Language → validated result
A user's sentence becomes a structured strategy specification, passes validation gates, generates code in a sandbox, runs a backtest on market data, and returns results — one agent turn, no human in the loop.
01 Work
Agents we've built — and run every day.
We don't just advise on agents; we build and operate them. A commissioned agent product delivered into production with live users, and internal agent systems running daily inside our own trading operation — where an unreliable system costs real money.
02 Delivered engagement
One-Sentence Quant: a commissioned agent, live today.
Commissioned by an external team: an LLM agent that turns one plain-language sentence into a validated, backtested trading strategy. Designed, built, deployed, and operated end-to-end by AOTC AI — the engagement model on our Approach page, delivered.
The pipeline
The engineering
Production machinery, not a demo
Queued workers, sandboxed code execution, schema-validated agent outputs, rate limiting, live monitoring, and failure paths that degrade gracefully — serving real users on modest infrastructure.
The proof
See it running
The product is live at osq.aotc-ai.com — not a landing page: the actual agent, in production, delivered in 2026 and operated by AOTC AI since.
03 Internal systems
We are our own first customer.
Our trading operation runs on agents we built: automated research pipelines, live market-event capture with watchdogs and failover, and data-ingestion agents that reconcile vendor feeds daily. They run unattended in live markets — an environment that does not forgive silent failures.
Every reliability practice we sell — instrumentation, fallbacks, escalation paths, testing against live edge cases — exists because our own money depended on it first.
04 Worked example
What an engagement looks like. (Illustrative)
A representative mid-market scenario — shown to make the method concrete, not a client reference.
01
The workflow
An operations team reconciles counterparty statements against internal records every morning: multiple formats, frequent exceptions, two staff-days daily, and the real rules — which breaks matter, who to chase, when to escalate — live in two senior operators' heads.
02
The embed
Our team sits with those operators for two weeks, capturing the exception taxonomy and decision rules as a testable specification — the latent knowledge that makes the workflow actually work.
03
The agent
An agent ingests statements in every live format, matches and classifies breaks against the captured rules, drafts the chase correspondence, and escalates low-confidence cases to a human queue — instrumented so every decision is auditable.
04
The handover
After parallel-running against the manual process to measured agreement, the agent takes the morning run. Source, runbooks, and dashboards are handed over at a one-time cost — the team keeps the exceptions queue, drops the grind.
05 Next
Have a workflow like this?
Bring it to a free 45-minute diagnostic — we'll map the workflow, locate the latent knowledge, and tell you honestly whether an agent fits.