Case study — DTC beverage brand
A decision platform for a DTC beverage brand
This case study walks through one real engagement: a platform Anneal built and runs end to end for a DTC beverage brand, automating customer service, delivery exceptions, fulfilment and marketing-spend decisions in production.
The brief
A small eCommerce business, run by a group of friends who all had full-time jobs, was struggling to keep up with a growing ready-to-drink (RTD) beverage brand. Nobody had a complete view of the day-to-day business, every task meant logging into several systems, and decisions were often made reactively. The platform below is what got built: agentic workflows, a marketing-mix model, operational and inventory monitoring, and the engineering discipline (testing, incident response, evaluation) that keeps it trustworthy in production.
Analytical use cases
The Solver
Try the interactive spend-scenario planner — built on the real fitted marketing-mix model.
Agentic workflows
Customer service email
Refunds and returns, classified and resolved automatically — with an honest look at what happens when confidence is low.
Read the replay → 02Delivery exception auto-resolution
Shipping problems detected, customers notified, reshipments created — no human touch on the happy path.
Read the replay → 03Sample-pack zero-touch fulfilment
From nineteen identical manual requests to a fully automated intake-to-invoice flow.
Read the replay → 04Self-modifying playbook
A plain-language Telegram message becomes a committed, deployed policy change — or a filed issue if it needs real code.
Read the replay → 05Evaluation framework
How the agent's behaviour gets tested before it ever reaches a customer.
Read the replay →Stack
- Python
- FastAPI
- SQLAlchemy
- Alembic
- PostgreSQL
- Streamlit
- PyMC
- APScheduler
- Anthropic Claude
- JupyterLab
- pytest
- Caddy
- GitHub Actions