Thesis: Design and evaluation of a voice AI agent for 24/7 structured support intake

High Level Description

Customer Success and Support teams are expected to be reachable around the clock, but staffing phone lines 24/7 is expensive and

often unrealistic. Outside business hours, customers are typically met by voicemail or a web form, which leads to incomplete

information, delayed responses and missed requests.

Recent advances in speech-to-text, text-to-speech and large language models make it possible to build voice agents that can answer

inbound calls, hold a structured conversation, collect the information needed to handle an issue, and hand it over to the support

team in a consistent format. This thesis aims to design, implement and evaluate a voice AI agent for structured support intake,

and to investigate how reliable and consistent this approach is compared to today’s alternatives.

Project Description

In this thesis, design and implement a voice AI agent that receives inbound customer phone calls in Swedish or English, guides the

caller through a structured intake dialogue, handles follow-up questions within a controlled scope, generates a structured summary

of the call, and delivers it into the existing support workflow (initially a shared mailbox).

The agent itself is a means, not the end. Building the prototype is required, but the contribution is the evaluation: how complete

and consistent is the collected data, how faithful are the summaries, and how well current speech pipelines perform for Swedish

and English support conversations. Compare design alternatives such as a scripted intake flow versus an LLM-driven flow with

structured output. Existing telephony, STT, LLM and TTS platforms/APIs should be used; building components from scratch is not

expected. The prototype will be evaluated with simulated calls. Scope is adapted to 15 credits (bachelor) or 30 credits (master).

Purpose

Bachelor students typically focus on one question; master students take a broader comparative evaluation.

1. How do different dialogue control strategies (scripted flow vs. LLM-driven with structured output) affect completeness and

consistency of collected intake data?

3. What transcription accuracy and latency do available STT/TTS/LLM pipelines achieve for Swedish and English support calls,

using the same intake flow in both languages?

4. What architecture and integration points are needed to fit such an agent into an existing support workflow?

An Exciting Journey with Knightec Group

Semcon and Knightec have joined forces as Knightec Group. Together, we are Northern Europe’s leading strategic partner in

product and digital service development. With a unique combination of cross-functional expertise and a holistic business

understanding, we help our clients realize their strategies – from idea to complete solution.

Practical Information

This is a thesis position, located at our office in Sundsvall. Start date January or March 2027.

Please submit your application as soon as possible, but no later than 2026-11-30. If you have any questions, you are welcome to

contact johanna.edstrom@knightec.se. Note that due to GDPR, we only accept applications through our careers page.