Most software has historically been a database with a user interface. The user does the actual work — researching, comparing, deciding, remembering — and the software records the output.
The first generation of AI products mostly preserved this architecture and added a chat box. Instead of tracking wines, ask GPT which wine to buy. Instead of maintaining a travel spreadsheet, ask it where to stay. This is useful, but it is not much of a product idea.
Clay is interesting because it suggests a different architecture. Airtable gives you rows and columns and lets you fill in the cells. Clay lets you describe what a column means and has an agent fill it in for every row. A task that previously required researching 500 companies, applying the same fuzzy judgment 500 times, and manually recording 500 answers becomes a declarative operation: define the judgment once, then curate the results. The model is not bolted onto the database; it changes the relationship between the user and the database. Rows are items, columns are jobs, and cells are work.
This is roughly the architecture we are building at Sonora. Customer software traditionally stores accounts, tickets, calls, notes, product usage, and CRM fields, while humans are still responsible for synthesizing all of it. Is this account actually at risk? Has this problem happened before? Did the champion leave? Is this an escalation or just noise? Which customers are asking for the same thing?
Those are repeated judgments over a set of customers. Sonora turns them into work the system can perform continuously across the customer base, using the underlying calls, tickets, email, CRM, and product data as its substrate. The user defines what matters, corrects the system when necessary, and increasingly curates the output instead of manually producing it.
We celebrated Sonora reaching general availability tonight. A GA party is a chance to stop long enough to notice what the team has built, and what we celebrated was not a database with a chatbot attached. It was an engine that can do this work across every customer, continuously. There is much more to build, but that shift now feels real.
The interesting transition in AI software is not database → chatbot. It is database → engine.
Software used to store the output of human judgment. Increasingly, it can store the method.