Pythia Store | Retail Managers Smarter Decisions (Without Being in the Store)

Retail leaders today face three big challenges:

1. Visibility: managers can’t be in every store at the same time.
2. Noise vs. insight: it’s hard to know which customer interactions really matter.
3. Actionability: even if you gather data, turning it into decisions is another story.

AI-powered retail tools promise to help. But integrating hardware, cloud systems, and models into something reliable is a tall order, especially for early-stage startups.

Pythia Store set out with a bold vision: help managers make smarter decisions by literally listening to what’s happening in stores. They collaborated with us at Milo to bring that vision to life. Together, we designed and built:

• Embedded software to capture in-store audio in real time.
• Secure cloud pipelines to transmit and process recordings.
• AI models (Whisper and Gemma 3:27b) to analyze conversations and generate tailored recommendations.
• A clean, responsive web app for browsing insights from any device.
• Automated communications, integrated with Resend and Klaviyo, to deliver recommendations straight to managers’ inboxes.

The result? A first-of-its-kind system that helps managers react faster, work smarter, and lead better, even when they’re not on site.

As one of our engineers noted:

The challenges involved reliable audio capture in a noisy store, including microphone calibration, noise filtering, and robust voice activity detection (VAD) without losing quiet segments. Low latency and proper segmentation had to be ensured so that context wouldn’t break between packets. An unstable Wi-Fi/LTE connection could require offline buffering, upload resumption, and flow control. Power loss resilience demanded atomic writes, fsync, and emergency logs. The limited resources of the Raspberry Pi required zero-copy streaming, lightweight Python processes, CPU/IO limits, and on-the-fly compression. OTA updates had to be secure and reversible, with remote configuration. Observability was based on heartbeats, health/temperature metrics, and clear logs for remote diagnostics. Hardware diversity in later phases of the project also had to be taken into account.

Maciej Tulikowski, Software Engineer at Milo

Like many early-stage startups, Pythia faced hurdles:

• AI models needed fine-tuning to deliver actionable insights, not just text.
• Audio had to be transmitted securely, without slowing down devices.
• The scope evolved quickly as new ideas emerged.
• Everything had to be built on a lean budget.

We solved this by:

• running targeted tests to improve AI output,
• building lightweight embedded software,
• delivering a lean MVP with flexibility for future growth,
• working side by side with the founder to prioritize impact over “nice-to-haves”.

From Pythia’s journey, there are three key takeaways for founders:

1. AI alone isn’t the product — it needs the right ecosystem (hardware, integrations, UX) to create value.
2. MVP discipline is critical — ambitious visions can succeed when you prioritize what matters first.
3. Partnership beats outsourcing — working closely with the founder made it possible to adapt to changes without losing focus.

This project reflects what we believe at Milo: technology should feel invisible. The managers using Pythia Store don’t care about AI models or cloud pipelines it runs on — what’s important to them is opening an email with insights that actually help them run their store better. That’s the kind of practical innovation we love building.

Retail is noisy. AI won’t replace store managers, but it can make their jobs smarter and easier by turning everyday interactions at the counter into decisions that truly impact the business.

If you’re a founder building at the edge of AI, embedded systems, or retail tech, Milo can help you design and develop a process that is practical, scalable, and fit for real-world use.

👉 Let’s talk about how to bring your vision to life.