Common requests are known.
Questions, approved answers and required details can be derived from real calls.
A specific AI production use case
The assistant answers inbound calls, handles repeat questions from your approved content, captures the request and contact details, and passes everything else to your team as structured data. No black box: answers, costs and handovers remain traceable.
A clearly bounded fixed-price package: live within four weeks from the day the number, content and target system are ready.
Remote from Germany. Straight with me, no agency in between.
The starting point
Many calls concern the same things: opening hours, status, responsibility or routing a request. They take time even though the answer is already documented. At the same time, an assistant must not guess when its knowledge base has no answer.
That takes more than a voice attached to a model. The important parts are a bounded knowledge base, clear escalation to a person, structured handover into existing workflows and operations where silent failures are noticed.
Typical starting pointWho this is for
For service, sales and operations owners with regular inbound call volume. Many requests repeat, information exists and a clean handoff to people matters more than endless artificial conversation.
Questions, approved answers and required details can be derived from real calls.
Leads, appointments or service cases are lost or create callback queues for the team.
Limits, escalation and handoff are part of the process; the assistant supports the team.
What I do
We define which requests the assistant handles, what it captures and when it hands over to a person. What it does not know, it does not invent.
Answers come from an approved knowledge base rather than the model’s general knowledge. Content can be updated without rebuilding the whole assistant.
The request, urgency and contact details are captured as structured data and passed to CRM, ticketing or email with a transcript.
The number, language model and target system are connected. Cost per call is measured, failures trigger alerts and conversations follow the agreed data-protection framework.
Which calls should the assistant handle?
In 30 minutes we establish whether the use case fits the package.
How it runs
The scope stays clear: one assistant, one target system and defined conversation paths.
Repeat questions, handover rules, language and target system are agreed.
Approved content, conversation flow and boundaries are configured and tested.
Telephony, target system, transcripts and cost measurement are connected.
Tests with realistic conversations, a walkthrough and a controlled production launch.
Entry offer
one-off, no monthly cost from me
The assistant answers calls, handles what repeats, qualifies the rest and files it where your team already works.
What you get
What you do not get
The outcome
Repeat enquiries are accepted and answered around the clock.
Your team receives the request, urgency and conversation rather than only a number to call back.
Configuration, prompts, knowledge base and integration sit with you rather than a reseller’s account.
Technologies I use
Running it myself
The assistant I offer here is one I run myself. Call +49 (2451) 6123001 and fail to reach me, and you do not land in voicemail. The assistant picks up, says at the start that you are speaking to an AI system, answers questions about my work from the content of this website, takes down what you need and your contact details, and offers an appointment if you want one. It has worked that way since June 2026.
It is the fallback, not the front line: if I can pick up, I pick up. That split is the right one for me, because a call here is usually meant to become a conversation. Where the same twenty questions arrive every day the line sits somewhere else, and where it sits in your case is something we decide together.
My own version runs on Twilio, a server at Hetzner and two language models from Google and Mistral, with all processing inside the EU. Yours would look different if everything already sits on AWS. There is deliberately no metric here: I have none I could substantiate. You can still check the claim, by calling.

Who you are talking to
I am Tim Rutte. More than 20 years in software development. Today I take AI systems into controlled production. You talk to the person who touches your code, from the first call to the handover.
Common questions
No. It handles repeat requests and collects context. As soon as a conversation falls outside the agreed scope, it hands over to a person.
A bounded knowledge base, clear answer rules and handover on uncertainty reduce the risk. No language model can guarantee zero errors, which is why boundaries and transcripts are part of the system.
That depends on your phone system. Cloud telephony can usually connect directly; with an on-premise system we establish whether forwarding or a new number makes more sense.
The language model and telephony are billed by usage directly by their providers. Cost per call is measured and made transparent before launch.
On request, entirely inside your infrastructure and accounts. Location, retention and access rights are agreed before the first production call.
Other services
The infrastructure behind AI systems that actually ship: MCP servers, controlled tool access, LLM integration with real permissions and cost control.
Learn moreReplacing manual workflows with real systems: wired into the software you already run, with permissions and an audit log instead of a chain of tools.
Learn moreFrom the incoming document to the posted entry without retyping: read XRechnung and ZUGFeRD, match against the purchase order, route the approval, hand over to your ERP, with a full trail.
Learn more