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$1.97M Saved with ChatGPT Enterprise & AWS
LogicMonitor is a SaaS-based hybrid observability platform that helps businesses monitor and manage their IT infrastructure, including networks, servers, applications, and cloud services. It provides comprehensive monitoring capabilities and actionable insights to improve visibility, performance, and reliability of IT environments. LogicMonitor is known for its ability to handle hybrid and multi-cloud environments and its use of AI to automate monitoring and identify potential issues.
LogicMonitor is undertaking a comprehensive, enterprise-grade AI transformation in partnership with Altimetrik, designed to modernize how the company sells, learns, and makes decisions.
Key Business Challenges
C-level leadership prioritized positioning LogicMonitor as an AI-first company with strong thought leadership. The ambition was to showcase an “agent for every employee” vision, but this required both external credibility and internal adoption. The challenge was balancing bold positioning with tangible proof points that could demonstrate progress and differentiation in a crowded observability market.
Customer-facing teams often faced delays and inefficiencies when preparing for sales meetings. Critical context and proof points were scattered across Salesforce, Slack, and other sources, resulting in fragmented, incomplete, or outdated insights. This slowed preparation, reduced confidence in customer conversations, and limited the ability of sellers to focus on high-value engagement with prospects and clients.
Essential knowledge assets, such as customer success stories, proof points, and prior interactions, were spread across multiple systems and not easily accessible at the point of need. Without a unified access point, sellers relied on manual searches and disconnected workflows. This not only slowed decision-making but also led to inconsistent messaging and reduced productivity in high-stakes customer interactions.
Objectives
- Streamline and improve sales-meeting prep so reps can show up with a concise, accurate brief.
- Aggregate the right context automatically (Salesforce,Web Search, Slack) and make it available through a conversational assistant.
- Deliver smart recommendations, customer insights, and success stories at the moment of need (surfaced in Slack).
- Ground responses with RAG so answers are contextual, accurate, and actionable not just generic LLM output.
- Reduce manual sales/admin work and increase seller productivity and effectiveness in customer engagements.
- Provide a secure, governed front door via ChatGPT Enterprise (SSO/admin controls; no training on business data by default).
The Solution Roadmap
We made ChatGPT Enterprise the single, secure entry point for GTM questions, then used an AWS-hosted OpenAI Agents SDK to retrieve governed context and orchestrate tools. The assistant LISA sits in ChatGPT Enterprise, authenticates users via Salesforce, and blends Salesforce data, web search, and curated content (Slack customer stories).
- ChatGPT Enterprise provided SSO/RBAC controls and workspace analytics; AWS provided serverless execution and workflow reliability.
- Salesforce remained the system of record; structured Slack customer stories were pre-processed and indexed for grounded responses.
- The design mirrors the proven “smart front door → retrieval on AWS” pattern used for enterprise knowledge.
AWS
- AWS Lambda hosts the AgentsSDK tool & agent calls (Salesforce, Slack index, files, research) and powers ingestion & retrieval flows.
- Amazon API Gateway exposes secure, versioned endpoints that the ChatGPT Enterprise agent calls; schemas are validated here.
- Amazon DynamoDB stores agent/session state, retrieval pointers, ingestion checkpoints, and conversation memory
- Amazon Comprehend performs entity/sentiment detection and PII redaction during ingestion so only sanitized text is embedded and retrieved.
Non-AWS
- Pinecone (Vector Database) stores embeddings and serves similarity search at query time (namespaced by source/tenant for governance).
- Ask in ChatGPT Enterprise. LISA receives a natural-language prompt (e.g., “Prep me for Acme QBR”).
- Plan & authenticate. The agent validates identity (Salesforce) and picks one of six meeting templates with custom tool calls (Salesforce, Slack index, files, research) based on role.
- Retrieve & compose. A multi-agent flow using OpenAI Agents SDK fetches opportunities, prior interactions, and customer stories then drafts the brief with citations and next-best talking points.
- Deliver. Users get a concise meeting prep pack and can download the prep from GPT canvas
Keeping Knowledge Fresh
A scheduled ingestion routine processes Slack posts and relevant spaces into a vector index, with delete/error hygiene, so LISA always searches current content. This mirrors the AWS-native ingestion approach (fan-out, storage, extract-chunk-embed-upsert) used for enterprise RAG.
Faster prep, better conversations, and measurable capacity gains for sellers using ChatGPT Enterprise with AWS-backed retrieval.
- Weekly time saved per rep: 7.0 hours (QBR prep 3.0→0.5; content 4.0→1.0; other 2.0→0.5), equal to ~17.5% of the work week.
- Annual savings per QBR role: $22K
- At ~90 QBR roles: ~$1.97M annual savings ( ~16 FTEs of capacity).
By unifying GTM knowledge behind ChatGPT Enterprise and a resilient AWS backbone, LISA gives LogicMonitor’s sellers an always-on prep partner that matches the company’s hybrid-observability DNA in LM Envision and extends that operational discipline to revenue teams. The stack is built for trust and scale: ChatGPT Enterprise provides enterprise-grade privacy and admin controls (no training on your business data, SSO/SCIM, RBAC), while AWS Lambda and OpenAI Agents SDK orchestrate durable, retriable workflows that can grow with usage and content breadth. As coverage expands from Salesforce and Slack stories to broader knowledge sources the assistant’s guidance gets sharper, driving sustained adoption and compounding ROI for a business already centered on AI-powered reliability.
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