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Use case · Vendor switching

Find where a competitor's customers are unhappy

Customers say in public when a tool fails, when the price goes up and when they leave.

Get a scoped data testSee all four social use cases

What you build

From signal to action

First the view a seller gets, then one record behind it. Both come from the public sample files.

01

What your team sees

Five records from Reddit and product reviews. Each one names the vendor, the complaint and who wrote it. Two also name the vendor the customer compares.

Vendor displacement

The records are real, from the public sample files for Reddit and product reviews. Join them to your install data to see which of your accounts run the tool.

02

The record your product receives

One review record as it arrives. It carries the phrase that shows the intent to leave, how urgent that intent is, and the job title of the reviewer.

Record · product-reviews.csv
signal_type
g2-product-review
signal_subtype
ReliabilityIssues
company.domain
quadient.com
insight.headline
President warns against Quadient AP due to unreliable sync and absent support.
data.switching_intent.signal_phrase
Due to this I would recommend you stay away from this solution.
data.switching_intent.urgency
immediate
data.decision_maker_complaint.title
President

A real record from the public sample file. The field names are the ones your code reads.

How it arrives

Built for how your stack already works

GCS push for a daily file per feed. REST API for one account on demand. MCP server for AI agents.

  • Every signal of a feed, on a schedule

    GCS push

    Daily to weekly
    Scheduled exports to your bucket in JSONL, Parquet, or CSV.
    gs://autobound-<signal>/<date>/output.jsonl
  • One account or one contact, right now

    REST API

    JSON over HTTPS
    Real-time search, enrich, and filter across every signal type.
    $ curl https://signals.autobound.ai/v1/companies/search
  • An AI agent that asks its own questions

    MCP server

    Agent-native
    Every signal type as a tool in Claude, ChatGPT, or Cursor.
    $ claude mcp add --transport http autobound https://mcp.autobound.ai/mcp

The feeds

The feeds behind this use case

Each feed page shows how we build the signal, the fields, and a sample file.

Reddit mentions →Complaints, outages, price changesProduct reviews →Churn, reliability, pricing

FAQ

Common questions

How do we get from a complaint to an account?

The record names the vendor. Join the vendor to your install data to list the accounts.

Which complaints do the records cover?

Review records carry subtypes such as reliability issues, pricing concerns and active churn. Reddit records add outages and price changes.

How much data is there?

The page of each feed and the coverage report list the volume, measured each week.

See which vendors lose customers in public

Send a list of company domains. We return the signals we hold for each one, so you judge the fit on accounts you know.

Get a scoped data test