This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
Sustainability WorkBench
Self-hosted AI workbench for drafting ESG reports
Self-hosted AI drafting workbench for sustainability (ESG) reports — SSE and HKEX rule sets. MIT. - Dexter-Yao/sustainability-workbench
I spent a while building ESG reporting tooling, and the same thing kept happening: people would paste their internal policies, ledgers and certificates into a chatbot, get back something fluent, and have no way to tell which parts were grounded in their own documents and which the model had invented.
Two things in here are my attempt at fixing that.
First, the agents that read your files never write report prose. One agent reads each document into a summary; another decides which materials a given chapter may draw on. The actual drafting happens separately, block by block, and each block only sees the facts assigned to it. So "don't make things up" is a structural property, not a line in a prompt.
Second, nothing ships until a deterministic gate says so — plain code, no model, checking completeness and consistency against the disclosure standard. Same report, same verdict, every time.
It runs entirely on your own machine: Postgres and auth in local Docker, your files on your own disk, and you bring your own model key (Azure OpenAI by default, but any OpenAI-compatible endpoint works — including a local Ollama).
Three rule sets ship today: Shanghai Stock Exchange, and HKEX in both Traditional Chinese and English. There's a fully synthetic corpus in the repo, so you can run the whole pipeline end to end — all the way to a rendered Word file — without a model call or any data of your own.
MIT, no paid tier, no feature gates. It drafts; it does not assure — what
comes out is a draft for professional review.
Happy to go into the architecture if anyone's curious, especially the
provenance panel — every generated paragraph can tell you which file and
which of your answers it drew on.
About Sustainability WorkBench on Product Hunt
“Self-hosted AI workbench for drafting ESG reports”
Sustainability WorkBench was submitted on Product Hunt and earned 1 upvotes and 1 comments, placing #158 on the daily leaderboard. Self-hosted AI drafting workbench for sustainability (ESG) reports — SSE and HKEX rule sets. MIT. - Dexter-Yao/sustainability-workbench
On the analytics side, Sustainability WorkBench competes within GitHub, Climate Tech and Accounting — topics that collectively have 56.4k followers on Product Hunt. The dashboard above tracks how Sustainability WorkBench performed against the three products that launched closest to it on the same day.
Who hunted Sustainability WorkBench?
Sustainability WorkBench was hunted by Dexter. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Sustainability WorkBench including community comment highlights and product details, visit the product overview.