Install Jentic One Beta

Connect the Natural Language Understanding to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Natural Language Understanding, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fcloud.ibm.com%2Fibm-cloud" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fcloud.ibm.com%2Fibm-cloud" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Natural Language Understanding API.

Analyse free text, raw HTML, or a public URL for entities, keywords, sentiment, emotion, and concepts

Detect categories and high-level topics in long-form content with the built-in taxonomy

Extract semantic roles, syntax tokens, and relations between entities in a document

Train and manage custom classification models for domain-specific labelling tasks

GET STARTED

Start building with Natural Language Understanding API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Natural Language Understanding API for, with concrete tasks.

★ Brand and Entity Monitoring

Pull entities and sentiment out of news articles, blog posts, and social content to track how a brand is mentioned across the web. The /v1/analyze endpoint accepts a URL or raw HTML, removes ads and navigation, and returns extracted entities with confidence scores and per-entity sentiment, which is enough signal to drive a daily brand monitoring report. The same call returns concepts and keywords for thematic analysis.

Call POST /v1/analyze with a URL and features={entities, sentiment, keywords} and return the top 10 entities ranked by relevance

Support Ticket Classification

Train a custom classification model on historical support tickets and route new tickets automatically by predicted label. The /v1/models/classifications endpoints handle the full lifecycle - create, list, retrieve, update, and delete a model - and the analyze endpoint applies the trained model at inference time. This replaces brittle keyword-rule routing with a model that adapts to the way customers actually phrase their issues.

Train a classifications model from a CSV of labelled tickets, then call /v1/analyze with classifications.model={model_id} on a new ticket

Content Categorisation

Map articles or product descriptions onto a customer-defined hierarchy using a custom categorisation model. The /v1/models/categories endpoints expose model lifecycle calls, and analyze applies the model to new content with hierarchical category scores. This is useful for content management systems, ad targeting, and recommendation pipelines that need consistent topic labels.

Submit a labelled training set to POST /v1/models/categories, poll until the model is available, then run /v1/analyze with categories.model on new articles

Voice-of-Customer Analytics

Run customer feedback, reviews, and survey responses through the analyze endpoint to extract emotion, sentiment, and the entities customers discuss most. Per-entity sentiment makes it possible to say 'shipping is rated negatively while product quality is rated positively' rather than reporting a single average score, which is the typical bar for a useful CX dashboard.

POST /v1/analyze with text=review and features={sentiment.targets, emotion, entities} and return per-target sentiment

Agent-Driven Text Analysis via Jentic

AI agents that need text analytics can search Jentic for an intent like 'extract entities from text' and Jentic returns the IBM NLU analyze operation with its full input schema. The IAM API key lives in your Jentic One instance, so the agent receives a scoped credential per call and never logs the raw key. This collapses what is normally a multi-step IBM Cloud onboarding into a single search-load-execute pattern.

Search Jentic for 'analyse text for entities and sentiment', load the analyze operation, execute with the user's text, and return entities plus document sentiment

Key Endpoints

14 endpoints — jentic publishes the only available openapi specification for natural language understanding, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/v1/analyze

Analyse text, HTML, or URL for selected features

GET

/v1/models

List Watson Knowledge Studio models

POST

/v1/models/categories

Train a custom categorisation model

GET

/v1/models/categories

List categorisation models

POST

/v1/models/classifications

Train a custom classification model

GET

/v1/models/classifications

List classification models

DELETE

/v1/models/{model_id}

Delete a Watson Knowledge Studio model

POST

/v1/analyze

Analyse text, HTML, or URL for selected features

GET

/v1/models

List Watson Knowledge Studio models

POST

/v1/models/categories

Train a custom categorisation model

GET

/v1/models/categories

List categorisation models

POST

/v1/models/classifications

Train a custom classification model

GET

/v1/models/classifications

List classification models

DELETE

/v1/models/{model_id}

Delete a Watson Knowledge Studio model

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring IBM Natural Language Understanding by hand means passing an IAM API key in the Authorization header, pinning the correct regional host such as the us-south Watson endpoint, mapping its analyze and model endpoints, and handling retries yourself. Through Jentic you install once, import the Natural Language Understanding API from the API Directory, store the IAM key once, and your agent calls it.

Permission scoping

IBM NLU's analyze target and feature options travel in the request body rather than the URL path, so scope the agent to the operations it needs, such as analyzing text or listing models. You choose which operations are in that set, so deleting a custom model is not included unless you add it.

Credential isolation

Your IBM Cloud IAM API key is stored once, encrypted, by your own Jentic One instance, which injects a per-call scoped token in the Authorization header. The raw IAM key never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'extract entities from text' or 'classify a ticket', and Jentic returns the IBM NLU analyze operation with its full input schema, so the agent calls the right endpoint without browsing IBM's API reference.

FAQs

Specific to using Natural Language Understanding API through Jentic.

Why is there no official OpenAPI spec for Natural Language Understanding?

IBM Cloud does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Natural Language Understanding via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.

What authentication does the Natural Language Understanding API use?

The API uses IBM Cloud IAM, which is presented as an apiKey scheme in the Authorization header. Through Jentic, the IAM API key is stored encrypted in the vault and a scoped credential is issued per call so the raw key never enters the agent's context.

Can I extract sentiment per entity with this API?

Yes. POST /v1/analyze accepts a sentiment.targets array or sentiment.document=true and returns sentiment scores both at the document level and per entity or keyword, which lets you separate signals like positive product feedback from negative shipping feedback in the same response.

What are the rate limits for the Natural Language Understanding API?

Rate limits depend on the IBM Cloud plan. Lite plans cap usage at 30,000 NLU items per month and a small number of requests per second, while Standard and Premium plans raise both the throughput and the items-per-month allowance. Check the IBM Cloud plan page for the exact tier limits.

How do I analyse a piece of text for entities and sentiment through Jentic?

Run pip install jentic, search for 'analyse text for entities and sentiment', load the analyze operation, then execute with text and features={entities, sentiment}. The call hits POST /v1/analyze and returns the structured analysis.

Can I train custom models against this API?

Yes. The /v1/models/classifications and /v1/models/categories endpoints support full lifecycle management for custom classifiers and categorisers, and the trained model_id is then passed into /v1/analyze at inference time. Watson Knowledge Studio is the recommended path for custom entity and relation models.

Can I limit what my agent is allowed to do with the Natural Language Understanding API?

Yes. Because you run Jentic One yourself, your own rules decide which Natural Language Understanding operations and credentials the agent may use. You can allow just the operations it needs, such as POST /v1/analyze to extract entities and sentiment or GET /v1/models to list trained models, and leave everything else out of the set. Since the analyze target and feature options travel in the request body rather than the URL path, destructive calls like DELETE /v1/models/{model_id} are never available to the agent unless you explicitly add them.

Cloud Ibm Natural Language Understanding

AI ReadinessAI-Aware (B+)67/100

See full scorecard

Agent-ready OpenAPI document · curated by JenticAI/MLMl InferenceapiKey14 EndpointsREST

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For Agents

Extract entities, keywords, sentiment, emotion, categories, and relations from text or web pages, and manage custom classification models.

Use for: I need to extract people and organisations from a news article, Detect the sentiment of a customer review, Find all keywords and concepts in a long blog post, Classify support tickets into a custom taxonomy

Not supported: Does not handle text generation, translation, or speech transcription - use for feature extraction and classification of existing text only.

Jentic publishes the only available OpenAPI specification for Natural Language Understanding, keeping it validated and agent-ready. IBM Watson Natural Language Understanding analyses text, raw HTML, or a URL and returns structured features such as entities, keywords, concepts, categories, sentiment, emotion, syntax, semantic roles, and relations. The service strips advertisements and boilerplate from HTML before analysis and supports custom Watson Knowledge Studio models for domain-specific entity and relation extraction. Custom classification and categorisation models can be trained, listed, and deleted directly through the API.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: ASignals: 4

98%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

96%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

35%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: BSignals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

50%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: C-Signals: 4

70%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

93%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

48%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: C-Signals: 1

50%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

100

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: A+Signals: 1

100%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Natural Language Understanding to your agent

Capabilities

Start building with Natural Language Understanding API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Cloud Ibm Natural Language Understanding

Jentic AI Readiness Score

Natural Language Understanding

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness