Skip to content

Case study

AI Fitness Coaching Application

An AI coaching PWA with Supabase authentication and data, Groq inference using Llama 3.3 70B, and private network deployment.

Strength training equipment in a gym
Industry
Fitness and Sports
Project type
AI assistant and PWA
Technology
PWA · Supabase · Groq · Llama 3.3 70B
Integrations
Supabase authentication and data · Groq LLM inference

Photography shows industry context, not the client’s premises or a product screenshot.

The context

The business behind the brief

The coaching application is a delivered AI coaching PWA for fitness. It combines an application interface with Supabase authentication and data.

Groq provides LLM inference with Llama 3.3 70B. The private network deployment of the application is distinct from the location of model inference.

The challenge

What the system needed to solve

  • 01

    Connect a coaching PWA to authenticated user data and Groq model inference.

  • 02

    Keep private network application deployment distinct from the external inference request.

  • 03

    Present the Llama 3.3 70B coaching capability through a usable browser application.

The solution

From requirements to working software

Delivered functionality and implementation boundaries.

01

Enter through the PWA

The progressive web application is the user's entry point to AI coaching. It brings browser-based interaction together with the authentication, data and inference components. PWA delivery describes the application surface; model inference is a separate part of the request flow.

02

Connect authentication and data

Supabase supplies authentication and application data. Identity and the information used in a coaching interaction must be considered together: knowing who the user is and deciding which information an interaction may use are related engineering responsibilities.

03

Request model inference

The coaching integration uses Groq and Llama 3.3 70B. The application presents the interaction while the model produces language output. A model response is assistance within the coaching product, not a guarantee of a training outcome or a substitute for medical care.

04

Make the network boundary clear

The application is deployed on a private network, while Groq supplies external LLM inference. Application access and model processing therefore have different boundaries. The private application boundary controls access; the inference boundary determines what information leaves the application for model processing.

Strength training equipment in a gym

Core functionality

What the product lets people do

  • AI coaching PWA
  • Supabase authentication and data
  • Groq LLM coaching
  • Llama 3.3 70B
  • Private network deployment

Technical implementation

Architecture and system boundaries

The PWA is the coaching interface. Supabase provides identity and data, and Groq serves Llama 3.3 70B inference. The application was deployed on a private network; inference is an external service call. Private application access does not mean that model processing occurs on the same network.

Documented technology

PWA · Supabase · Groq · Llama 3.3 70B

Business impact

The delivered outcome

  • Users have a PWA coaching interface
  • Supabase supports authentication and data
  • Groq powers the LLM coaching interaction
  • The application is deployed within a private network

Let’s talk about the system you need.

Tell us where the current process breaks down. We can map the workflow, examine the integration requirements and define a practical scope for the build.

  • A mutual NDA is signed before project details are shared.
  • Scope is fixed in writing before any build starts.
  • You own the source code, the IP and the documentation at delivery.
  • Support plans are available and tailored on a discovery or technical call.