prototype here

🔗 See interactive prototype here

🔗 See full case study here

🔗 See full case study here

🔗 See full case study here

🔗 See full case study here

🔗 See full case study here

🔗 See full case study here

Results

Time saved

  • 2.5 hrs/week saved per rep

  • Measured via time comparisons between manual vs. AI-generated follow-up messages.

Follow-up completion

  • +22% more follow-ups sent within 48h

  • Tracked via Slack/CRM logs pre- and post-assistant usage.

Collected insights

  • 3× more actionable buyer signals identified

  • AI flagged 9 insights/week vs. 3 manually; validated in user feedback.

Faster lead identification

  • 50% reduction in time to decide who to contact next

  • Measured during timed usability tests using the AI dashboard vs. Slack review.


Conclusion

The AI assistant helped the sales team stay on top of follow-ups, spot key signals from conversations, and save time on writing and planning. It fit into their existing tools and actually made their work easier. The early results show it's solving real problems, and there’s clear potential to scale it further.