RecordioAIApp

Inspiration:

AI agents are increasingly being used to handle conversations with customers, but an important problem remains: what happens when an AI agent says something that matters?

A customer may be promised a price, product, service, delivery, refund, or other commitment, yet there may be no reliable way to prove exactly what was said later. We wanted to build a trust layer that turns important AI conversations into verifiable evidence.

That idea became RecordioAI — “Prove What Your AI Promised.”

What We Built:

RecordioAI turns AI-agent conversations into structured Conversation Receipts.

Instead of treating a conversation as just a transcript, the system extracts important information such as:

Customer and conversation details
Products and prices
Fees and quantities
Promises and commitments
Potential discrepancies
Verifiable evidence from the conversation

The result is a structured receipt that can be reviewed when a customer or business needs to verify what actually happened.

How We Built It:

We designed RecordioAI as a web application that connects conversation data from supported AI voice-agent and telephony workflows.

The application processes conversation information, creates structured records, extracts relevant commitments and transaction details, and generates a cryptographic fingerprint for the resulting receipt. This gives each receipt an additional integrity layer and makes it possible to detect whether the recorded evidence has been altered.

The interface was designed around a simple workflow:

Conversation → Transcript → Extraction → Conversation Receipt → Verification → Resolution

We focused on making the system useful for freelancers, consultants, service businesses, sales teams, and eventually AI-agent platforms that need reliable evidence of what their agents communicated.

What We Learned:

Building RecordioAI taught us that AI reliability is not only about generating accurate responses. For real-world AI agents, accountability and evidence are equally important.

We learned how to structure unstructured conversation data into useful evidence, design verification workflows, work with speech-to-text and AI processing pipelines, and think about cryptographic integrity as part of a user-facing product rather than just a technical feature.

Challenges:

One of the biggest challenges was deciding what information from a conversation actually matters.

A raw transcript can be extremely long, while a useful receipt needs to surface the important details without losing the original evidence. We therefore focused on extracting concrete information such as products, prices, fees, quantities, and commitments while keeping the underlying conversation available for verification.

Another challenge was making the system feel simple despite the complexity happening underneath. The goal was not to build another complicated CRM, but a straightforward trust layer that answers one question:

«What did the AI actually promise?»

RecordioAI is our attempt to make AI conversations more accountable, verifiable, and useful in the real world.