Deploy the platform
We deploy the Hedgineer platform directly into your cloud environment. This empowers your team to build agents, skills, and workflows that you own and never export to external vendors.
See the platformAI-native isn’tsomething you buy.It’s something youcraft.
The governed AI layer your fund owns. Agents, skills, observability, and automation in one place so your team can focus on what matters most.
90 daysKickoff to live
BuiltBy industry veterans
GovernanceFor every action
Wired into the stack you already run
THE PROBLEM[01]
Building a comprehensive AI strategy is more complicated than it should be.
Funds are already leveraging frontier models, but the harder challenge is scaling more impactful automations across teams. Portfolio systems, internal research, and custom automations live in disconnected silos; governance tightens as adoption spreads; and token spend rises without clarity on ROI. For hedge funds and asset managers, turning fragmented experiments into production-grade workflows isn’t a technology problem.
It’s an operations problem.
THE SOLUTION[02]
Hedgineer makes your fund AI-native
in 90 days.
Three parallel workstreams, run by a single team of engineers,
financial analysts, and product specialists.
Automate key workflows
We target 2 to 3 high-value workflows and automate them end to end. Our team builds the MCP connectors, skills, and agents directly into your instance of the platform.
See examplesTeach your team to build
We host targeted teach-ins on tools like Claude, Codex, and the Hedgineer platform. While our team builds your first suite of agents, we're disseminating our knowledge across your org so your people can start building and owning AI growth themselves.
Read the notesGOVERNED BY DESIGN[03]
Stay ahead of AI governance, automatically.
Government regulations around AI record-keeping are still sparse, but as your team uses AI for more mission-critical functions like research, ops, and IR, the compliance surface area grows. The platform tracks all AI usage across all AI subscriptions (Claude Enterprise, ChatGPT Teams, etc.), and can flag issues like PII/MNPI detection. All skill and agent modifications go through a review process to ensure the right stakeholders are signing off on modifications before they go live.
Portfolio Risk Alert
Why this change
The skill often pulls T-2 factor model data when T-1 data is unavailable, but when this happens the nuance isn’t explicitly included in the report.
Diff
SKILL.md
Before
Fetch latest exposures and build the factor risk report.
After
Fetch latest exposures. At the top of the report, include the as of date of the factor betas.
references/data-staleness.mdNew
When factor risk model data is stale (older than T-1), make sure to pull portfolio positions, market prices, etc as of the market close on that date.
WHAT WE SURFACE[04]
Coaching for every employee, insights for every stakeholder.
Hedgineer synthesizes usage into engagement reviews that read like a coach helping team members get better at using AI. Individuals get personalized feedback on their patterns and tips to address shortcomings; management stakeholders get details on adoption trends, gaps across teams, and suggestions for what to build next.
Jordan Ellis
Research · individual report
May 19 – 25
Workflow Sophistication
2.5
Intent Clarity
3.2
Iteration & Verification
2.0
Cost Efficiency
3.2
5ACTIVE DAYS
97SESSIONS
2.8AVG ENGAGEMENT
$247.80CLAUDE CODE COSTCost captured from Claude Code OTEL telemetry only; Cowork and Claude Chat sessions aren't priced here, so treat it as a lower bound.
