Problem: Elasticsearch is powerful but complex
When a company accumulates terabytes of logs and the team needs to quickly find anomalies or answer data-related questions, Elasticsearch becomes an indispensable tool. But configuring it, writing queries, and maintaining integrations take hours of developer time. It's especially painful when you need to connect Elasticsearch to internal systems: CRM, marketing platforms, or even simple CSV reports.
At ASI Biont, we encountered this pain when one of our clients — a fintech startup with hundreds of microservices — asked us to set up real-time error monitoring. Their team spent up to 15 hours a week manually analyzing logs through Kibana (Elasticsearch's interface). Most queries were repetitive: "show all 500 errors for the last hour," "find queries with delays over 2 seconds," "group errors by service." Developers wrote the same DSL queries over and over, and analysts couldn't use Elasticsearch without developers' help.
The solution was to connect ASI Biont's AI agent to Elasticsearch. It's not just a chatbot, but a full-fledged integration layer that automates search, analytics, and even report generation through the Elasticsearch API.
What ASI Biont integration with Elasticsearch brings
1. Natural language search
Instead of writing DSL queries (essentially JSON structures with filters, aggregations, and sorting), users simply type in chat: "Show the last 50 500 errors from the payments service from 2:00 PM to 3:00 PM." The AI agent itself constructs a properly structured request to Elasticsearch, executes it, and returns the result in a clear format — a table, list, or chart.
This is especially useful for non-technical staff: product managers, analysts, support teams. They no longer need to learn Query DSL syntax or understand aggregations.
2. Automation of routine reports
Boring routine — every morning preparing a report on error counts, average response time, top slow queries. The AI agent does this on its own. Once you describe the task: "Every day at 9:00 AM send a summary of errors for the day, grouped by service, and compare it with the previous day." The agent will create a scheduled query via the Elasticsearch API, collect results, and send them to Telegram, Slack, or email.
3. Integration with other services
The most interesting part begins when Elasticsearch works not alone. For example, ASI Biont can receive data from your CRM, enrich it with data from Elasticsearch (say, determine what errors customers encountered) and pass it to the analytics system. All this is done via API — the AI itself writes the integration code that connects services.
How to connect: no buttons, just dialogue
Most integration platforms (Zapier, Make, Tray) have ready-made modules for Elasticsearch. But what if you have a non-standard scenario? Or need a special data format? Usually you have to write custom code. With ASI Biont, you don't need to.
The entire setup happens through chat. You tell the agent: "Connect Elasticsearch using the API key," and pass the key from your cluster. The AI independently reads the Elasticsearch REST API documentation (at https://www.elastic.co/docs/api/doc/elasticsearch/), determines endpoints, authentication, and writes integration code right in the dialogue.
No control panels, no "add integration" buttons — just natural language. You can ask the agent to rewrite query logic, add filters, or change the response format — it will do it instantly.
Real case: how we saved 80 hours a month
Client — a fintech company with 30 microservices in Kubernetes. Logs are collected in Elasticsearch, index volume about 500 GB per day. Problems: increased API response time, mass errors during deployments, difficulty finding specific transactions.
Before: developers wrote DSL queries in Kibana and manually shared links to charts in chat. Analysts asked to "look at logs" — it took 30 to 60 minutes per request due to queue.
With ASI Biont:
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Finding incidents in minutes. Support team writes in the common chat: "Find all requests with 503 error for the last 30 minutes for the api-gateway service." The agent forms a request in seconds, receives data, and outputs a list with IP, time, error body. Before, this would take an on-duty engineer 15-20 minutes.
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Anomaly notifications. The AI agent is configured to monitor metrics (via Elasticsearch) and alert in Slack when the number of 5xx errors exceeds a threshold. Moreover, the agent doesn't just monitor, it analyzes trends: if errors suddenly double, it first looks for possible causes in the logs (e.g., recent deployment) and sends a hypothesis to the chat.
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Report generation for management. Every Monday, the agent collects data on service availability (from synthetic monitoring indexes), builds a chart, and sends it to the managers' Telegram channel. Previously, a DevOps engineer spent half a day on this.
Result: the project saved about 80 person-hours per month (5 people × 16 hours) and reduced average incident resolution time from 40 minutes to 7-10 minutes.
Specific use cases
Here are a few more examples of how you can use the integration right now:
| Scenario | How it works with ASI Biont | Result |
|---|---|---|
| Security log analysis | The AI agent, on request "find all authorization attempts from IPs that appear on the blocked list," filters by client_ip and threat.intel fields |
SOC analyst saves up to 3 hours a day |
| E-commerce monitoring | The agent monitors the number of errors during checkout, groups by city, reason (product not found, payment declined), and sends a summary | Quickly identifying issues with payment gateways |
| Log search for customer support | Support operator writes: "Show all actions of user john@example.com today" — the agent collects data from all indexes, sorts by time, and provides a timeline | Customer Support response becomes 5 times faster |
Why it's beneficial
- Saves developer time. No need to write queries manually — AI does it in seconds.
- Data accessible to everyone. An analyst can get data from Elasticsearch on their own without distracting a programmer.
- Fast implementation. Integration is set up in 5-10 minutes of talking to the agent, instead of 3-4 days of writing a microservice.
- Flexibility. ASI Biont connects to ANY service via API — whether it's Elasticsearch, Stripe, Salesforce, or even your company's own REST API. You don't have to wait for platform developers to add the needed integration. Just pass the API key in chat — and the AI will write the connection code itself by studying the service's documentation.
Conclusion
Elasticsearch is a powerful search engine, but its power often remains locked behind complex queries. ASI Biont removes this barrier by letting you talk to data in natural language and automate routine tasks. You save money because you don't hire an extra specialist to work with Elasticsearch, and you speed up processes that used to take hours.
Want to see it in action? Visit asibiont.com, create an agent, and connect your Elasticsearch via API key in chat. Try it right now — and your team will forget about complex JSON queries.
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