Give your agents the experience of your entire service organization.
Generic agents know equipment in general. Connect them to Aiventic and they reason from your own repairs: what failed, what held, what to bring, and who should go.
{
"mcpServers": {
"aiventic": {
"url": "https://mcp.aiventic.ai",
"auth": "oauth"
}
}
}
// Then ask your agent:
// "What have we learned from Wolf DF48650G ignition failures?"The protocol isn’t the difference. The service experience you reach through it is.
Three ways in. Same tools, same answers.
01
MCP server
Point any MCP client at Aiventic and your agent can call service tools by name. Works with ChatGPT, Claude, and any agent framework that speaks MCP.
02
REST API
The same tools over plain HTTPS and JSON, for your own apps, portals, IVR flows, and back-office automations.
03
Agent integrations
Ready connections for the AI tools your team already uses, plus the agents inside your FSM.
Service tools, not data dumps. What your agents can call.
aiventic.find_similar_repairs()
Past repairs on the same model and symptom, and which fixes held.
aiventic.get_failure_patterns()
What tends to fail on a model, and when.
aiventic.predict_required_parts()
Parts a job is likely to need, before dispatch.
aiventic.prepare_service_call()
Likely cause, parts, questions to ask, and the best tech for a job.
aiventic.recommend_diagnostic_path()
The next diagnostic step, ranked by what has worked before.
aiventic.assess_first_visit_resolution()
How likely this job is to be fixed on the first trip.
aiventic.identify_repeat_failure()
Whether a unit is failing the same way again.
aiventic.compare_repair_outcomes()
Which repair paths actually hold up for a given problem.
aiventic.get_model_failure_history()
Everything your company has seen on one model.
aiventic.find_technician_experience()
Who has fixed this before, and how it went.
aiventic.recommend_next_service_action()
What should happen next on a job, with the evidence.
aiventic.search_manual()
Cited answers from manuals, bulletins, and SOPs.
Live, right now. Agents calling your service experience.
Each call is answered from Service Memory, Service Knowledge, and Service Reasoning, the same layers behind the Aiventic app.
How the platform worksAuthentication and security. Built for production, not demos.
Authentication
OAuth for MCP clients and scoped API keys for server-to-server calls.
Permissions
Agents see only what the connected user or workspace is allowed to see.
Audit trail
Every tool call is logged with the agent, the user, the inputs, and the answer.
Your data stays yours
Your service history isn’t used to train models for anyone else.
Certified
SOC 2 Type 2, ISO 27001, and HIPAA.
Evidence on every answer
Responses cite the jobs, manual pages, and bulletins they came from.
Questions. Straight answers.
Which agents can connect?
Any MCP client, including ChatGPT and Claude, plus anything that can make an HTTPS request to the REST API. FSM agents connect the same way.
Why not just give an agent our FSM data directly?
Raw records tell an agent what happened. Aiventic tells it what that history means for this job: which fixes held, which part to bring, and who has done it before.
How is it priced?
By usage. Each MCP tool call or API request is metered, so you pay for what your agents use, and you don’t need a seat plan. Contact us for current rates.
Do I need a developer to set it up?
Not for ChatGPT or Claude: add the Aiventic connector and sign in. Custom agents and API work need someone comfortable with HTTPS and JSON.
Give every agent your best tech’s experience.
Tell us what you’re building. We’ll get you connected.