AWS Comprehend
AIRun Amazon Comprehend natural-language processing on any text in a flow: sentiment, entities, key phrases, dominant language, and syntax, with native AWS Signature V4 signing.
What This Integration Enables
Comprehend turns unstructured text into structured signals. It scores sentiment, pulls out entities and key phrases, detects the dominant language, and returns syntax, all synchronously and fast enough to run on every record as it arrives. That makes it a natural first pass on any stream of free text: reviews, tickets, survey responses, inbound messages. The output is only useful if something acts on it. An orchestration layer takes the sentiment score and the entities and decides what happens next: file it, route it, or escalate it. It pulls a person in when the signal is strong enough to warrant one. FlowRunner is built for that layer, so text analysis becomes a routing decision rather than a dashboard nobody checks.
Without FlowRunner
With FlowRunner
Use Case Scenarios
Review Sentiment Routing
Customer reviews arrive continuously. The agent runs Detect Sentiment and Detect Entities on each one, tags it by tone and by the product named, and files it in the analytics store. The product team gets a rollup of negative reviews grouped by feature instead of reading the full stream. The analysis that used to require a person skimming every review runs automatically as they land.
Multilingual Ticket Sorting
Support tickets arrive in several languages. The agent runs Detect Dominant Language on each ticket and routes it to an agent who speaks it, then extracts key phrases so the receiving agent sees the gist before opening it. Tickets reach the right person in the right language without a manual sorting step.
Survey Response Analysis
Open-ended survey responses are hard to act on at volume. The agent runs sentiment and key-phrase detection across every response, clusters the themes, and produces a summary of what people are asking for. Responses that score strongly negative and mention a specific risk are pulled out and routed to the owning team rather than folded into an aggregate.
Human-in-Loop Highlight
Sentiment analysis is good at scoring tone and bad at judging what a message deserves. When Comprehend scores a message as strongly negative or flags text that names a person or a safety concern, FlowRunner routes it through a [human-in-the-loop](/concepts/human-in-the-loop/) step rather than filing it with the rest. The agent pauses, assembles the message and its scores, and sends it to a manager via Slack. A person decides whether it is a routine complaint or something that needs a real response. The model catches the signal; a human weighs it.
Agent Capabilities
9 actionsSentiment
3- Detect Sentiment Analyzes a single document and returns the prevailing sentiment (POSITIVE, NEGATIVE, NEUTRAL, or MIXED) along with confidence scores for each sentiment class.
- Detect Targeted Sentiment Performs entity-level (targeted) sentiment analysis on a single document, returning each identified entity along with its mentions and the sentiment expressed toward it.
- Batch Detect Sentiment Analyzes up to 25 documents in a single request and returns the prevailing sentiment and confidence scores for each.
Entities
3- Detect Entities Identifies named entities (people, places, organizations, dates, quantities, and more) in a single document, returning each entity's type, text, confidence score, and character offsets.
- Detect PII Entities Locates personally identifiable information (PII) such as names, addresses, emails, phone numbers, and account identifiers in a single document, returning each entity's type, confidence score, and character offsets.
- Batch Detect Entities Identifies named entities in up to 25 documents in a single request. Returns a resultList (successful documents, each keyed by its input Index with its detected entities) and an errorList (documents that failed).
Key Phrases
1- Detect Key Phrases Extracts the key noun phrases from a single document, returning each phrase's text, confidence score, and character offsets.
Language
1- Detect Dominant Language Determines the dominant language of a single document, returning candidate language codes ranked by confidence score.
Syntax
1- Detect Syntax Performs part-of-speech tagging on a single document, returning each token's text, part-of-speech tag, confidence score, and character offsets.
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