Elasticsearch AI employee
A Superkind AI employee that queries data, creates analyses, maintains documents, and checks cluster health in Elasticsearch. Message it in Teams or Outlook, it works in Elasticsearch and reports back with the result. It waits for your approval before sensitive actions.
- Hosted in the EU
- GDPR data processing agreement
- Ready to start today
- Works in Teams, Slack and more
What is a Elasticsearch AI employee?
An Elasticsearch AI employee is an AI that connects to your Elasticsearch account and completes work in it. It runs searches, creates aggregations, checks indices, and maintains documents. Superkind builds this AI employee with your company knowledge and delivers the finished result to wherever your team works.
Unlike workflow builders such as Zapier or Make, there is nothing to configure. You describe the outcome in plain English, the AI employee picks the right Elasticsearch actions, chains them with your other systems, and asks for your approval before sensitive changes.
Elasticsearch is a distributed search and analytics platform for structured and unstructured data in near real time.
You ask in Teams
Describe the data or analysis you need in plain English.
Superkind picks the actions
Selects the right Elasticsearch actions and connects them with your systems.
Superkind works in Elasticsearch
Searches indices, calculates aggregations, and prepares document changes.
Superkind reports back
Delivers the finished result to Teams or Outlook.
What can you ask Superkind to do in Elasticsearch?
Messages you would actually send. Copy one, swap in your specifics, and Superkind takes it from there.
“Ask Elasticsearch for every failed order from yesterday and deliver the results as a table.”
you, to @Superkind“Create an analysis of search latency by index and region for the last 30 days.”
you, to @Superkind“Correct the status of this order document in the orders index, but get my approval first.”
you, to @Superkind“Let me know here as soon as the error rate in our ingest pipelines exceeds five percent.”
you, to @SuperkindHow does Superkind work with Elasticsearch?
- Native integrations and connectors for 1,000+ tools
1Connect your systems
Connect Elasticsearch with the required access rights. Add Teams and the systems your data comes from. Superkind sets up the Elasticsearch connection and then reports in Teams.
@Superkind check the orders index for open orders with a mismatched payment status and prepare the correction.
On it. I am checking the documents in Elasticsearch and preparing the correction for approval.
2Tell Superkind what you need
Message your AI employee in Teams like a colleague. Ask for a table, aggregation, or check in Elasticsearch. Superkind understands the desired result and picks the right actions.
- Index orders checked
- 3 status mismatches found
- Correction documents prepared
Correction document order 8472Waiting for your approvalUpdate three order documents?3Superkind operates, you approve
Superkind runs the queries in Elasticsearch and delivers the result in Teams. Changes to documents or indices wait for your approval. Every step is logged.
What can Superkind do in Elasticsearch?
Ask in plain English from Teams or Outlook. Superkind picks the right Elasticsearch actions, completes the work, and reports back. No workflows to build.
Run search query
Searches one or more indices with the Elasticsearch Query DSL.
Filter documents
Filters documents by fields, time ranges, terms, or value ranges.
Get single document
Retrieves a document by its index and document ID.
Find similar documents
Finds similar documents with a More Like This query.
Analyse query profile
Uses the Profile API to analyse how Elasticsearch executes a search query.
Calculate aggregation
Calculates metric, bucket, or pipeline aggregations across documents in an index.
Analyse time series
Groups timestamps with date histogram aggregations and compares time ranges.
Calculate percentiles
Calculates percentiles for latency, revenue, or other numeric fields.
Run ESQL query
Analyses Elasticsearch data with ES|QL and returns tabular results.
Get document
Reads the source, version, and metadata of an Elasticsearch document.
Index document
Indexes a new JSON document under a specified document ID.
Update document
Updates selected fields of an existing document through the Update API.
Delete document
Deletes a document by its index and document ID.
Bulk index documents
Writes multiple documents to an Elasticsearch index with the Bulk API.
List indices
Lists indices with health, document count, and storage size through the Cat API.
Get mapping
Shows field types and mapping properties for an index.
Check index statistics
Checks document counts, search load, indexing rate, and segment data for an index.
Switch alias
Moves an index alias atomically to another index.
Delete index
Deletes an Elasticsearch index with its documents and metadata.
Check cluster health
Checks the cluster health status and shard allocation.
List nodes
Lists cluster nodes with roles, utilisation, and Elasticsearch version.
Check running tasks
Shows running cluster tasks with action, start time, and duration.
Restore snapshot
Restores selected indices from a registered snapshot repository.
Simulate ingest pipeline
Tests documents against an ingest pipeline without indexing them.
Get roles
Lists Elasticsearch roles with cluster and index privileges.
Check privileges
Uses the Has Privileges API to check which actions a user may perform.
Superkind AI employees that work with Elasticsearch
Every role brings its expertise and uses Elasticsearch as one of its tools.
Companies working with Superkind
Frequently asked questions
Everything you need to know about your AI employee for Elasticsearch.
Yes. Superkind connects Elasticsearch through a managed connector. Once connected, your team can ask the AI employee from Teams or Outlook to search, analyse, and execute prepared changes. No custom workflow or code is required. Access remains limited to the agreed indices and actions.
An admin provides the Elasticsearch endpoint and secure credentials with the required roles. Together, we select the permitted indices, APIs, and actions. We then test read queries and the approval path for changes. Superkind configures the connector during onboarding, so your team can work directly from Teams or Outlook afterwards.
It can search documents with Query DSL or ES|QL, deliver tables, calculate aggregations, check mappings and index statistics, and assess cluster health. It can also index, update, or delete documents, switch aliases, and restore snapshots. Superkind only performs write operations and other sensitive actions after your approval.
No. With Zapier or Make, you build triggers, steps, and field mappings yourself and maintain them when something changes. With Superkind, you describe the result you need in plain English. The AI employee picks the right Elasticsearch actions, connects them with Teams, Outlook, Jira, or Linear when needed, and asks questions if anything is unclear.
Only with your approval. Superkind can run searches, analyses, and health checks independently. Deleting or changing documents and indices, switching aliases, and restoring a snapshot all wait for an explicit yes in Teams. Your team decides which actions are sensitive and who is allowed to approve them.
Superkind is hosted in the EU, and we sign a GDPR data processing agreement. The connector receives minimal roles and access only to the agreed indices and APIs. Model requests are not used for training. Every query, approval, and change is logged, so your team can trace the AI employee’s work at any time.
Self-managed Elasticsearch can be used free of charge, while Elastic Cloud can start in the low hundreds of euros per month depending on resources and tier. Zapier starts at about 20 euros per month and Make at about 10 euros, both plus setup and maintenance time. Superkind is priced per use case, a fraction of a full-time hire.