Scale the engineering, not the slopOutbenching the frontier with discipline you can scale
Prompts are instructions, not contracts, which is why Descant uses explicit contracts and type-safe data to orchestrate multiple models through software-enforced engineering, with quality quantified and verified. You get software you can trust, built at scale, with every cent tied to the work produced.
Great engineering shouldn’t depend on [who’s] prompting
Give everyone the same models and quality depends on who’s prompting. When it falls short, the whole team pays.
Even when perfect, prompts aren’t contracts. Descant brings professional engineering discipline to autonomous work at scale. It examines the whole problem from different perspectives and challenges assumptions. Structured data and software-enforced checks carry that standard through development, verification and independent review. Claims need measurable evidence checked against the work.
No slop grenades for your team to clean up, no relying on hope when your customers rely on you.
How Descant enforces the engineering standardYou know the instruments, meet the orchestra
Descant structures how the problem is understood—not just how the code gets written. It brings different perspectives to the work, exposes assumptions to challenge, and carries the resulting requirements through implementation and review.
That composition produces better software: each perspective sharpens the others, and each part strengthens the whole.
Explore the product
Descant resolved 93 of 113 long-horizon software engineering tasks on its current release, Soprano 7.
Read more about the benchmark scoreStay at the frontier without chasing it
Greater intelligence does not automatically produce comprehensive, dependable engineering. Information still needs to be discovered, behavior verified and learnings retained.
Descant carries those responsibilities through a process run by software. As models improve, we shed unnecessary machinery and develop new ways to multiply their value.
Why intelligence needs an engineering systemGive the work somewhere to go
Start with one issue or give Descant the refactor, migration, feature or larger initiative your team has struggled to make room for.
Issuegraph connects dependencies, priorities and follow-ups so independent work runs in parallel while related changes stay coordinated. Descant retains learnings and grooms the work ahead, reconciling duplicates and stale issues as the codebase evolves.
Increase capacity while keeping the same engineering discipline and visibility into spending.
acme-labs ▾Signed in to acme-labs · owner. Here's where your repos stand.
Increase your limit in the dashboard.
Bring us the scale you need.
The AI spend is clear, the impact should be, too
See what is running, what has been completed and what was spent getting there.
Descant tracks costs down to the cent and ties spending to units of work. Follow an assignment through development and review, inspect the changes and see what delivering them cost.
You have evidence to evaluate the investment: better software, a record of what changed and the numbers behind it. Use it to decide where to put Descant to work next.
Better software with less wasted compute
Latest measured work across 100 PRs: 92.8% of tokens from cache; 96.9% during implementation.
acme-labs ▾A run counts in full under every label on its issue, so these figures add up to more than the range total.
Go and be brilliant
Make more room for the ideas, architecture and decisions that move the business forward. Research, ideate and work through system architecture, technical designs, specifications and protocols with your favorite agents. We encourage it, and it's how we use Descant ourselves. Turn that work into issues with requirements and acceptance criteria, then hand the engineering to Descant.
Through the API, CLI and MCP, those agents can file issues, follow progress and report back while Descant executes the work. Descant turns requirements and acceptance criteria into structured data that software quantifies and verifies, not a model's assertion. Agents can synthesize research and evidence, but model intelligence can't replace information that is truly missing.