Financial Services

AI agents for quant research, trading infrastructure, and data platform engineering — where code and data can't leave your environment. Fleet orchestration across globally distributed teams.

The problem is trust, not capability

Walk into any hedge fund, asset manager, or bank with a serious engineering team and you'll find the same thing: developers who already know what AI agents can do, and a compliance team that won't let them do it.

The capability gap closed months ago. Claude, Codex, and open-weight models can write production code, refactor systems, build data pipelines, and run research workflows. The developers know this. They've used these tools on personal projects. Some are quietly using them on their work laptops already.

But the security and compliance requirements in financial services are real, not bureaucratic theatre:

• Code and data can't leave the environment. Proprietary trading strategies, quantitative models, position data, and client information are the crown jewels. Sending any of this to a third-party API — even encrypted, even "just for inference" — is a non-starter for most compliance teams.
• Audit trails are mandatory, not optional. Every action an agent takes — every file it reads, every commit it makes, every prompt it receives — must be logged, attributable, and available for regulatory review.
• Credential management is critical. Agents that access internal systems need ephemeral, scoped credentials — not long-lived API keys that create blast radius if compromised.
• Multi-jurisdiction complexity. A London desk, a New York desk, and a Singapore desk all have different regulatory regimes. The AI platform needs to work across all of them without creating cross-border data transfer problems.

The result: engineering teams that could be 5–10× more productive are stuck at 1×, because the tools that would unlock that productivity can't satisfy the security requirements.

What financial services teams actually need

Quant research acceleration — AI agents that can explore datasets, prototype models, backtest strategies, and write research code. Running inside the firm's environment, with no data exfiltration risk. A quant researcher gives an agent a hypothesis, the agent builds the prototype, runs it against historical data, and presents results — all within the firm's infrastructure.

Trading infrastructure engineering — Building and maintaining the systems that execute trades, manage risk, and process market data. These systems are complex, latency-sensitive, and safety-critical. AI agents can handle the routine engineering — refactoring, test writing, dependency updates, monitoring configuration — while engineers focus on the hard problems.

Data platform engineering — ETL pipelines, data quality frameworks, analytics infrastructure. Every financial firm has a mountain of data engineering work. AI agents working a backlog in parallel — each isolated, each auditable — can clear months of accumulated tech debt in weeks.

Fleet orchestration across time zones — London opens, New York is sleeping, Singapore is closing. AI agents don't have time zones. A fleet of agents working 24 hours across three desks, with human review gates at each handoff point, means work never stops and context is never lost.

How Helix fits

Helix deploys on the firm's own Kubernetes cluster — the same infrastructure they already run their trading systems on. No new security review for a new cloud vendor. No data leaving the perimeter. No third-party API calls unless the firm explicitly configures them.

Ephemeral per-task credentials — Every agent gets scoped git keys issued at task start and revoked at task end. No long-lived secrets. If an agent is compromised, the blast radius is one branch of one repository for the duration of one task.

Branch-scoped access — Agents can only write to feature branches. No writes to main. Every change goes through the firm's existing review and merge process. The agent proposes; humans approve.

Full audit trail — Every prompt, every response, every file access, every git operation. Exportable, queryable, ready for compliance review. This isn't a log file buried in a container — it's a first-class audit system.

SOC 2 Type II and ISO 27001 certified — Independently audited. The security controls are verified by a third party, not just claimed.

Air-gap deployable — For the firms that need it, Helix runs fully disconnected. No internet access required after initial deployment. Open-weight models that rival Claude and GPT on coding benchmarks, running locally.

Multiplayer across desks — The London team watches agents work during their day. When they leave, the Singapore team picks up the same agent desktops, same context, same running applications. Zero reconstruction. Zero re-prompting.

The ROI case

A senior quantitative developer costs £200K–£400K fully loaded in London, more in New York. If AI agents can handle 30–50% of the routine engineering work — test writing, refactoring, documentation, boilerplate, data pipeline construction — that's the equivalent of adding several engineers to the team without adding headcount.

At the Enterprise tier (from $75K for an 8-week production pilot), the breakeven is fast. A team of 20 developers, each running 2–3 concurrent agents, produces the throughput of a team of 30–40 — without the recruitment timeline, the onboarding cost, or the office space.

For firms that want to own the hardware: the Sovereign Server ($175K) supports 20–30+ developers with 768 GB of VRAM and zero external dependencies. Cloud AI costs roughly $3,000/developer/month. The server pays for itself in under three months.

Get started

Enterprise deployment — Deploy on your existing Kubernetes cluster. RBAC, SSO, SOC 2 Type II, ISO 27001, ephemeral credentials. 8-week production pilot from $75K. Talk to us →

Sovereign Server — A turnkey 4U rack server. Ship to your data centre, power on, done. $175K. Learn more →