Analysis updated 2026-05-18
Let employees request purchases in plain language through chat tools instead of filling out forms.
Automatically approve, reject, or escalate a purchase request based on company spending policy.
Issue a single-use, vendor-locked virtual card the moment a purchase is approved.
Automatically match a purchase request, its receipt, and the resulting bank charge for reconciliation.
| jeremyefarr/po-pilot | 0xhassaan/nn-from-scratch | 3ks/embedoc | |
|---|---|---|---|
| Stars | 0 | 0 | — |
| Language | Python | Python | Python |
| Last pushed | — | — | 2023-06-08 |
| Maintenance | — | — | Dormant |
| Setup difficulty | hard | moderate | hard |
| Complexity | 5/5 | 4/5 | 1/5 |
| Audience | pm founder | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires DGX Spark on-premises hardware, Stripe and Plaid accounts, and a local LLM setup to run as designed.
PO Pilot is a system that aims to automate corporate purchasing from start to finish. Instead of employees filing purchase requests through separate emails, chat messages, or forms, PO Pilot lets them ask for what they need in plain language through Slack, Microsoft Teams, email, or a dashboard, and one automated agent handles all of it. It is built on top of Hermes Agent, a project from Nous Research, and was made for a contest built around that framework. The system checks every request against a company's purchasing rules before any money moves, so approvals and rejections happen automatically based on policy rather than manual review. When a purchase is approved, it issues a Stripe virtual card that is locked to a specific vendor, capped at a set spending limit, and usable only once. After the purchase, PO Pilot connects to a company's bank data through Plaid to match the original request, the receipt, and the actual bank charge down to the penny, automating the reconciliation work that would otherwise be done by hand. Behind the scenes it combines a language model, reasoning through Hermes Agent, with a sandboxed component called NemoClaw that enforces the purchasing policy at the moment a request is made. It is designed to run on NVIDIA DGX Spark hardware on premises, so company data does not leave the building. The backend is built with Django and Celery, the dashboard with Next.js, and Playwright automates parts of the checkout process in the browser. The README includes a demo file that plays through three example requests at once: one small purchase gets automatically approved and a card is issued, one falls into a blocked spending category and is rejected outright, and one exceeds a spending threshold and gets escalated to a manager based on the company's reporting structure. Together these show how the system is meant to route different kinds of purchase requests without a human needing to process each one manually.
An automated system that takes purchase requests from Slack, Teams, email, or a dashboard, checks them against policy, and issues Stripe virtual cards.
Mainly Python. The stack also includes Python, Django, Next.js.
The README does not state license terms.
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly pm founder.
This repo across BitVibe Labs
Verify against the repo before relying on details.