Case Study
From 30-90 Days to Hours: Agentic AI for AWS Co-Sell Automation
How Skematic and AllCode built an agent-to-agent execution layer with the Model Context Protocol, putting real-time partner intelligence and automated co-sell execution directly inside AWS sellers' native workflows.
Segment: SaaS & ISV | Use Case: Agentic AI, AWS Co-Sell Automation, Partner Intelligence | Industry: AWS Partner Ecosystem, Go-to-Market Software
AWS Co-Sell Challenge
Manual Co-Sell Was Costing ISVs Weeks
Skematic is an AWS Partner Growth Platform that helps ISVs assess partnership readiness, execute go-to-market playbooks, and manage co-sell pipelines. Early-stage AWS ISVs and AWS field teams faced real friction inside programs like ISV Accelerate: AWS Sellers and Partner Solutions Managers had to leave Amazon Quick, their daily workspace, and sign into separate portals just to check an ISV's readiness, and unindexed partners stayed invisible in internal searches while competitors surfaced instead.
Preparing a single AWS Customer Engagements (ACE) opportunity took 15 to 20 minutes of specialized effort to fill roughly 40 required fields, map AWS product attribution, and structure the customer's business problem by hand. On top of that, qualifying for ISV Accelerate meant clearing 80-plus pages of manual checklists alongside Well-Architected Framework Review and Foundational Technical Review validation.
None of this friction was optional. It sat directly between ISVs and the co-sell revenue AWS Accelerate is supposed to unlock, and it scaled linearly with headcount that early-stage companies didn't have.
The Solution
An Agent-to-Agent Execution Layer Inside Sellers' Native Workflow
AllCode architected a multi-layered generative AI framework inside Skematic on AWS Bedrock (Claude models), Model Context Protocol servers, LangChain/LangGraph orchestrators, Pinecone vector storage, and BullMQ queues, embedding MCP action servers and dual AI agents directly into Amazon Quick and Claude Cowork so sellers never have to leave the tools they already use.
Amazon Quick MCP connector
A remote MCP server over Streamable HTTP/SSE with OAuth 2.0 identity checks exposes analyze_accounts, get_partner_readiness, search_partners, get_portfolio_summary, run_ftr_assessment, and generate_remediation_iac, discovered and invoked by Amazon Quick as an MCP client.
ACE co-sell registration
A 3-layer async BullMQ pipeline hydrates CRM data from HubSpot and Salesforce, uses an LLM to gap-fill AWS-formatted business problems and use cases, and mirrors mapped products to Marketplace ARNs, all behind an 895-line pre-submit validator and human-in-the-loop approval.
Automated WAFR/FTR module
An automated telemetry sweep across 50-plus AWS services runs the 56-question Well-Architected Framework Review with deterministic risk triage, auto-drafting 27 answers, handling 21 one-click confirmations, and fast-tracking 44 Foundational Technical Review controls.
Alliance Lead Advisor
A Claude-powered Bedrock agent trained on 569 Avoma call transcripts (~75MB), semantically chunked, embedded with Titan Embeddings V2, and indexed in Amazon OpenSearch Serverless, answers procedural questions and drafts ACE descriptions like a senior alliance coach.
Guided implementation PRM
A pattern-recognition tree generates pre-tagged CloudFormation, CDK, or Terraform remediation code with aws-apn-id tags built in, so infrastructure spend attributes correctly to the partnership without any custom infrastructure writes.
Technology
MCP, Bedrock, and LangGraph Powering Agent-to-Agent Execution
The Alliance Lead Reporting Agent tracks ISV milestone progress across the whole portfolio, surfacing stall points and flagging overdue follow-ups for leadership, while the LangGraph orchestration layer keeps every agent call auditable and every system write gated behind explicit human approval, including snapshot role registrations.
Nothing here required ISVs or AWS sellers to adopt a new tool. The MCP action server, the ACE enrichment pipeline, and the Bedrock knowledge base all surface through Amazon Quick, Claude Cowork, and Skematic's own UI, so the AI layer meets sellers exactly where their day already happens.
Results
Before & After: From Manual Checklists to Agentic Execution
| Operational Metric | Before | After |
|---|---|---|
| Co-sell onboarding & execution timeline | 30 to 90 days per opportunity | A matter of hours |
| ACE opportunity data entry | 15-20 minutes of manual entry across ~40 fields | Automated CRM hydration and LLM gap-fill |
| Search visibility to AWS sellers | Unindexed, invisible next to competing 3PI partners | Surfaced natively inside Amazon Quick, at parity with indexed vendors |
| Technical compliance review | 80+ pages of manual checklists, full WAFR/FTR by hand | Automated 56-question sweep, 27 auto-drafted, 21 one-click, 44 fast-tracked |
| Alliance coaching & institutional knowledge | Tribal knowledge, ad hoc coaching sessions | RAG-backed Alliance Lead Advisor over 569 indexed call transcripts |
Business Impact
Measurable Impact: Agentic Co-Sell at a Glance
Over 90,000 eligible AWS ISVs can now surface partner-readiness metrics without leaving Amazon Quick or Claude Cowork. Institutional coaching knowledge that used to live in individual Alliance Leads' heads is now searchable across 569 indexed call transcripts, and technical compliance reviews that used to consume days of manual checklist work now run through an automated 56-question sweep with 44 controls fast-tracked, all while every system write still requires explicit human approval.