Faster decisions. Full lineage. Pick both.
gravityAI turns your models, connectors, and workflows into governed components deployed inside your own cloud. Each one is risk-tiered, documented, and traceable to the data and code that produced it. Your teams move faster on decisions, and the lineage is already written when someone asks.
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When governance can't keep up, the answer is "no"
At an asset manager, ungoverned AI gets switched off.
Governance is a property of the artifact
gravityAI is an AI toolbox and governance platform for regulated firms. Models, data connectors, and agentic workflows become governed microservices, deployed inside your own cloud.
The important part: risk tier, documentation, ownership, access, and lineage travel with the component from the moment it is deployed. Using the platform produces a documented artifact every time, by construction.
Which is why the same control that satisfies model risk is what lets a second desk pick the methodology up and trust it.
What governed actually means
| Control | How it works | Why it matters |
|---|---|---|
| Inventory | Every model, connector, and workflow registered as a deployed component | You can answer "what is running, and on what data?" without a survey |
| Risk tiering | A risk level assigned to each component at deploy time | Oversight scales with exposure, so low-risk work stops waiting behind high-risk work |
| Documentation | Business context, implementation detail, and risk assessment generated alongside the artifact | Validation packs stop being a three-week archaeology project |
| Lineage | Each output traces to the component, data source, and version that produced it | An investment decision can be reconstructed months later, on demand |
| Ownership and attribution | Creator and consumers tracked per component | Every methodology has a named owner, and reuse is credited |
| Access control | Role-based access to components and the data beneath them | Desk-level data boundaries hold while discovery stays open |
| Deployment location | Your cloud, on-prem, or air-gapped | Models and data stay inside your perimeter |
Governance applied to the artifact is a control. Governance applied to a process is a policy. Only one of them survives contact with a deadline.
From one desk's model to a governed firm-wide component
Bring what you already have. Existing Python models, internal data platforms, market and reference data, vendor APIs, and your own LLM provider, connected once and available to everyone cleared for them.
• Upload existing models and packages
• API connectors for internal and third-party data
• Your LLM provider, your keys, your contract
• Runs in your VPC, on-prem, or air-gapped
Assemble components into decision workflows. Non-technical users build in the no-code canvas. Quants drop into the API and extend the same components in code. Same building blocks, two front doors.
• Drag-and-drop agentic workflow builder
• Full API and code path for technical users
• Chain models, connectors, and LLM steps
• Human-in-the-loop checkpoints where they matter
Every component carries a risk level and its own documentation, generated as it is built. Every deployment across every desk shows up in one dashboard, including the ones you didn't know about.
• Risk tier assigned per component
• Business, implementation, and risk documentation generated with the artifact
• Single dashboard across every deployment
• Access control, audit trail, and lineage by default
Publish to an internal catalog. The credit team's methodology becomes the fixed income team's starting point, with its assumptions, documentation, and lineage attached. The second desk inherits the reasoning along with the code.
• Internal catalog of models, workflows, and connectors
• Fork and adapt an existing component
• Usage attributed to the creator
• Versioning and rollback
One platform, three people who never agree on tooling
The quant or data scientist
Publish work as a governed service other desks can use
Find prior work before starting from scratch
A defined path to production that runs without a ticket queue
Your code, your libraries, your standards
The PM, trader, or analyst
Compose analysis without writing code
Adapt a methodology another desk already validated
Live data from systems you already trust
See the assumptions behind the number before you act on it
The head of model risk
Every deployed model and workflow in one view
Risk tiering applied at deploy time
Documentation and lineage that exist because the platform made them
Everything stays inside your cloud
Decisions teams put on the platform first
Patterns investment teams build with governed components.
Build it, bolt it on, or deploy it governed
| Feature | Build in-house | Workflow tool plus governance overlay | gravityAI |
|---|---|---|---|
| Governance attached to the deployed artifact | — | — | ✓ |
| Lineage from output back to code and data version | — | — | ✓ |
| Runs inside your own cloud | ✓ | — | ✓ |
| No-code and code paths on the same components | — | — | ✓ |
| Inventory across every desk | — | — | ✓ |
| Documentation generated with the model | — | — | ✓ |
| Live without a multi-quarter build | — | ✓ | ✓ |
Questions we get from investment firms
Restriction is what happens when usage can't be evidenced. On gravityAI, every model call runs through a registered component with a risk tier, an owner, access controls, and a record of what data it touched. That is the difference between allowing a tool and allowing a governed capability, and it is usually what lets a firm turn access back on.
Yes. gravityAI runs in your secure cloud, on-premise, or air-gapped. Models and data stay inside your perimeter, and deployments inherit your existing access controls.
No. It sits on top of it. gravityAI connects to the data platform, warehouse, and vendor APIs you already have, and makes what gets built on them governable and reusable. If you are mid-build on a central data platform, this is the layer that makes it pay off.
Each component carries a risk tier and generated documentation covering business context, implementation, and risk, plus lineage from output back to source. It feeds your existing validation and inventory obligations and works within your current framework.
No. Non-technical users build in the no-code interface. Technical users work through the API with their own code. Both groups work on the same governed components.
No. You connect your own LLM provider under your own contract, and your data and models stay in your environment.