Claw Empire installs a private AI assistant for hedge funds on customer-controlled infrastructure. It handles the data plumbing, point-in-time validation, and research grunt work that eats 90% of your day, then drafts IC-ready analysis with every claim traced to a source for approval before it touches a committee or an investor.
The useful install is narrow, explicit, and tied to work you already repeat. Claw Empire maps the lane, connects approved systems, and keeps sensitive actions gated.
Clean point-in-time accurate data is the biggest pain in the ass, as one quant-flaired PM put it on r/quant. It is the root of most future problems, and getting it wrong means backtests that look great until they blow up live.
The actual strategy logic is usually the easiest part. The bigger challenge is everything around it: data pipelines, vendor APIs, cleaning, validation. Teams spend 90% of their time on infrastructure instead of alpha research.
LLM hallucinations are a structural problem. You cannot trust long-form research outputs without checking for slop, and a hallucinated number in an IC meeting is career-ending. AI that sounds certain when wrong is the worst kind.
Bloomberg terminals cost nearly $29K per year per seat with no real alternative. FactSet is similar. Small and mid-size funds pay this lock-in tax just to access data they could process more efficiently with governed AI workflows.
A private assistant should produce a reviewable queue, not mystery automation. Every output should make approval faster and safer.
The assistant handles data ingestion, cleaning, point-in-time validation, and cross-source reconciliation so your team spends time on strategy, not plumbing. Every pipeline step is logged and reviewable.
Drafts of IC memo sections, risk summaries, and research notes come with every claim traced to a specific filing, cell, or data point. No ungrounded assertions reach the committee.
The assistant flags uncertainty instead of projecting confidence when wrong. Every output cites its sources, and gaps in evidence are called out explicitly for PM review before approval.
This is the level of specificity we want before a workflow touches production tools.
WORKFLOW: hedge_fund_research_pipeline
TRIGGER: new data ingest OR scheduled research cycle OR IC prep request
READ: SEC filings, market data feeds, alt data, internal models, sell-side research
DO:
1. validate point-in-time accuracy and flag look-ahead bias
2. ingest and clean data across sources with reconciliation logs
3. draft research summary and IC memo sections with source citations
4. flag hallucination risks and evidence gaps for PM review
APPROVAL GATES:
- never send IC memo or research note without PM approval
- never update internal models or risk parameters without approval
- never share competitive intelligence externally without review
STOP CONDITIONS:
- regulatory disclosure, performance dispute, compliance block, unclear owner
RECEIPT:
- write source citations, proposed analysis, PM reviewer, unresolved questionsThe point is not to remove judgment. It is to reserve judgment for the decisions that matter.
The assistant drafts memo sections and research summaries but waits for PM approval before anything reaches the investment committee.
Risk parameters, model updates, and signal adjustments are proposed with evidence before a human approves writes.
Peer fund and sell-side analysis stays internal and is never distributed externally without approval.
The exact implementation depends on tool access and data quality. The operating model stays simple: map, install, dry run, hand off.
Define approved data sources, research workflow, IC cadence, risk rules, and stop conditions.
Connect approved data feeds, filings, and internal models to the private assistant on your infrastructure.
Review proposed research summaries and IC drafts until the workflow matches your investment process.
Receive the operating runbook, approval receipts, and expansion notes for the next research cycle.
Plain answers for the trust, security, and implementation questions buyers ask before connecting AI to real workflows.
A Claw Empire hedge fund AI assistant is a private workflow on customer-controlled infrastructure that automates research infrastructure, validates point-in-time data, and drafts IC-ready analysis with source citations for PM approval before any external action.
The assistant validates point-in-time accuracy during ingestion, flags look-ahead bias, and logs every reconciliation step. Backtests run against properly scoped data so signals that looked great historically do not blow up live.
Every output cites its sources. The assistant flags uncertainty instead of projecting confidence when wrong, and evidence gaps are called out explicitly for PM review. The default workflow is approval-gated so nothing reaches the committee unchecked.
No. The assistant works alongside your existing data feeds and vendor tools. It handles the plumbing between them so your team spends time on strategy, not on stitching together vendor APIs at $29K per seat.
Pricing is scoped on a discovery call because data sources, model complexity, IC cadence, and compliance requirements vary. The public page does not publish a flat price because installs differ by fund size and infrastructure.
Send the workflow you want off your desk. We will confirm the tools, approval gates, stop conditions, and safest week-one install path.