Built so autonomous agents can be trusted with real work.
TrustyGenius is not a chat layer bolted onto an API. Agents run on a purpose-built production-agent runtime: durable state, approved tools and data, fast computation, validation, a full audit trail, and access control enforced where the data lives. The result is a system you can deploy inside your own boundary and inspect after the fact.
From input to reviewed action
Every agent, whatever its job, runs the same loop. Each step leaves state behind, so the team can see what an agent did, what it used, and where a person was asked to decide.
- 01
Receive
An agent starts from an input: a schedule firing, a new email, a price move, a filing, or a question from a person.
- 02
Understand
It works out what the input means and what it is being asked to do, against its charter and its memory of what came before.
- 03
Retrieve and compute
It pulls the data and runs the tools it is allowed to use: queries, calculations, models, whatever the job needs.
- 04
Act or request approval
Within its permissions, it acts. Past them, it stops and asks a person, with the context they need to decide.
- 05
Record and monitor
Every step is written down, and the agent keeps watching for what changes after it acts.
The quant research lifecycle
The quant platform is that same loop, instantiated for research and paper trading. It stays in research and simulated paper trading throughout; nothing graduates to live execution.
- 01
Ingest
Market data, paper account state, reference data, filings, macro series, and custom feeds land in one shared place, so research and risk see the same picture.
- 02
Compute
Derived exposures, rolling contracts, custom spreads, indicators, risk views, and scenarios stay current and consistent, ready the moment an agent or a person asks for them.
- 03
Research
Agents generate and investigate ideas and write the evidence into a durable journal, so a conclusion is never just a line in a chat transcript.
- 04
Validate
Every candidate is stress-tested and proven out-of-sample before it trades on paper, with a record of what survived and what failed.
- 05
Monitor
Running paper strategies are watched for regime change, decaying edge, correlation creep, reconciliation breaks, and limit violations, and paused before a stale edge takes hold.
Built to be trusted, not just fast.
Agent systems break when intelligence, data, and permissions are scattered across loosely connected services. TrustyGenius keeps the state that matters in one place and is engineered to stay quick on modest hardware, so speed never comes at the cost of control.
Quick on modest hardware
Purpose-built for the work a desk actually does. You do not need a server farm to run it.
Your data stays within the agreed boundary
Run TrustyGenius managed or inside your own cloud, VPC, or on-premises environment. Data sources, credentials, and storage follow the deployment and governance model agreed with your team.
Access at the data layer
Permissions are enforced where the data lives, not only in application code, helping prevent an application defect from exposing another user's data.
Everything is auditable
Insights, strategy candidates, tool calls, validation results, and operator actions can all be inspected after the fact.
Your alpha should not become someone else's training data.
Runs where you choose
Run it managed, or deploy in your own cloud, VPC, or on-prem. Data sources and credentials follow the governance model you agree with your team.
Isolation at the data layer
Access control at the data layer is designed to prevent users from accessing another user's book.
Auditable agent work
Insights, candidates, tool calls, validation results, and operator actions can be inspected after the fact.
Provider flexibility
Use the model providers your governance allows. Work routes through approved keys and tools.
Built for a real operations path
TrustyGenius can start small for evaluation and graduate into a managed production footprint, adapting to the container platform and controls your team already uses.
Evaluate
Seed data, paper account, and a focused instrument universe.
Operate
Scheduled agents, dashboards, reconciliation, alerts, and audit logs.
Extend
Custom feeds, brokers, models, tools, asset classes, and agents.
The point of the architecture
Speed matters, but the larger value is discipline. The system gives autonomous agents enough context to be useful, enough controls to be reviewable, and enough memory to improve from the team's feedback instead of starting over every morning.