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,this is where theanswer lives.
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 trust us to check the record.
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 researchOur research entered OpenAI's record.
OpenAI cited our Baba Is Bench work while examining what makes agent evaluations succeed.
Read the citationLatest from the blog
HN Claude Code pricing: same tokens, same model, up to 40x the price
Claude Code buyer’s guide, August 2026: what Max, Team, and Enterprise cost, how to get the best deal, and how Uber and Shopify cap the spend.
I wired 4 models together in Claude Code. It backfired 4 ways on Terminal-Bench
Common wisdom says to put a strong model like Fable in charge and let cheaper models do the work. I tested it and the result was not what I expected: seventh place at twice the cost of the top single-model run.
Quantization hurts knowledge nonlinearly - Qwen3.6 27B case study
Quantization is a lossy compression, and factual knowledge is incompressible. We test Qwen3.6 27B GGUF quantizations from Hugging Face (by Unsloth and Bartowski) and llama.cpp on the Incompressible Knowledge Probes (IKP) benchmark.
Bring clarity to your AI coding budget.
Early access opens soon. The open-source collector deploys in your environment, the record accumulates in your cloud, and early teams get every analysis first. Contact us to get in early.