An AI co-researcher
for quant teams
Zerve is a research and analytics environment with an AI co-researcher built in. It runs many lines of inquiry in parallel across your compute, and everything it produces stays inspectable down to the code and the sources. It deploys on your own infrastructure.
In use at multi-manager and systematic hedge funds, global asset managers, investment banks, and institutional investors.
Quant researchers use Zerve for
The value is throughput: more ideas tested in a compressed period of time, with a record of everything that was tried.
Point the co-researcher at a paper and it works the methodology through against your data. Run it across hundreds of papers at once to find where published results break, instead of reading them one at a time.
Each iteration reads the last one. Promising branches carry forward across epochs, dead ends are recorded rather than repeated, and the learnings travel with the project instead of living in one researcher's head.
Take an idea from hypothesis to tested strategy in one environment. Data prep, feature construction, and iteration, against your own catalog of data sources rather than whatever is uploaded.
Run backtests with the analysis history preserved, so overfitting checks, robustness work, and lookahead-bias review sit alongside the original result rather than in someone's notebook.
Treat prompt and agent design as an experiment: run variants side by side, compare them on the same data, and assign cheaper models to the roles that do not need frontier reasoning.
Your AI, your rules
Generating code alone never solved anything. Testing it, evaluating it, and compiling it does. Let the AI co-researcher write the commits, while you keep the flexibility.
Guardrails and constraints
Approved data sources, permissible tool calls, and required output formats, set at enterprise level and signed off independently of the research team.
Agent skills
Your analytical frameworks, approved methods, and research conventions, version controlled and peer-reviewable. Every agent works to the same standard.
Audit and attribution
Every data query, model call, transformation, and output is logged with attribution. The full trace is inspectable and exportable for model risk review.
Deterministic Output
Editable Python, R, SQL, GraphQL, or PySpark you can rerun without the agent. Same inputs, same result.
Parallelisation and compute control
Work fans out across thousands of parallel executions, with per-step control over the compute each one gets and firm ceilings on what the team can consume.
No black box
Variables cached and inspectable at every block. Downstream cannot mutate upstream.
Built to pass your security review
Quant research is sensitive by definition. Three deployment models to fit into your security requirements
Air-gapped on premises
Runs on your hardware with no network in or out. Helm charts on Kubernetes, Docker on individual machines, or a tarball.
Your cloud account
Self-hosted into your own VPC on AWS, Azure, or GCP. A CloudFormation install takes minutes and your data never leaves your perimeter.
Managed by us
An isolated tenancy we operate, for teams deliberately reducing the number of systems they run internally.
Any model, including your own
- Model-agnostic. Frontier models on your own API keys, your internal LLM gateway, open source, or models hosted locally.
- Assign models per sub-agent role: fast models for retrieval, frontier reasoning for hypothesis generation.
- No product analytics, no tracking, and no training on your data. The knowledge that accrues, accrues to you.
The perfect interface for human-agent collaboration.
