Shoppers describe the room they are furnishing and get the right fixture back.
Lightopia Ask Cleo
Agent Ready Award Agentic Achievement Award
CarParts.com Multi-Agent Ecosystem
Prototyping runs 10 times faster, seven dashboards became one, and developers reclaimed 2 hours a day.
AskSpark coordinates nine tools across fitment, inventory, and checkout, and reaches shoppers inside external assistants.
What the agent does
Coordinates nine specialized tools to verify fitment, check stock, and complete a purchase, on the storefront or inside another assistant.
- 2026 Agentic Achievement Award, MACH X: Toronto
- Built by
- CarParts.com, in-house
- Contributed by
- CarParts.com (Built in-house)
- Industry
- Automotive, Retail
- Audience
- B2C
01
The problem
Why this exists
Automotive parts retail has a constraint most catalogs do not: fitment. A part is only right for a specific year, make, and model, and getting it wrong produces a return rather than a complaint. Any agent that helps a shopper has to hold vehicle context across every step, or it will confidently recommend a part that does not fit.
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The second problem is reach. Shoppers increasingly start in an assistant rather than on a storefront, and building a separate integration for each assistant does not scale.
Internally, the engineering organization carried the usual load: repetitive code, seven separate operational dashboards, and integration projects such as connecting a vendor email workflow to a support system, each of which had to be justified against everything else in the queue.
02
Outcomes
What changes when this is in place
- Combined business impact exceeds $500,000 in cost savings within the first 6 to 8 months, from replacing licensed tools, automating content creation, and compounding engineering productivity.
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Shopper fitment
AskSpark on the storefront, holding vehicle context across every tool call so a recommendation fits the car.
- Real-time fitment verification validates part-to-vehicle compatibility before a part reaches the cart, which reduces returns.
- Cart management checks inventory in real time and substitutes an alternative when a part is out of stock.
External assistant reach
The same agent exposed through an MCP server, so a shopper who starts in an assistant rather than on the storefront still reaches the catalog.
- One MCP server replaces a separate integration per assistant, which is what made reach beyond the storefront affordable at all.
- AskSpark answers inside external assistants including OpenAI and Claude.
Internal engineering productivity
More than 20 agents running semi-autonomously against the engineering, operations, and content backlog.
- Engineers report 10x faster feature prototyping, with cycles down from weeks to days, 2 hours reclaimed per developer per day, and 40% less context switching.
- Seven dashboards were consolidated into one AI-driven intelligence layer.
- Vendor email processing was fully automated, replacing what would have been a multi-week API integration project with an agent deployed in days.
- More than 100,000 SKUs were enriched with enhanced product content.
03
How it works
Architecture
AskSpark is powered by an agent-to-agent orchestration server coordinating nine specialized tools: discovery by keyword, brand, or vehicle context; fitment verification against a specific year, make, and model; comparison surfacing vehicle-specific alternatives with specifications and pricing; product details; VIN decode; vehicle images; cart management with real-time inventory checks and substitution for out-of-stock items; add to cart; and checkout covering address, shipping, tax, payment, and fraud checks.
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Those tools sit in a layered architecture. An authentication layer supports OAuth 2.1, bearer token, and API key simultaneously. An orchestration layer holds a context router, a session coordinator, and a tool chain orchestrator supporting sequential and parallel batching. A memory and state layer holds client-side widget state and a server-side context store with a 30-minute time to live, plus session metadata and structured logging with redaction.
A shared Vehicle Context Authority preserves fitment state across the whole interaction, which is the piece that stops the agent losing the vehicle between one tool call and the next.
AskSpark is reachable on CarParts.com and, through an MCP server, from external assistants including OpenAI and Claude.
Internally more than 20 agents run semi-autonomously. Ops Whisperer consolidates seven dashboards into one intelligence platform. An A+ Content Generator produces enhanced product content at scale. Rather than building an API to connect the vendor email workflow to the support system, agents read vendor emails, interpret the decision context, and execute the action directly, which demonstrated that agents can bridge systems never designed to talk to each other.
Responsible AI controls, covering PII handling, consent management, observability, and evaluation, are built into the stack rather than added afterward.
Open standards used
Model Context Protocol (MCP)Agent-to-Agent (A2A)OAuth 2.0REST APIs
Tech stack
AskSparkAgent-to-Agent orchestration serverCarParts MCP serverVehicle Context AuthorityOps WhispererA+ Content GeneratorOpenAIClaude
Resources
Next step
Request an intro
CarParts.com is behind this solution. Ask about reaching shoppers inside external assistants, and the MACH Alliance will put you in touch with the person who can answer.
Not ready to talk to anyone? MACH Insider is the Alliance mailing list. New solutions, program openings, and the research behind them, once a month.
Everyone credited on this solution
- CarParts.comBuilt in-house
Every organization named here has a page listing the rest of its work on the Hub.
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Verification
Published case study
The detail on this page is drawn from a case study published by the contributor or the MACH Alliance. Any figures on this page are the ones that case study states.
What this page does not establish
- No quantified conversion or revenue result for AskSpark itself has been published.
- The Alliance describes AskSpark as operating across five or more LLM platforms. The case study names OpenAI and Claude and does not enumerate the rest.
Where this came from
Published from the winner's case study and the 2026 MACH Impact Awards announcement.
Status: Live in productionAdded Apr 29, 2026A listing is an editorial decision, not a MACH Alliance certification.
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