Episode Number:

84

August 10, 2026

In this episode of The eCom Growth Show, Danan Coleman talks with Jaimin Patel, founder and CEO of Aakaar AI, about what the next generation of AI-powered Amazon selling actually looks like.

AI is already everywhere in eCommerce software, but Jaimin argues that adding a chatbot or automating PPC rules isn’t the same as building a truly agentic system. The bigger opportunity lies in AI agents that can understand context, reason through decisions, learn from outcomes, and take action across an Amazon business.

From context-aware advertising optimization to Model Context Protocol (MCP), Jaimin explains how these technologies could fundamentally change the way sellers analyze data, manage campaigns, optimize listings, and build workflows.

Meet Jaimin: The Amazon Seller Building the Agentic Operating System

Jaimin Patel brings together two perspectives that rarely exist in the same person: deep technology experience and firsthand Amazon selling experience.

Before founding Aakaar AI, Jaimin spent nearly a decade building technology, commerce, and AI products. He led Facebook Shops and commerce advertising products at Meta, worked on supply chain intelligence and routing at Wayfair, and later contributed to Apollo’s growth as a product leader.

But he has also built and scaled his own private-label Amazon brand.

That meant experiencing the same tedious processes sellers deal with every day, from managing keywords in spreadsheets to analyzing performance and making optimization decisions manually.

Those experiences ultimately led him to create Aakaar AI: an agentic AI platform designed specifically around the realities of running an Amazon business.

AI for PPC Isn’t New, But Agents Are Different

Amazon advertising software has used automation for years. The important distinction, according to Jaimin, is understanding what sits behind that automation.

Historically, many platforms have relied on:

  • Machine learning models.
  • Rule-based PPC automation.
  • Predetermined conditions that trigger bid changes.
  • Human managers monitoring and adjusting those rules.

Those systems can be powerful, but an AI agent is supposed to go further.

Instead of simply reacting when a metric crosses a threshold, an agent can reason through why something happened, incorporate additional context, recommend an action, and explain the reasoning behind that recommendation.

Key Distinction: A rule can tell you what condition was triggered. An agent should be able to help explain why the change makes sense in the broader context of the business.

Context Intelligence Changes the PPC Equation

One of the central concepts behind Aakaar AI is what Jaimin calls context intelligence.

Imagine your CPC changes significantly. A traditional rule-based system might respond because a predetermined threshold was crossed.

A human PPC manager would typically look deeper.

They might consider:

  • Seasonality.
  • Inventory levels.
  • Historical performance.
  • Product characteristics.
  • Previous advertising decisions.
  • Other signals influencing performance.

That wider context is what Jaimin wants AI agents to understand.

Aakaar AI ingests signals, context, and data points so its agents can reason through optimizations rather than looking at one metric in isolation.

Strategic Shift: The goal isn’t simply to automate decisions faster. It’s to give AI enough context to make more informed decisions in the first place.

From Reactive AI to Proactive Agents

Chat-based AI has made accessing information dramatically easier. Sellers can ask a question and receive an answer without manually digging through reports.

But that still leaves the human responsible for deciding what questions to ask.

Jaimin sees agents moving beyond this reactive model.

Rather than waiting for a seller to request an analysis, a true agentic platform can identify what needs attention, recommend what should happen next, and potentially execute the appropriate task.

That becomes especially valuable at scale.

A seller managing:

  • 500 products.
  • 5,000 keywords.
  • Multiple campaigns.
  • Constantly changing marketplace conditions.

…cannot realistically evaluate every signal and piece of context manually.

Big Opportunity: The real value of AI isn’t making the same workload slightly smarter. It’s reducing the amount of manual effort required to manage the business.

Why Adding AI to an Old System Isn’t Enough

There’s an AI gold rush happening across eCommerce software.

Existing platforms are adding AI features, while new companies are launching tools centered around AI from day one.

Jaimin sees an important distinction between the two.

Many existing platforms were originally built around rule-based or traditional machine-learning architectures. Adding a conversational AI layer can improve the user experience, but the underlying system may still operate the same way.

Agentic platforms are designed differently.

The objective is to build workflows where AI behaves more like a capable team member: reasoning, executing tasks, learning from outcomes, and collaborating across different functions.

Core Idea: Putting a chatbot on top of existing software doesn’t automatically make the underlying system agentic.

AI Agents Can Learn From Their Decisions

One major limitation of traditional rule-based automation is that rules don’t naturally learn from what happened after they fired.

Jaimin describes Aakaar AI as having a feedback loop designed to address this.

When the system recommends an optimization, it can examine:

  • The previous decision.
  • Whether the seller accepted or rejected it.
  • What happened after the change.
  • Whether the expected outcome actually occurred.

That information can then influence future recommendations.

If an AI makes a decision with 80% confidence and the outcome doesn’t match expectations, that result becomes another learning signal.

It’s much closer to how a strong human operator improves over time: make a decision, observe the outcome, learn, and adjust.

Growth Principle: Automation becomes more valuable when the system can learn from previous decisions instead of endlessly repeating static rules.

What Is MCP, and Why Should Amazon Sellers Care?

One of the biggest topics in the episode is MCP, or Model Context Protocol.

Jaimin describes it as a connector between AI systems and external data or tools.

Historically, software platforms communicated primarily through APIs. One platform would expose specific endpoints, and another platform would need to build the infrastructure required to access, process, and use that information.

MCP creates a standardized way for AI systems such as Claude or ChatGPT to interact with outside systems.

Jaimin compares it to USB-C for AI.

Instead of creating a completely different connector for every AI application, an MCP server can provide tools and context that compatible AI systems can use.

For Amazon sellers, that opens up a much bigger possibility than simply chatting with an AI assistant.

Simple Explanation: MCP can serve as the bridge that lets an AI system work with your Amazon business data and available actions instead of operating only on what you manually paste into a chat.

One AI Connection Across Your Amazon Business

Aakaar AI isn’t limited to PPC data.

According to Jaimin, the platform can work with data across:

  • Amazon Ads.
  • Seller Central.
  • Vendor Central.
  • Brand Analytics.
  • Listings.
  • Other Amazon-related information.

That wider dataset matters because different areas of an Amazon business influence each other.

Danan gives the example of PPC and organic performance. An advertising decision that looks unprofitable when viewed only through immediate PPC results may have a different impact when organic sales and longer-term performance are considered.

Historically, finding those relationships could require manually analyzing enormous amounts of account data.

Agentic systems have the potential to connect those signals much faster.

Seller Advantage: The more relevant context an AI can access, the less likely sellers are to make decisions based on a single isolated metric.

Your Claude or ChatGPT Can Become an Amazon Interface

One of the most interesting parts of Aakaar AI’s MCP approach is that sellers aren’t necessarily limited to interacting with AI inside Aakaar itself.

Jaimin explains that sellers can connect their Amazon account and use their existing AI environment, such as Claude, to work with the data and available actions.

That could allow sellers to:

  • Ask questions about their Amazon account.
  • Analyze performance.
  • Develop strategies.
  • Take actions on campaigns or listings.
  • Build custom dashboards.
  • Create workflows.
  • Automate reporting.
  • Connect workflows with tools such as Slack, Google Docs, or Notion.
  • Create alerts around important events.

Aakaar essentially offers two complementary paths: sellers can activate Aakaar’s agents to handle much of the work automatically, or more hands-on operators can use the MCP connection to create their own workflows.

Power Move: Instead of waiting for software companies to build every dashboard or workflow you need, MCP can give advanced sellers the flexibility to create those experiences around their own business.

Why Not Just Connect Directly to Amazon Yourself?

Technically capable sellers and developers may wonder why they need a platform at all.

Amazon has been developing MCP capabilities of its own, and APIs already allow developers to access Amazon data.

But accessing the data is only part of the problem.

Jaimin points to several additional considerations:

  • Security.
  • Authentication.
  • Traceability.
  • Observability.
  • Tool design.
  • Data pipelines.
  • Controlling how AI accesses and acts on information.

There’s also the issue of giving an AI system too many poorly structured tools.

Jaimin warns that simply loading an MCP with a massive number of tools isn’t necessarily better. Too many options can create confusion and increase the possibility of hallucinations or incorrect tool selection.

Important Lesson: More AI tools don’t automatically create a better AI system. The architecture and context around those tools matter just as much.

The Future of Amazon Software May Be Workflows, Not Dashboards

For years, Amazon sellers have logged into software platforms, opened dashboards, studied reports, adjusted filters, and manually decided what to do next.

Agentic AI could challenge that entire model.

Instead of asking:

“What does this dashboard tell me?”

The future may look more like:

“What needs my attention today?”

And eventually:

“Handle everything that doesn’t require me.”

That shift could change the role of Amazon software from a place where sellers manage information into a system that actively helps manage the business.

Future Focus: The winning AI platforms may not be the ones with the most dashboards. They may be the ones that eliminate the need to constantly look at dashboards in the first place.

The eCom Growth Show Listener Offer

Jaimin also offered listeners of The eCom Growth Show a special discount on Aakaar AI.

Listeners can receive 25% off any Aakaar AI plan, including its MCP or ad agent plans.

There isn’t a dedicated coupon code. When signing up, mention The eCom Growth Show or Danan Coleman so the Aakaar AI team knows you came from the episode.

Listener Perk: If you’re interested in testing an agentic approach to Amazon management, mention the show when signing up to receive the 25% discount.

Connect with Jaimin Patel

Final Thoughts

AI in Amazon selling is moving beyond chatbots, static automations, and rules that adjust bids when a metric crosses a predetermined threshold.

The bigger transition is toward systems that understand context, reason through problems, take action, and learn from previous outcomes.

For sellers, MCP adds another layer to that transformation by creating a bridge between AI systems and the actual tools and data required to operate an Amazon business.

The question may soon shift from “What AI features does my software have?” to “How much of this work does my AI actually handle for me?”

For sellers managing thousands of keywords, multiple products, changing listings, advertising campaigns, and mountains of Amazon data, that difference could be enormous.

Stay tuned for more episodes of The eCom Growth Show, where Amazon sellers and eCommerce operators explore the technology, systems, and strategies reshaping how modern brands are built and scaled.