Picaro.ai Officially Releases AI Agent Function and MCP – 20 Years of Amazon Operations Know-How as an AI Consultant
Picaro.ai has officially released its "AI Agent Function" and "MCP," offering 20 years of Amazon operations know-how as an AI consultant. This aims to resolve challenges faced by companies in Amazon account management and ad analysis/operations.
📋 Article Processing Timeline
- 📰 Published: April 28, 2026 at 01:56
- 🔍 Collected: April 27, 2026 at 17:31
- 🤖 AI Analyzed: April 27, 2026 at 21:08 (3h 36m after Collected)
Picaro.ai Inc. (Headquarters: Minatomirai, Yokohama City, Kanagawa Prefecture; Representative Director: Suei Shimohira) announced today, April 27, 2026, the official release of its AI Agent function (for Standard Plan and above) and the MCP (Model Context Protocol) "Picaro.AI MCP" for external partners, within its Amazon account operation and ad analysis/management platform "Picaro.AI."
Even with an increased number of consultants, the quality of proposals can be inconsistent. Training takes time, and expertise tends to become siloed—Picaro.AI addresses these common challenges faced by companies offering Amazon support with two approaches: an AI agent function that completes Amazon operation analysis to execution on the platform, and MCP which allows direct connection of Picaro.AI's logic to commonly used AI tools. This enables the entire team to operate with the same data insights as a senior consultant, without hiring engineers or building systems.
Two approaches to enhance consulting quality and productivity:
### Approach ① AI Agent Function – End-to-end analysis and execution on Picaro.AI
Through chat-based interactions on the Picaro.AI platform, users can consistently perform overall account and ad data analysis, strategy planning, and execution.
What significantly differentiates it from other AI tools is that Picaro.ai is based on proprietary ad operation logic and contribution analysis algorithms developed in-house. While general AI analysis infers based on generic data, Picaro.AI operates on a data structure and analysis logic specialized for Amazon accounts and ad operations. Consultants simply input "How should this campaign be improved?" and the AI immediately performs SQP/N-gram analysis and contribution analysis, presenting data-backed strategies and bid prices. The decision-making process is logged, which can be utilized for team reviews and training.
Relevant scenarios:
- When there are many projects, and the on-site operation members are overwhelmed.
- When new members join, and training takes time or expertise is siloed.
- When teams already using Picaro.AI want to reduce the effort of analysis, proposals, and report creation.
- When aiming to accelerate the speed of weekly PDCA cycles.
### Approach ② Picaro.AI MCP – Incorporating Picaro's Amazon analysis know-how into your company's AI environment
This mechanism connects Picaro.AI's analysis logic to AI tools already used by the team, such as Claude or ChatGPT. Without opening the Picaro.AI screen, users can instantly retrieve Picaro.ai's proprietary logic and advanced analysis data specific to Amazon ads, such as SQP and N-gram analysis, simply by providing instructions in natural language through their everyday AI. No system development is required.
Example 1 | Cross-sectional analysis in EC manufacturers
How to leverage the extracted data depends on the business context of the utilizing company. For example, in an EC manufacturer, based on Amazon search query trends and purchasing data output from Picaro.AI, it can be applied to comparative analysis with their own EC sites and other channels, understanding loyal customer behavior, and designing multi-channel customer journeys. For companies facing the challenge of "having data but unable to use it correctly because it's not organized for AI," Picaro.AI's MCP offers a direct solution.
Relevant scenarios:
- When wanting to comprehensively analyze Amazon data along with data from Rakuten or proprietary sites using a single AI.
- When the use of Claude, which Picaro.ai uses, is not permitted internally.
- When wanting to integrate Amazon analysis logic into your company's workflow and AI environment.
- When wanting to utilize Amazon data as a starting point for multi-channel strategy planning.
Example 2 | OEM utilization by consulting firms
Picaro.ai MCP is also envisioned for use as an analysis foundation integrated into proprietary AI services and tools of consulting firms. By placing Picaro.ai's analysis logic behind the scenes, firms can offer it to clients as their own branded AI analysis service—a so-called white-label utilization. This allows them to deploy highly accurate analysis specialized for Amazon ads as their own service without incurring the cost of building data infrastructure and analysis algorithms from scratch.
Relevant scenarios:
- When wanting to integrate Amazon analysis logic into your company's workflow and AI environment.
- When wanting to provide it to clients as your own branded AI service.
Even with an increased number of consultants, the quality of proposals can be inconsistent. Training takes time, and expertise tends to become siloed—Picaro.AI addresses these common challenges faced by companies offering Amazon support with two approaches: an AI agent function that completes Amazon operation analysis to execution on the platform, and MCP which allows direct connection of Picaro.AI's logic to commonly used AI tools. This enables the entire team to operate with the same data insights as a senior consultant, without hiring engineers or building systems.
Two approaches to enhance consulting quality and productivity:
### Approach ① AI Agent Function – End-to-end analysis and execution on Picaro.AI
Through chat-based interactions on the Picaro.AI platform, users can consistently perform overall account and ad data analysis, strategy planning, and execution.
What significantly differentiates it from other AI tools is that Picaro.ai is based on proprietary ad operation logic and contribution analysis algorithms developed in-house. While general AI analysis infers based on generic data, Picaro.AI operates on a data structure and analysis logic specialized for Amazon accounts and ad operations. Consultants simply input "How should this campaign be improved?" and the AI immediately performs SQP/N-gram analysis and contribution analysis, presenting data-backed strategies and bid prices. The decision-making process is logged, which can be utilized for team reviews and training.
Relevant scenarios:
- When there are many projects, and the on-site operation members are overwhelmed.
- When new members join, and training takes time or expertise is siloed.
- When teams already using Picaro.AI want to reduce the effort of analysis, proposals, and report creation.
- When aiming to accelerate the speed of weekly PDCA cycles.
### Approach ② Picaro.AI MCP – Incorporating Picaro's Amazon analysis know-how into your company's AI environment
This mechanism connects Picaro.AI's analysis logic to AI tools already used by the team, such as Claude or ChatGPT. Without opening the Picaro.AI screen, users can instantly retrieve Picaro.ai's proprietary logic and advanced analysis data specific to Amazon ads, such as SQP and N-gram analysis, simply by providing instructions in natural language through their everyday AI. No system development is required.
Example 1 | Cross-sectional analysis in EC manufacturers
How to leverage the extracted data depends on the business context of the utilizing company. For example, in an EC manufacturer, based on Amazon search query trends and purchasing data output from Picaro.AI, it can be applied to comparative analysis with their own EC sites and other channels, understanding loyal customer behavior, and designing multi-channel customer journeys. For companies facing the challenge of "having data but unable to use it correctly because it's not organized for AI," Picaro.AI's MCP offers a direct solution.
Relevant scenarios:
- When wanting to comprehensively analyze Amazon data along with data from Rakuten or proprietary sites using a single AI.
- When the use of Claude, which Picaro.ai uses, is not permitted internally.
- When wanting to integrate Amazon analysis logic into your company's workflow and AI environment.
- When wanting to utilize Amazon data as a starting point for multi-channel strategy planning.
Example 2 | OEM utilization by consulting firms
Picaro.ai MCP is also envisioned for use as an analysis foundation integrated into proprietary AI services and tools of consulting firms. By placing Picaro.ai's analysis logic behind the scenes, firms can offer it to clients as their own branded AI analysis service—a so-called white-label utilization. This allows them to deploy highly accurate analysis specialized for Amazon ads as their own service without incurring the cost of building data infrastructure and analysis algorithms from scratch.
Relevant scenarios:
- When wanting to integrate Amazon analysis logic into your company's workflow and AI environment.
- When wanting to provide it to clients as your own branded AI service.