An autonomous agent pipeline that turns a folder of trucking company CSVs into a complete sales demo — insights report, company profile, and a working ops platform — with no human steps between input and output.
Carrier Demo Factory is an autonomous agent pipeline that turns a folder of trucking company CSVs into a complete, deployed sales demo — with no human steps between input and output. One command triggers a five-phase Claude Code pipeline that generates synthetic operational data, analyzes it like a consultant, produces three polished deliverables, deploys them to a live VPS, and updates the portfolio page automatically.
The project was built to solve a real sales problem: how do you walk into a cold meeting with a trucking company and immediately demonstrate that you understand their business better than they do? The answer is to show up with a working product already built for them — not a deck, not a mockup, but a live platform running on their own numbers. Every demo is unique because every dataset tells a different story.
companies.json state file updated by deploy scripts so the portfolio page never requires a manual editcurrent_state.txt as a simple checkpoint so a failed phase can be retried without rerunning the entire pipelinerun-local.sh and run-cycle.sh scripts with identical logic, only differing in whether deploy steps are executedA self-contained HTML file with Plotly charts covering every major pain point. No login required — built to be emailed to the prospect, their accountant, or their bank before the pitch even begins.
A public-facing profile that tells the company's story, surfaces key stats, and frames pain points in plain language. Designed to create recognition — the prospect feels understood before they see a product.
A multi-page Streamlit application deployed over HTTPS and running on the prospect's actual data. Not a mockup — a working product accessible from any device before the sales call starts.
Claude writes its own Python analysis scripts and follows threads in the data independently. Every demo surfaces different pain points because every dataset is different — nothing is hard-coded.
The agent registers systemd services, writes Caddy reverse proxy routes, serves everything over HTTPS, and updates the portfolio JSON — no manual infrastructure steps after the pipeline runs.
The pipeline generates demos for owner-operators, small fleets, regional carriers, and mid-size carriers — each with carrier-appropriate data volumes, pain patterns, and platform feature sets.
The pipeline is driven by a bash state machine (run-cycle.sh) that advances through five phases, invoking Claude Code with a scoped prompt at each step:
company_profile.mddeploy-app.sh to register and start itEach phase reads outputs from the previous phase and writes its own outputs to a company directory. The state machine tracks the current phase in current_state.txt — if a phase fails, the pipeline restarts from that phase, not from the beginning.