Customer Challenges

Labra’s internal teams handle thousands of data signals across AWS APN Customer Engagements Program (ACE), Marketplace transactions, pipeline activity, co-sell motions, CRM updates, and partner intelligence. Although Labra’s platform automated much of the cloud Go-To-Market (GTM) workflow, internal sales and alliances teams still relied on:

• • Manual searches across multiple systems (ACE, Salesforce, Marketplace dashboards).
  • Scattered intelligence signals that were not summarized or prioritized.
  • Slow decision cycles because insights lived across many tools.
  • High operational effort to find answers to simple questions like “top deals,” “high-intent buyers” or “ACE updates.”

1. Lack of Real-Time Intelligence

Teams needed instant insights—summaries, rankings, revenue snapshots—but retrieving them required switching between 4–5 systems.

2. Inefficient Workflows Across GTM Teams

Every update was manual: checking ACE entries, reviewing Marketplace subscriptions, interpreting co-sell status, and preparing internal reports.

3. Delayed GTM Decision-Making

Without automated intelligence, prioritization took longer, slowing revenue acceleration and reducing seller productivity.

Without intervention, Labra risked slower growth, reduced visibility into high-value opportunities, and a GTM engine dependent on human effort instead of automated intelligence.

To solve this, Labra partnered with Ibexlabs to build a Generative AI-powered Slack Agent—powered entirely by AWS.

The engagement focused on modernizing Labra’s cloud GTM intelligence layer using Generative AI.

Ibexlabs aligned the project with AWS Generative AI Consulting Services competency by delivering:

• • A Slack-native Generative AI assistant for cloud sales and co-sell workflows.
  • Automated intelligence powered by Amazon Bedrock, RAG pipelines, and AWS-native vector search.
  • Real-time retrieval of Marketplace, ACE, CRM, and revenue signals.
  • A secure, scalable, production-ready GenAI solution deployed entirely on Labra’s AWS environment.

Labra aimed to:

• • Reduce manual research time for sellers.
  • Centralize intelligence in Slack where teams already work.
  • Surface high-intent opportunities instantly.
  • Summarize ACE and Marketplace data automatically.
  • Streamline internal GTM workflows using automation.

This case study highlights Ibexlabs’ expertise in building secure, enterprise-ready Generative AI applications on AWS.

Partner Solution

1. Slack-Integrated GenAI Assistant

Labra AI agent lives inside Slack as a conversational assistant.
A user types “@labra show me my top AWS opportunities this month” and receives an instant, AI-derived summary.

AWS API Gateway exposes secure endpoints for Bi-Directional Slack events → AWS → Slack responses.

2. Serverless Inference & Reasoning Layer

All intelligence logic runs on AWS Lambda, creating a fully serverless pipeline:

• • Instant scaling
  • Low operational overhead
  • Zero server maintenance

Lambda retrieves signals, builds prompts, fetches context, and interacts with Amazon Bedrock.

3. Amazon Bedrock as the AI Backbone

Labra AI agent uses Amazon Bedrock for:

• • Summarization
  • Ranking
  • Contextual reasoning
  • Co-sell strategy suggestions
  • Marketplace revenue interpretation
  • Query understanding

Bedrock guardrails ensure safe, predictable, and compliant outputs.

4. RAG Pipeline with Vector Search (Amazon OpenSearch)

Ibexlabs implemented a Retrieval-Augmented Generation (RAG) workflow:

• • Marketplace metadata
  • ACE exports
  • Pipeline data
  • CRM-synced documents
  • Internal wikis / strategy docs

Stored in OpenSearch for semantic and vector-based retrieval. This ensures every AI answer is grounded in Labra’s actual data.

5. DynamoDB for Conversation Memory

Conversation context, session data, preferences, and user-level interaction history are stored in DynamoDB, enabling:

• • Multi-turn conversations
  • Context carry-over
  • Personalized responses

6. S3 for Knowledge & Document Storage

All reference documents, prompt templates, and knowledge packs are stored in Amazon S3, giving:

• • Durability
  • Low cost
  • Centralized knowledge governance

7. Workflow Orchestration via AWS Step Functions

For more complex tasks like multi-source aggregation or multi-step analysis:

• • Step Functions coordinate retrieval, summarization, and formatting
  • Provides reliability, retries, and observability
    

Solution Optimality

The chosen architecture delivered:

• Zero infrastructure management
• Instant scalability via serverless AWS
• Low-latency Slack experiences
• High accuracy using RAG-enhanced Bedrock inference
• Seamless integration with Labra’s existing tools

Alternative options were evaluated:

1. Self-hosted LLMs → Rejected for high operational overhead and security complexity.
2. Non-AWS open-source models → Rejected due to compliance risks and lack of enterprise guardrails.
3. Dashboard-based intelligence → Rejected because Labra needed a conversational, real-time assistant.
   
   The AWS-native GenAI approach proved optimal, giving Labra a production-ready GTM intelligence engine with minimal maintenance.

Reduced Manual Effort

Manual searches across ACE, Marketplace, and CRM were replaced by instant Slack queries. Teams now focus on selling, not searching.

Faster Decision Making

Labra AI agent delivers ranked lists, summaries, and strategic insights within seconds, enabling:

• • Faster pipeline reviews
  • Better prioritization
  • Stronger internal alignment

Scalable, Always-Available Intelligence

Because Labra AI agent is built on AWS serverless services, it scales automatically with Labra’s usage patterns—no maintenance required.

Stronger GTM Performance

Labra AI agent improved seller productivity and the speed at which teams identify high-intent opportunities and understand co-sell status.

Immediate Access to Critical Intelligence

Users now get instant answers to questions like:

• • “What are my top deals this week?”
  • “Which accounts have Marketplace potential?”
  • “Summarize my ACE opportunities.”
  • “Who are the AWS stakeholders for this deal?”

This accelerates both operational execution and revenue strategy.