A failure worth fixing once.
Three teams worked around the same broken integration environment.
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Your AI bill doubled.Can you saywhy? We can.
Every month the invoice says your coding agents worked hard. It cannot say on what, for whom, or whether any of it was worth it.
Quesma turns Claude Code, Codex, and Cursor sessions into a clear view of what the agents did, what it cost, and what you got back.
Combined monthly bill−$90,300 saved / 80.3%
When the bill doubles,the reasonslook like this.
Marketing started using Claude Code
A team you didn’t expect found a use for the tools.
Heavy work with Fable 5
The default model got smarter, and pricier.
Security fixes before the audit
The work ran hot for three weeks because you asked it to.
Fix the problems. Find good practices.
Cost tools only find things to cut. Quesma also finds problems recurring across teams, and the skills, tools, and routines used in one part of the company but nowhere else.
A skill worth sharing.
deploy-preview worked for one team. Others had never seen it.
Platform12/12
Payments7/12
Web2/12
Datanot yet
Instructions agents misread.
One repository rule sent every session down the same wrong turn.
“Always run the full e2e suite before committing”
- #4821 fix typo in tooltip → full e2e suite38 min
- #4837 rename a variable → full e2e suite41 min
- #4859 update README → full e2e suite44 min
31 sessions took the same wrong turn
Dependencies nobody decided on.
context7-mcp spread until more than half the company relied on it.
10 teams · 50%
Our track record
BinaryAudit
Can coding agents find backdoors in compiled binaries using Ghidra and radare2?
We trained the hardest models.
We built training environments for difficult, multi-hour agent tasks. The work required reading full trajectories: prompts, tool calls, failures, retries, and results.
Teams ask us to verify results.
We audited Blitzy's 66.5% SWE-Bench Pro result, reviewing the environment, required changes, and final score.
Read the independent audit Terminal-Bench 3.0We advance open agent evaluation.
Quesma contributed to Terminal-Bench 3.0, a benchmark for testing agents on practical terminal tasks.
View the contributors OpenAI-cited researchOpenAI cited our research.
OpenAI cited our Baba Is Bench work while examining what makes agent evaluations succeed.
Read the citationLatest from the blog
Knowledge vs wisdom: asking AI “What mushroom is that?”
AI mushroom identification from a photo with ChatGPT, Claude or Google Gemini: GPT-6 Astra, Gemini 3.8 Flash, Claude Fable 5.1, GPT-5.6 and GLM-5.3-Flash. Asked “What mushroom is that?” on 360 photos of poisonous species. Which warn, which get it right, which fail.
HN Mushroom hunting with LLMs: what can go wrong?
Mushroom identification with AI: GPT-5.6-Sol, Gemini 3.8 Flash, GLM-5.3-Flash and Claude Fable 5.1 benchmarked on poisonous and edible species of FungiTastic. A lot of dangerous errors.
OpenAI Codex pricing: the $270 PR a $200/month sub covers daily
A buyer’s guide to OpenAI Codex for individuals, teams, and enterprises. Includes comparisons with Claude Code.
Bring clarity to your AI coding budget.
Early access opens soon. The open-source collector runs in your environment and the session data stays in your cloud. Contact us to be one of the first teams.