Conversations as Data: My Path to Building a Call Analysis Assistant with Helix
When Call Records Meet Natural Language
Have you ever wished you could just ask your computer, "What did Frank and I discuss last Tuesday about the project timeline?" instead of digging through call logs and recordings? That's exactly the challenge I set out to solve—transforming rigid call data into conversational insights.
My Journey with Voice Data
My fascination with voice analysis began when I realized how much valuable information gets lost in the ocean of recorded calls. Customer preferences, project decisions, action items—all buried in hours of audio that nobody has time to revisit.
After exploring various approaches, I discovered the VCON (Voice Conversation) format—a structured way to represent calls with rich metadata about participants, timestamps, and dialog. This became the foundation for what followed.
Finding Helix: An AI Application Platform That Just Works
My previous attempts at building AI interfaces involved weeks of wrestling with frameworks, tokens, rate limits, and boilerplate code. Then I discovered Helix—an open-source platform that radically simplifies creating AI applications.
What struck me immediately was how Helix handled all the complex orchestration between:
My custom VCON server (where call data lives)
Language models (for understanding natural queries)
User interfaces (for responsive interaction)
Instead of writing hundreds of lines of code, I defined my entire application in a YAML configuration that told Helix how to connect these components.
Building the Call Analysis Assistant
I began by spinning up a local Helix installation using Docker—a process that took minutes rather than days. Next came the VCON server, a simple Go application that served structured call data.
The magic moment came when I connected these components in Helix's UI:apiVersion: app.aispec.org/v1alpha1
kind: AIApp
metadata:
name: telco-bot
spec:
assistants:
- provider: togetherai
model: mistralai/Mistral-7B-Instruct-v0.2
apis:
- name: VCON Search API
url: "http://host.docker.internal:8005"
With this configuration, I had created a bridge between natural language and my call database. I could now simply ask:
"Show me calls between Frank and Obi"
"What was discussed about the project timeline?"
"When did Frank mention milestones?"
The Technical Challenges I Overcame
Building this system wasn't without hurdles. Some key challenges included:
1. Docker-to-Host Communication
Getting Helix (running in Docker) to communicate with my locally running VCON server required using host.docker.internal instead of localhost in the configuration.
2. Network Interface Binding
My initial VCON server only listened on localhost, making it invisible to Docker containers. Switching to listen on 0.0.0.0 solved this issue.
3. Rate Limit Management
Together AI's rate limits (60 requests per minute) meant I needed to pace my testing to avoid 429 errors.
4. Crafting Effective System Prompts
The quality of responses dramatically improved when I provided clear instructions in the system prompt:
system_prompt: |
1. Present call information in a clear, structured format
2. Include relevant details like participants, timestamps, and call subjects
3. If multiple calls are found, summarize them chronologically
What I Learned About AI Application Development
This project taught me several key lessons:
Declarative beats imperative for AI apps
Defining what I wanted (in YAML) rather than how to achieve it (in code) made development faster and maintenance easier.
APIs are the glue between AI and data
The OpenAPI schema definition in Helix allowed the LLM to understand exactly how to query my data source without custom code.
System prompts are your secret weapon
Carefully crafted instructions dramatically improve how LLMs format and present information.
Local development simplifies iteration
Having everything running locally meant I could iterate rapidly without cloud deployment delays.
From Prototype to Production
What started as an experiment quickly evolved into a useful tool. The ability to query call data using natural language opened up new possibilities:
Customer service teams could quickly retrieve previous customer interactions
Sales teams could analyze successful calls for training
Project managers could extract action items from meetings
Compliance officers could verify what was discussed without listening to hours of audio
Why This Matters
In a world drowning in recorded conversations, the ability to interact with that data naturally is transformative. We're moving from "searching through calls" to "discussing with our call history."
The technical barrier to building such systems has traditionally been high—requiring expertise in speech recognition, natural language processing, dialog management, and more. Platforms like Helix are democratizing this technology, making it accessible to developers without specialized AI knowledge.
What's Next?
This project is just the beginning. I'm exploring:
Sentiment analysis of conversations
Automatic extraction of commitments and deadlines
Multi-modal interfaces that incorporate both text and audio
Integration with meeting platforms for real-time analysis
Final Thoughts
If you had told me a year ago that I could build a natural language interface to call data in a weekend, I wouldn't have believed you. The pace of innovation in AI tooling is making previously complex applications accessible to mainstream developers.
The most exciting part is imagining what others will build now that these capabilities are within reach. From healthcare to education, from customer service to team collaboration—natural language interfaces to structured data will transform how we work with information.
---
The complete source code for this project is available here, including detailed installation instructions and configuration examples.