C

Chaindesk

· #421 most-used

Query your AI agents and manage custom knowledge bases via API

CommunicationSupportDeveloperAIAutomation

Chaindesk is a no-code platform for building, deploying, and managing AI chatbots trained on your own data — documents, PDFs, Notion pages, Google Sheets, and more. Its REST API exposes agents, datastores, and datasources as first-class resources, so your Actionist agents can query AI agents for answers, manage knowledge base datastores, add and remove datasources, and respond to new-response events — all without touching the Chaindesk dashboard.

Average time saved
10 hours
per person · per month
≈ 1 workdays back

Eliminates manual work. Agents eliminate manual dashboard visits for querying chatbots, ingesting new knowledge documents, and monitoring conversation-response quality across support and ops teams.

Schedule

What your Chaindesk agent runs on autopilot

A week of scheduled jobs your Actionist agent will execute on your behalf.

28Scheduled jobs
7Agents at work
24/7Always on
Agents
TueThu
Tue
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Thu
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12p
1p
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Multi-app workflows

Chaindesk × every other app you use

End-to-end automations that span multiple apps — each one a real business outcome.

6Workflows
5Apps spanned
~16 hrsSaved / week
6Personas served
For customer success
Featured4 apps

Help article published, support agent trained within a minute

When a help article is published in Notion, the agent creates a datasource in Chaindesk, waits for ingestion to complete, and confirms to the support team via Slack that the AI agent can now answer questions about the new article. The full cycle completes within about a minute of publish, with no manual dashboard interaction.

~3 hrs

Time saved for your team — every week, on autopilot

The flow
Trigger·When a new help article is published in the CMS
Result
Create datasource in the support datastore with the article URLPost confirmation to #support-team that the new article is live in the AI agentLog article title, URL, and ingestion timestamp to the knowledge-base tracker
The win
Saved per run
20 min
Runs / week
~8×
Support agents always have the latest documentation in their AI assistant
Driven byCustomer Support Agent
ROI

Savings

What your team gets back — two angles: what you stop doing manually, and what that's worth.

Without Actionist

What you do manually today

With Actionist

What your agent runs for you

  • Sales
    25 min / week
    Manual competitive research before calls

    Reps manually search internal wikis, ask colleagues, or skip preparation entirely — each first call consumes 20+ minutes of pre-call research time.

    Sales Agent
    0 min
    Agent queries competitive intel before every call

    When a deal is created, the agent queries the Chaindesk competitive-intel agent and appends a briefing note to the deal record in minutes — the rep enters the first call with datastore-grounded positioning.

  • Marketing
    20 min / week
    Manual campaign knowledge base setup

    The marketing ops team manually creates a Chaindesk datastore, configures the agent, and uploads the brief document for each new campaign — typically 30–45 minutes of dashboard work per campaign.

    Marketing Agent
    0 min
    Agent provisions campaign knowledge base automatically

    When a campaign brief is confirmed, the agent creates the Chaindesk datastore and agent, ingests the brief, and posts the agent details to Slack — the knowledge base is queryable before the brief meeting starts.

  • Customer Support
    15 min / week
    Manual help article ingestion

    The support team manually adds each new or updated article URL to the Chaindesk datasource list — a task that gets skipped when the ticket queue is busy, leaving the AI agent working from outdated content.

    Customer Support Agent
    0 min
    Agent ingests new articles as they publish

    When a help article goes live, the agent creates the datasource and confirms ingestion within about a minute — the support AI agent knows about the new article before the first ticket about it arrives.

  • Human Resources
    12 min / week
    Manual policy datasource management

    HR manually removes old policy datasources and adds new ones whenever policies change — a step that often runs behind schedule, leaving the AI agent returning answers from outdated policy text.

    Human Resources Agent
    0 min
    Agent swaps old and new policy versions atomically

    When a policy is superseded, the agent deletes the old datasource and ingests the new one in a single workflow — the HR AI agent never answers questions from both versions simultaneously.

  • Finance
    18 min / week
    Manual data retention enforcement

    The compliance team manually reviews Chaindesk datastores quarterly, identifies expired documents, and deletes them one at a time — producing no audit trail and often running weeks behind the retention schedule.

    Finance Agent
    0 min
    Agent enforces retention automatically with an audit trail

    The finance agent identifies expired regulatory datasources each Friday and deletes them with a timestamped audit log — compliance evidence is generated automatically rather than assembled manually before each audit.

  • Operations
    30 min / week
    Manual Chaindesk resource audit

    Ops manually logs into the Chaindesk dashboard, navigates through agents and datastores, and checks each against the registry spreadsheet — a process that is done quarterly at best and often misses resources created outside the standard process.

    Operations Agent
    0 min
    Agent audits every agent and datastore weekly

    Every Monday the agent lists all Chaindesk resources, compares them to the approved registry, and flags unapproved agents and orphaned datastores — the ops team starts the week with a verified knowledge-graph snapshot.

  • Legal
    35 min / week
    Manual legal precedent search

    Lawyers manually search the internal contracts database and case files for relevant precedents before each contract review — typically 30+ minutes per flagged clause, done during the review session itself.

    Legal Agent
    0 min
    Agent surfaces precedents in under two minutes

    When a contract clause is flagged unusual, the agent queries the legal Chaindesk agent and appends datastore-grounded precedents to the contract record before the lawyer opens it for review.

+ 100s of other Chaindesk automations
Average time saved
16 hrs / person / month
Calculator

Calculate what your team saves

Team size
5 people
Hourly rate
$75 / hr
Hours saved / week
13
Hours saved / year
625
Annual ROI
$46,875

Based on Chaindesk's typical team usage — the visible tasks plus a few other automations the agent runs: ~2.5 hrs / person / week of admin work automated.

Connect

How to plug Chaindesk into Actionist

Pick the connection method that suits your environment.

Authenticate with a Chaindesk Bearer token. Generate one from your Chaindesk dashboard under Settings → API Keys, then paste it into Actionist.

1
Open API Keys in Chaindesk

Log in to Chaindesk, go to Settings → API Keys, and click Generate Key. Copy the token — you only see it once.

2
Paste into Actionist

In Actionist's Apps tab, find Chaindesk, click Connect, and paste your API key into the token field.

3
Test the connection

Actionist makes a read-only call to verify the handshake. Once confirmed, your agents can query datastores and agents immediately.

Credentials you'll need
API Key*
Chaindesk dashboard → Settings → API Keys → Generate key
Actions

15 actions your agent can call

Read and write operations available to your Actionist agent.

Triggers

0 events your agent can react to

Events your agent watches for, and the actions it kicks off in response.

This app has no triggers yet.
FAQs

Questions about Chaindesk + Actionist

How does Actionist connect to Chaindesk?
In the Apps tab, find Chaindesk and click Connect. Chaindesk uses Bearer-token authentication — generate an API key from your Chaindesk dashboard under Settings → API Keys, copy it, and paste it into the API Key field in Actionist. Actionist makes a read-only test call to verify the handshake before any agent actions run. The same token covers agents, datastores, and datasources, so one key unlocks the full Chaindesk API.
What can Actionist agents actually do with Chaindesk?
Actionist agents can query any of your Chaindesk AI agents and datastores — asking questions and retrieving AI-generated answers — and manage the full lifecycle of agents, datastores, and datasources through the Chaindesk REST API. That means creating and deleting agents, provisioning and naming datastores, ingesting new documents as datasources, removing outdated content, and auditing the state of your entire Chaindesk knowledge graph, all without touching the Chaindesk dashboard.
Can I query multiple Chaindesk agents from the same Actionist workflow?
Yes. A single Actionist workflow can call List Agents to discover agent IDs dynamically and then call Query Agent multiple times with different agent IDs — for example, routing a question to a support agent for the answer draft and to a compliance agent to check the response against policy, then combining the outputs before sending. Each Query Agent call is independent; you can pass a shared conversationId to maintain context across sequential queries to the same agent.
How quickly does a newly ingested datasource become queryable?
Ingestion time depends on document size and Chaindesk's processing queue, but most URL-based datasources complete within about a minute for typical help articles and policy documents. The recommended pattern in Actionist is to call Create Datasource, then poll Get Datasource until the status field returns 'synced' before running any Query Agent calls that depend on the new content. This ensures the Chaindesk agent is not queried before the document is indexed.
What types of datasources can I add to a Chaindesk datastore via the API?
The Chaindesk API accepts URL-based datasources (web pages, publicly accessible PDFs, Google Docs with link sharing enabled) and plain text content submitted directly in the request body. File uploads are supported via the Chaindesk dashboard but not through the REST API that Actionist integrates with. For PDF or document ingestion via automation, the recommended approach is to upload the file to a publicly accessible storage URL first, then submit that URL as a datasource through Actionist.
Can I use Actionist to enforce data retention on Chaindesk datastores?
Yes. The recommended pattern is to store datasource IDs and creation dates in a tracking sheet when you create datasources via Actionist. On a scheduled cadence, the Actionist compliance agent reads the tracking sheet, identifies datasources past their retention date, calls Get Datasource to confirm they still exist, and calls Delete Datasource for each expired item. Each deletion is logged with a timestamp and the name of the authorizing team member, giving you an audit trail for your next compliance review.
What happens to a Chaindesk agent if I delete its connected datastore?
The agent continues to exist in Chaindesk but will have no knowledge base attached — it will respond to queries using only its system prompt and the base model, without any of the custom data that gave it specialized knowledge. To avoid this, always call Update Agent to detach the datastore from the agent before deleting the datastore, or delete the agent first. When Actionist handles the cleanup as part of a post-campaign or offboarding workflow, it follows the delete-agent-then-delete-datastore order automatically.
How do I combine Chaindesk with other apps in an Actionist workflow?
Chaindesk integrates naturally with the apps where knowledge originates and where answers are delivered. Common patterns: create a datasource when a new Notion page, Google Drive document, or CMS article is published; query a Chaindesk agent and append the response as a note in HubSpot or a Jira ticket; post AI-generated answer drafts to Slack; log ingestion events and retention deletions to Google Sheets for audit purposes. Any of Actionist's connected apps can send input to or receive output from Chaindesk agent and datastore actions within the same scheduled agent task.