Feedback Intelligence with AI Agents

The short answer

Indellia is a feedback intelligence platform for consumer brands. It natively ingests reviews from 20+ retail and review channels, support tickets from seven help-desk tools, and returns from Loop, Narvar, and AfterShip — then uses named AI agents to link each piece of feedback to a specific product (SKU, UPC, Model#, or ASIN), detect themes and anomalies, and surface answers grounded in real reviews.

The Pipeline

What goes in.

Feedback doesn't live in one place. Indellia meets it where it is.

Retail review channels

Native retail ingestion.

Amazon, Walmart, Best Buy, Costco, Lowe's, Target, Home Depot, and every Bazaarvoice-powered retailer page. One connector per retailer, maintained by Indellia.

Support & ticketing

Every help-desk signal.

Zendesk, Intercom, Freshdesk, Gorgias, Gladly, Kustomer, and Front. Tickets, macros, and internal notes read together with reviews.

Product returns

Return reasons and RMA notes.

Loop Returns, Narvar, and AfterShip. Return reasons are structured feedback. They belong with reviews for every SKU view.

Surveys, calls, warehouse

The rest of the picture.

Typeform, SurveyMonkey, Qualtrics (read-only). Call data from Grain, Gong, Twilio. Snowflake read for structured warehouse data. Shopify and Segment for commerce and CDP context.

Pipeline diagram

retail channels ┐
support tickets ┤
product returns ├──>  SKU Agent  ──>  Theme Agent  ──>  Anomaly Agent
surveys, calls  ┤                  ╲                  ╲
warehouse reads ┘                   >── indelliaGPT ──╳── outputs
                                   ╱                  ╱
                   Defect Agent ──┘   Response Agent ╱

The Agents

Seven agents with defined jobs.

Each agent is a named, addressable component. Use them in the web app, through Slack, or via the Indellia MCP Server from Claude, ChatGPT, and Cursor.

Shipped Agent · 01

Theme Agent.

Clusters every piece of feedback into named topics using deterministic topic modeling. No pre-built taxonomy required. Themes update as new reviews arrive, and you can merge, rename, or pin them.

Auto-generated themes per SKU and per channel
Custom taxonomies layered on top
Theme drift tracking over time
Foundation for every downstream agent

Themes · last 30 days AUTO

Battery drain 412

Overheating during charge 288

Setup confusing 141

Packaging damaged 97

Anomaly · SKU-4421 · Overheat ALERT

Predicted rate 3.1%

Actual rate (7d) 8.4%

Delta vs forecast +5.3pp

Channel trigger Amazon · Walmart

Shipped Agent · 02

Anomaly Agent.

Monitors sentiment, volume, and star-rating trends per SKU, per theme, per channel. Triggers alerts when patterns break — not on thresholds but on prediction-vs-actual deltas.

Prediction-based alerting beats keyword alerts
Per-SKU and per-theme sensitivity
Slack and email delivery
Often catches defects before warranty data

Shipped Agent · 03

indelliaGPT^™ .

Conversational Q&A grounded in your full feedback corpus. Every answer is returned with citations to the actual source reviews, tickets, or returns. Deterministic retrieval with generative summarization — explicitly positioned against LLM hallucination.

Natural-language queries across every SKU
Every answer cites the underlying feedback
No cross-tenant training
Works inside the app, Slack, and MCP

indelliaGPT^™ ASK

Q. What are users saying about the PAN-LX-7 battery?

A. Reviews mention shorter-than-expected runtime (412 mentions) and overheating during charge (288). Both themes spiked in the last 14 days, concentrated on Amazon and Walmart. [cites 14 source reviews]

SKU Agent · Normalization LINK

Amazon ASIN B0C2N4X5F7

Walmart Item ID 4402891

Best Buy SKU 6541892

Internal Model# PAN-LX-7

Shipped Agent · 04 · The Moat

SKU Agent.

Links every incoming piece of feedback to a specific product via Model#, UPC, ASIN, or retailer-specific SKU. Handles retailer-specific identifiers and normalizes across channels. This is the moat — no competitor offers it natively for consumer brands.

Amazon ASINs, Walmart Item IDs, Best Buy SKUs
UPC and Model# lookup
Custom SKU aliases per brand
Unified product view across channels

Beta Agent · 05

Defect Agent (Beta).

Built for QA and manufacturing teams. Reads reviews and returns for a specific SKU and surfaces the root-cause themes behind failures — often weeks before defect rates show up in warranty data.

SKU-level defect rate estimation from reviews
Returns data fused with review data
Root-cause tree view per defect theme
In beta with select hardware and appliance customers

Defect Agent · PAN-LX-7 BETA

Overheat on fast-charge 184 signals

Firmware v2.1 correlation 0.79

Returns share 31%

Warranty data lag ~18 days

Response Agent · Draft BETA

Review (2★): "Battery drained in an hour on my first hike. Returning."

Draft: "Hi — sorry the battery disappointed on day one. If you can share your firmware version from Settings > About, we'll send a replacement under warranty. — Panasonic Support"

Beta Agent · 06

Response Agent (Beta).

Drafts on-brand responses to reviews across every channel you've connected. Customizable tone and guardrails. Currently in beta; a public API for direct response posting is on the roadmap.

Brand-voice templates per product line
Sentiment-aware tone adjustment
Approval queue before push-to-post
Works with Amazon, Walmart, and retailer accounts you connect

Shipped MCP · Agent · 07

Indellia MCP Server.

The Indellia MCP Server plugs your feedback corpus into Claude, ChatGPT, and Cursor. Ask questions about your own reviews from the AI tools your team already uses — no context switching.

Model Context Protocol — standards-compliant
Tool-level auth per workspace
Read-only by default; write actions require explicit scope
Read the MCP for voice of customer guide

{
  "tool": "indellia.ask",
  "input": {
    "question": "Why are Amazon returns
                  spiking on PAN-LX-7?",
    "window": "14d"
  },
  "output": {
    "summary": "Overheat on fast-charge,
                 correlated with firmware v2.1.",
    "citations": [
      "review_amzn_2891",
      "return_loop_774"
    ]
  }
}

Outputs

Where your feedback picture shows up.

Indellia web app

Dashboards, search, theme explorer, SKU view, alerts.

Indellia MCP Server

Feedback tools in Claude, ChatGPT, and Cursor.

Slack

Anomaly alerts, daily digests, query-from-channel.

Email

Weekly summaries, per-SKU alerts.

CSV export

Themes, sentiment scores, raw feedback.

Snowflake pushback

Write-back to your warehouse (Mid-Market).

Pricing

$495 or $1,995 per month. That's the whole thing.

SME

$495 / month

Teams under 100 employees. Every shipped agent. Unlimited users. Unmetered data.

Start Free Trial

Mid-Market

$1,995 / month

Teams 100+ employees. SSO, Snowflake pushback, dedicated CSM.

Book a Demo

See full pricing details.

Ask Indellia

Have a specific question?

Indellia's AI agents answer with citations from real customer feedback across Amazon, Walmart, Best Buy, and 20+ retail channels.

FAQ

Frequently asked questions

Which retail channels does Indellia ingest?

Indellia natively ingests reviews from Amazon, Walmart, Best Buy, Costco, Lowe's, Target, Home Depot, and every Bazaarvoice-powered retailer page. That covers the vast majority of US consumer-brand retail review volume. Support ticket data comes from Zendesk, Intercom, Freshdesk, Gorgias, Gladly, Kustomer, and Front. Returns data comes from Loop Returns, Narvar, and AfterShip.

How does the SKU Agent link reviews to a specific product?

The SKU Agent resolves retailer-specific identifiers — Amazon ASINs, Walmart Item IDs, Best Buy SKUs, and others — to your internal Model# or UPC. You upload a simple SKU map once; the agent maintains the link as new reviews arrive. For brands with complex hierarchies (variant SKUs, bundled listings), custom SKU aliases are supported.

Is indelliaGPT^™ based on a large language model?

indelliaGPT^™ uses deterministic retrieval to find the source reviews, then a language model to summarize the answer. Every response cites the specific reviews that support it. We do not train foundation models on your feedback, and there is no cross-tenant training. The architecture is explicitly positioned against LLM hallucination because the answer is grounded in citable evidence.

What does the Indellia MCP Server do?

The Indellia MCP Server exposes feedback intelligence as a Model Context Protocol server. Connect it to Claude Desktop, ChatGPT, or Cursor and query your feedback from the AI tools you already use. Access is scoped per workspace; the default scope is read-only. Write actions (posting a response, for example) require an explicit scope grant.

Which agents are in beta versus shipped?

Shipped and in production: Theme Agent, Anomaly Agent, indelliaGPT^™ , SKU Agent, and the Indellia MCP Server. In beta: Defect Agent (for QA and manufacturing teams) and Response Agent (for review replies). Beta agents are included at no additional cost during the beta period.

Get started

See Indellia on your SKUs.

Start the free trial, or book a 30-minute walkthrough with a member of our team.

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Feedback intelligence with AI agents — built for consumer brands.