Akkio API

Name: Akkio API API
Brand: Akkio API
Availability: InStock

★ Only Publicly Available SpecAI/MLMl InferenceapiKey16 EndpointsREST

For Agents

Upload datasets, train predictive models, run predictions, and ask natural-language data questions on Akkio without managing ML infrastructure.

Quickstart

Get started with Akkio API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"train a predictive model on a dataset"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Akkio API API.

Upload a tabular dataset to Akkio with appended rows or replace an existing dataset by name

Submit a model training task on a chosen dataset and target column, then poll for status and results

Make a prediction by sending input rows to a trained Akkio model

Run a Chat Explore query against a dataset and retrieve the conversational answer when ready

GET STARTED

Start building with Akkio API API

Explore with Jentic

View OpenAPI Spec

Use for: I need to train a predictive model on a CSV dataset without writing ML code, Make a prediction using a trained Akkio model from inside an agent workflow, Upload a new dataset to Akkio for analysis, Check the status of a running Akkio model training task

Not supported: Does not handle deep learning model training, real-time streaming inference, or LLM fine-tuning — use for no-code tabular predictive modeling and Chat Explore queries only.

Jentic publishes the only available OpenAPI specification for Akkio API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Akkio API, keeping it validated and agent-ready. Akkio is a no-code predictive AI platform that lets teams upload tabular datasets, train classification and regression models, and run predictions through a REST API. The same API exposes a Chat Explore endpoint for asking natural-language questions about a dataset, plus project and dataset management for organising data across teams. Authentication uses an API key supplied either as an X-API-Key header or an api_key query parameter.

Use Cases

Patterns agents use Akkio API API for, with concrete tasks.

★ No-code churn prediction for SaaS teams

A customer success team uploads a dataset of historical accounts and churn outcomes to Akkio, trains a classification model on the churned column, and then calls the prediction endpoint nightly with active accounts to score churn risk. Because Akkio handles feature processing and training internally, the team avoids managing notebooks or ML pipelines. End-to-end setup takes hours rather than weeks.

POST /api/v1/models/train/new with the customers dataset and target column 'churned', poll /api/v1/models/train/{task_id}/status until complete, then POST /v1/models with active account rows to get churn probabilities

Conversational analysis of a sales dataset

An analyst loads a quarterly sales dataset into Akkio and uses Chat Explore to ask questions like 'which region grew the most last quarter?' through the API. The /api/v1/chat-explore/new endpoint accepts the question, returns a task ID, and the result endpoint delivers a structured answer once the model finishes. This embeds natural-language data Q&A into internal tools without exposing the underlying tabular data.

POST /api/v1/chat-explore/new with the sales dataset id and the question, then GET /api/v1/chat-explore/result/{task_id} once the status endpoint reports completion

Lead scoring inside a CRM workflow

A sales operations team trains an Akkio regression model on closed-won deals and exposes the prediction endpoint to a CRM automation. As new leads enter the CRM, the agent posts the lead's attributes to the Akkio model and writes back a lead score on the record. Akkio handles the training and inference; the CRM stays the system of record.

POST /v1/models with model_id and an input row containing lead attributes, then update the matching CRM record with the predicted score

Agent-driven dataset and model lifecycle

A Jentic agent receives an intent like 'train a model on this dataset', uploads the rows via POST /v1/datasets, submits a training task, polls for completion, and finally calls the prediction endpoint with new inputs. The same agent can list existing models, retire stale ones, and create new Akkio projects for separate use cases. The full loop runs without a human opening the Akkio UI.

Search Jentic for 'train a predictive model on a dataset', load the Akkio training operation schema, execute POST /api/v1/models/train/new and chain the status and result calls to deliver a trained model id back to the user

Key Endpoints

16 endpoints — jentic publishes the only available openapi specification for akkio api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/v1/datasets

Create a dataset or append rows

POST

/api/v1/models/train/new

Submit a model training task

GET

/api/v1/models/train/{task_id}/status

Get training task status

POST

/v1/models

Make a prediction with a trained model

POST

/api/v1/chat-explore/new

Submit a Chat Explore natural-language query

GET

/api/v1/chat-explore/result/{task_id}

Retrieve a Chat Explore result

POST

/api/v1/projects

Create a new project

POST

/v1/datasets

Create a dataset or append rows

POST

/api/v1/models/train/new

Submit a model training task

GET

/api/v1/models/train/{task_id}/status

Get training task status

POST

/v1/models

Make a prediction with a trained model

POST

/api/v1/chat-explore/new

Submit a Chat Explore natural-language query

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Akkio's X-API-Key and api_key parameter are stored encrypted in the Jentic credential vault. Agents receive a scoped session and never see the raw key in context.

Intent-based discovery

Agents search Jentic by intent (e.g. 'train a predictive model on a dataset' or 'run a prediction'), and Jentic returns the matching Akkio operation with its input schema so the agent can call it without browsing the docs.

Time to first call

Direct Akkio integration: 1-2 days for auth wiring, dataset upload, training polling, and prediction calls. Through Jentic: under an hour using search, load, and execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

OpenAI API

Use general-purpose LLMs and code interpreter for ad-hoc data analysis instead of training a tabular model

Choose OpenAI when the agent needs flexible reasoning over arbitrary data; choose Akkio when the use case is repeated predictions on a structured dataset

Alternative

Hugging Face Inference API

Run hosted open-source models for inference rather than training a custom no-code model

Choose Hugging Face for off-the-shelf model inference; choose Akkio when training a tabular predictive model on user data is required

Complementary

Airtable API

Source structured rows from an Airtable base to feed Akkio dataset uploads

Pair with Akkio when the system of record for the rows being trained on or scored lives in Airtable

FAQs

Specific to using Akkio API API through Jentic.

Why is there no official OpenAPI spec for Akkio API?

Akkio does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Akkio API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Akkio API use?

Akkio uses an API key passed either as the X-API-Key request header or as an api_key query parameter. Jentic stores the key encrypted in its credential vault and injects it at execution time, so the raw key never enters the agent's prompt or response context.

Can I train a predictive model on a CSV dataset with the Akkio API?

Yes. POST your rows to /v1/datasets to register the dataset, then POST /api/v1/models/train/new with the dataset reference and target column. Poll /api/v1/models/train/{task_id}/status until the task completes, then call /api/v1/models/train/{task_id}/result for the trained model id.

What are the rate limits for the Akkio API?

The OpenAPI spec does not declare per-endpoint rate limits. Plan-based limits apply on Akkio's side; check your account tier in the Akkio dashboard before running large batch prediction or training jobs.

How do I run a prediction with the Akkio API through Jentic?

Search Jentic for 'make a prediction with an Akkio model', load the operation schema for POST /v1/models, and execute it with the model id and an input rows array. Jentic returns the prediction payload directly to the agent.

Does Akkio support natural-language questions over a dataset?

Yes, via Chat Explore. POST /api/v1/chat-explore/new with the dataset id and your question to get a task id, then GET /api/v1/chat-explore/result/{task_id} once the status endpoint reports completion to retrieve the answer.