Extend your product
with AI capabilities
Analyze documents, find products through conversation, or generate reports automatically. AI agents extend the workflows customers already use. Connic connects them to the existing application and handles their operation.
Read the quickstartConnectors
What can agents do for your customers?
From questions about documents to finding suitable products and preparing weekly reports, agents can support workflows that otherwise require customers to find and assemble information themselves.
Customers upload documents and get information they can work with in the application. An agent processes the content, which becomes available for later questions once indexed. The application receives structured results for further processing.
Customers can describe their requirements in their own words and ask follow-up questions about the results. The agent uses connected product information to find relevant items and explain its suggestions.
On a schedule, an agent retrieves data from the connected database, summarizes it, and emails the report. The team receives regular updates without compiling the information by hand each week.
Fit agents into your application’s workflows
A request, file upload, or schedule starts processing. Your application can receive the result directly in the response or through a configured connection once processing finishes. The integration supports both quick tasks and longer-running work.
- LLM integration: SDK setup, error handling, retries
- Tool execution: Framework code, schema definitions
- Deployment: Docker, Kubernetes, CI/CD pipelines
- Scaling: Auto-scaling config, load balancing
- Observability: Logging, tracing, custom dashboards
- Retrieval: Vector store, embeddings, chunking
Browse the connectors or jump straight into the quickstart.
Extend your product without building an agent platform
Use your existing team’s experience to create new AI capabilities. Connic provides deployment, scaling, and execution visibility without requiring you to build that infrastructure yourself.
Write a YAML config, add a few Python tools, push to Git. That's the deploy. No Docker, no Kubernetes, no platform team standing up clusters.
Teams that write Python and use Git can ship AI features without adding ML or DevOps headcount.
Every run is captured as a full trace: LLM reasoning, tool calls, timing, and cost. When something goes wrong, the trace exposes the root cause in seconds.
Agents live in the repository. Changes go through PRs, deployments ship on push, and rollbacks take one click. The workflow matches the rest of the team's code.
Variables are scoped per environment. Sensitive values stay masked in dashboards and logs. Every action lands in the project audit log.
Runs execute on managed infrastructure that scales with traffic, so idle clusters incur no cost. Spend alerts and hard limits per agent or environment prevent surprise bills.
Ready for production
The security and budget controls your security and finance teams need before AI capabilities reach customers.
Every connector ships with authentication and encryption. Webhook connectors verify a shared secret on each request, and the Bridge connector reaches services in the VPC over an outbound tunnel without opening inbound ports.
Cost alerts and hard spending limits per agent or environment help keep your AI capabilities within budget. Connic detects unusually expensive runs and notifies the team.
Traces make your agents’ executions traceable. Enterprise offers a DPA, EU AI Act readiness support, and access to the SOC 2 Type II report. These materials support review by legal and security teams.