Software has eaten the artifacts of work. Code, tickets, docs, transactions: all of it now lives in systems that models can read. And so the models have started to do the work the artifacts described: write the code, file the tickets, draft the docs. By every reasonable extrapolation, autonomous companies should already be here.
They aren't. It's worth asking why.
The artifacts are the residue, not the work
A pull request is the trace of a decision someone made in a hallway. A Jira ticket is the compressed output of a thirty-minute call where four people argued about priorities. A roadmap doc is the calm, post-hoc narration of something messier. The artifact records the conclusion. The work, the actual exchange of context, the negotiation of intent, the moment a decision is made, happens earlier, in conversation.
Models can't learn what they can't see. Train them on the residue and they get very good at producing more residue. Train them on the work itself and you start to get something that understands how a company actually operates.
The dataset that evaporates
Every company has a private corpus that nobody is collecting. It's the standup where a launch slips by a week. The 1:1 where a strategy quietly pivots. The customer call that reframes the entire product roadmap. The slack thread that resolves an architectural argument in nine messages. The hallway sync that decides who owns what.
Together, these form the dataset that would let a model understand a specific company's priorities, trade-offs, taste, and institutional memory. And right now, almost all of it evaporates the moment the meeting ends. What survives (the followup email, the doc, the ticket) is a lossy summary written for humans, by humans, after the fact.
The bottleneck isn't capability
It's tempting to assume autonomy is gated on smarter models. It isn't, or not only. Real-time transcription is solved. Long-context models can hold weeks of conversation. Retrieval over conversational data works. The bottleneck is upstream of the model: capture, structure, and access. The data has to exist in a form the model can actually use, and today it mostly doesn't.
What we're building
Harmony is the interface to that dataset. We sit on top of the conversational substrate of a company (its meetings, calls, threads, the connective tissue between them) and turn it into something an autonomous workforce can read, reason over, and act on.
Not a notetaker. Notetakers make summaries for humans. We're building the substrate itself: a structured, queryable, continuously-updated record of how a company actually operates, written in a form models can use as context.
The companies that capture this win
Autonomy doesn't arrive in your inbox. It arrives in the companies that did the work to make their own operations legible to a model. The ones that captured the dataset early get a compounding advantage: every meeting becomes training data for their own AI workforce, every decision becomes context the next agent inherits.
The missing dataset has been missing because nobody was building for it. That's changing.