Case Study
How Skematic Uncovered a $462K Annual AI Savings Portfolio in a 2-Week AWS AI Assessment
Three prioritized use cases, an 8.3-month portfolio payback, and a path to 5x partner certification throughput, all without adding headcount.
Segment: Startup | Use Case: Generative AI, Autonomous Partner Advisory at Scale | Industry: AWS ISVs and Marketplace Sellers
The challenge
A Modern Product on a Human-heavy Delivery Model
Skematic is an AI-native partner advisory platform and service that unifies expert guidance with automation to eliminate the gap between AWS partnership ambition and execution. Unlike point tools or one-off consulting, Skematic combines AI-generated, AWS-ready collateral, live milestone tracking, and guided playbooks into a single operating model, purpose-built for ISVs, SaaS companies, and cloud-native startups that want to compete and win through the AWS ecosystem.
The delivery model underneath it was human-heavy and expensive. Twenty customers each required roughly 20 hours of ongoing partnership guidance every month, at approximately $5,000 per customer per month in fully loaded advisory cost. Expert work like AWS FTR preparation consumed 40 hours of solutions-architect time per certification, at around $8,000 each, while Marketplace listings carried a 60% first-submission error rate that drove constant rework. Onboarding was manual and hard-coded, and frustrating for admins.
AI was already in use, but fragmented and ungoverned, split across a standalone research agent and a separate enterprise model subscription with no shared policy, memory, or observability. With a customer growth milestone tied to its next funding stage, the math was unforgiving: without automation, growth meant linear headcount growth.
The AllCode solution
A Quantified AI Investment Roadmap in Two Weeks
AllCode, an AWS Generative AI Competency Partner, ran a two-week AI Assessment (May 31–June 14, 2026) to determine the “art of the possible” and evaluate how agentic AI could extend Skematic’s offering. The result was a quantified roadmap for an AWS-native, Bedrock-powered agent architecture, backed by a $318,000 development pipeline. The design extends Skematic’s existing expertise rather than replacing it: agents absorb the repeatable, time-intensive work so the team’s advisory time stays focused on what actually needs a human.
Skematic has implemented an AI-native agentic architecture that transforms partner advisory into a scalable, high-velocity operating model. By leveraging Amazon Bedrock (featuring Claude Sonnet and Haiku models) and the AgentCore framework, Skematic automates complex compliance and certification workflows, enabling a 5x increase in throughput without increasing headcount. This production-grade architecture supported by a serverless foundation including Lambda, S3, DynamoDB, RDS, Step Functions, and AWS Config delivers quantifiable ROI and establishes a secure, high-performance platform for continuous AWS partner growth.
The engagement reinforced a partnership that was already deepening: Skematic continues to build and scale on Amazon Bedrock, and this assessment marks the next stage of that commitment. It’s the kind of AWS-native growth story AllCode loves to help write; a lean, AI-first partner turning two weeks of assessment into a funded path toward its next stage of scale, and a model other AWS partners can look to as they think about growing without growing headcount.
Architecture
An AWS-native, Bedrock-powered Agent Architecture
Skematic runs a production-grade agentic architecture on Amazon Bedrock AgentCore, using Runtime, Gateway, Memory, Policy, and Observability with the Strands framework, and Claude Sonnet and Haiku models underneath. Consolidating fragmented subscriptions into one governed environment turned scattered AI usage into a platform the team can secure, monitor, and extend.
Amazon Quick Suite handles analytics and automation through Quick Flows, Quick Automate, Quick Index, and QuickSight. The serverless foundation includes AWS Lambda, Amazon S3, DynamoDB, Amazon RDS, EventBridge, Step Functions, and AWS Config. The same work retired Amazon App Runner ahead of its deprecation deadline, clearing a platform risk while the architecture was already in motion.
Results
Before and After
| Metric | Before | After |
|---|---|---|
| Cost per partner certification | $8,000 | $1,600, an 80% reduction |
| FTR preparation time | 40 hours | 8 hours |
| Marketplace listing preparation | About 6 hours | Under 15 minutes |
| Tagging audits | 96 hours per year | 3 hours per year |
| Certification throughput | 4 per quarter | 5x with the same headcount |
| Marketplace listing error rate | 60% on first submission | Under 10% |
| Tagging compliance | No defined target | 95% or higher |
| Productized compliance offering | None | $2,500 to $5,000 per certification cycle |
Business impact
What the Roadmap Unlocks
The assessment quantified $462,405 in projected annual savings across three prioritized use cases, with an 8.3-month portfolio payback and a $318,000 development pipeline.
Thousand dollars in projected annual savings
%
Reduction in cost per partner certification
Times certification throughput, with the same headcount
Month payback across the use case portfolio
This assessment gave us more than a list of AI ideas, it gave us a sequenced, funded roadmap for scaling the exact expertise our partners already trust us for. Two weeks got us a clear line of sight to the certifications, the listings, and the revenue model that gets us to our next stage of growth.