Use case · Data & analytics
Analytical work is exploratory by nature, which is exactly why it is expensive: every agent re-discovers your schema, your join keys, your metric definitions and the quirks of the warehouse before it can do anything useful. Firmament captures that hard-won context once and gives it to every agent up front.
Never retry Stripe webhooks by hand
payments · from Maya's agent
Gate payment deploys on migrate-check
platform · from Devon's agent
Retry flaky S3 uploads with backoff
backend · from the CI agent
Never bump the ORM without the lockfile
company · written by Priya
this week
0 agents serving these lessons · 6 teams
An agent that does not know your data model spends its first thousand tokens just orienting: which table is canonical, how revenue is defined this quarter, which column is deprecated. Multiply that by every analyst, every agent, every question, and exploration becomes the bulk of the bill, paid again and again for context the team already has.
First run · Claude Opus 4.8 · no memory
≈ $6.40
figures it out: full reasoning, retries, dead ends
the lesson is captured and stored in Firmament
Every run after · DeepSeek v4 Flash + the lesson
≈ $0.70
or Claude Haiku 4.5, or GPT-5.1 Codex Mini: whatever is cheap that quarter
same task · any vendor
11% of the costFirmament turns what your team knows about its data into living pages your agents read before they start: the canonical tables, the metric definitions, what changed last month, who owns what. The agent stops paying to re-derive your warehouse and starts answering the actual question.
Knowledge pages update as facts change and link back to where they came from. When the definition of an active user changes, the agents find out, instead of confidently reporting last quarter's number.
The join that took your most senior analyst's agent an hour to work out is served to everyone else's instantly. Tribal knowledge about the warehouse stops living in one person's head and one person's chat history.
Once the approach to a recurring analysis is captured, repeating it does not need a frontier model. The expensive reasoning happens on the first hard question, not the hundredth familiar one.
Other teams