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AI & technology

Architecture that keeps the human in the loop.

The important architectural decision is that the AI does not need to control the conversation. It observes, understands and suggests; the Supporter decides.

Stack

Technology in the current prototype.

Real-time communication

LiveKit

The foundation for real-time video and audio communication between the client and the Supporter.

Speech to text

Deepgram

Explored for real-time transcription with speaker identification, producing the conversation context.

AI

Claude

Processes the conversation context and produces assistance for the Supporter.

Technical flow

From the conversation to the Supporter's screen.

Client audio / video LiveKit Supporter
LiveKit Speech–to–text Conversation context Claude Supporter AI interface

The decision chain

Conversation Transcription Context AI analysis Suggestion Human Supporter Human decision

The Supporter remains the final human actor. No AI output becomes a consequential action on its own.

Capabilities

What the AI is being explored for.

Contextual memory

Understanding what has already been discussed.

Conversation themes

Identifying recurring topics and concerns.

Reflection

Helping the Supporter think about what may be happening in the conversation.

Question generation

Suggesting questions that could help the Supporter explore an issue.

Psychological frameworks

Helping Supporters work within predefined frameworks and their training.

Session summaries

Producing structured post-session summaries.

Risk signals

Identifying information that may warrant additional attention.

AI design principles

AI should be useful without becoming the protagonist.

The client should not need to interact with the AI. The Supporter should not become dependent on it. The conversation should continue even if the AI is unavailable.

Prompts & context

AI documentation.

A working area for the prompt engineering behind the Supporter assistant — kept alongside the product rather than buried in a repository.

System prompts

Current production and development prompts.

Context construction

How conversation history is provided to the model.

Output formats

The expected structure of AI responses.

Prompt experiments

Different approaches tested, and what each changed.

Evaluation

How AI outputs are judged for usefulness and accuracy.

Failure cases

Irrelevant suggestions, hallucinations, incorrect interpretations, excessive intervention, missed context.

In progress

Prompts and evaluation notes are being written up as the prototype stabilises. Failure cases are collected from the start — they are the most useful record the project can keep.