Sales outreach · Production

Vxceed builds personalized loyalty program sales pitches for CPG field teams at scale using Amazon Bedrock

The problem

CPG companies invest 15–20% of their revenue in trade promotions and loyalty programs, but uptake has historically remained below 30% because programs are complex and cannot address each individual retailer's needs. Field sales teams managing millions of outlets had no scalable way to create personalized, compelling pitches for each outlet.

Workflow diagram · grounded in source
1
Field rep requests pitch
trigger
“A mobile application is used by field sales teams to access compelling program sales pitches and interact with the system through a chat interface.”
2
Orchestration Agent coordinates flow
routing
“Orchestration Agent coordinates the workflow between agents and manages the overall story creation process, interfacing with the Amazon Bedrock LLM for intelligent processing.”
3
Story Framework structures narrative
ai_action
“Story Framework Agent – Establishes the narrative structure and flow of sales pitches based on proven storytelling patterns and sales methodologies.”
4
Story Generator creates personalized content
ai_action
“Story Generator Agent – Creates personalized content by combining data from multiple sources, including outlet profiles, loyalty program details, and historical data.”
5
Story Review Agent validates content
validation
“Story Review Agent – Validates generated content for accuracy, completeness, and effectiveness before delivery to sales personnel.”
6
Guardrails screen for compliance
validation
“Lighthouse uses Amazon Bedrock Guardrails to maintain professional, focused interactions. The system uses denied topics and word filters to help prevent unrelated discussions and unprofessional language”
7
Personalized pitch delivered to rep
output
“This solution uses generative AI to create personalized sales pitches based on individual retailer data and trends, helping field representatives effectively engage retailers, address common objections, and boost program adoption.”
Reported outcome

The Lighthouse Loyalty Selling Story achieved a 95% response accuracy rate and automated 90% of loyalty program queries.
Program enrollment increased by 5–15%, enrollment processing time fell 20%, support time requirements dropped 10%, and customers saved 2 person-months per geographical region annually.

Reported metrics
CPG revenue invested in trade promotions15–20%
Historical loyalty program uptake ratebelow 30%
Response accuracy rate95%
Loyalty program queries automated90%
Show all 8 reported metrics
CPG revenue invested in trade promotions15–20%
historical loyalty program uptake ratebelow 30%
response accuracy rate95%
loyalty program queries automated90%
program enrollment increase5–15%
enrollment processing time reduction20%
support time decrease10%
administrative overhead savings per region annually2 person-months per geographical region
Reported stack
Amazon BedrockAmazon API GatewayAmazon DynamoDBAWS LambdaClaude 3.5 Sonnet
Source
https://aws.amazon.com/blogs/machine-learning/vxceed-builds-the-perfect-sales-pitch-for-sales-teams-at-scale-using-amazon-bedrock?tag=soumet-20
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The Lighthouse Loyalty Selling Story achieved a 95% response accuracy rate and automated 90% of loyalty program queries.

What tools did this team use?

Amazon Bedrock, Amazon API Gateway, Amazon DynamoDB, AWS Lambda, Claude 3.5 Sonnet.

What results were reported?

CPG revenue invested in trade promotions: 15–20%; Historical loyalty program uptake rate: below 30%; Response accuracy rate: 95%; Loyalty program queries automated: 90% (source-reported, not independently verified).

How is this sales outreach AI workflow structured?

Field rep requests pitch → Orchestration Agent coordinates flow → Story Framework structures narrative → Story Generator creates personalized content → Story Review Agent validates content → Guardrails screen for compliance → Personalized pitch delivered to rep.

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