AI Agent Consulting · Financial Services

AI Agent Systems for
Financial Services Firms

Not chatbots. Operational agents - premarket briefings, intraday monitors, research pipelines, and reporting systems running daily in live trading environments.

Proof in production Systems that run on live trading desks daily
No bloat You work directly with the person who builds it
Bespoke by design Systems built for your operational goals, not off-the-shelf templates

How We Work

Three phases. Every engagement starts with Discovery.

01
2 weeks

Discovery Sprint

$3,500

Workflow audit, AI opportunity map, and a concrete build spec - ranked by ROI and delivered in writing. No commitment to build. This pays for itself if even one insight lands.

  • 3–5 stakeholder interviews
  • Top 3–5 agent use cases ranked by ROI
  • Architecture + integration spec
  • 30-minute written readout
Get Started
03
Ongoing

Monthly Retainer

$3,000 – $7,000/mo

Ongoing maintenance, prompt tuning, and new mini-agents as your workflow evolves. This is where long-term value compounds. Every Build client is invited to stay on retainer.

  • Agent monitoring + maintenance
  • 1–2 new mini-agents/month
  • Priority support
  • Quarterly strategy session
Get Started

Not demos. Running daily.

These systems operate on a live trading desk every market day. They are the proof of work.

5:30 AM ET · Mon–Fri
Live

Premarket Intelligence Agent

Pulls overnight news, earnings surprises, and catalyst data for the morning watchlist. Scores each catalyst on a 4-level credibility scale - HARD / SOLID / SOFT / FLUFF - so traders know exactly what to trust before the open. Delivered via Telegram before 6 AM.

Python Claude API Telegram Polygon.io
9:30–11:00 AM ET · Real-time
Live

Intraday Scanner

Monitors the first 90 minutes of session for high-conviction gap-and-go setups. Filters by price range, gap percentage, relative volume, and float. Client-side toggles for gap threshold and RVOL sensitivity. Refreshes every 15 seconds.

Python Flask Polygon.io Real-time
Strategy Validation
Research

Backtest Engine

PhD-level validation methodology: walk-forward windows, Monte Carlo simulation across 10,000 runs, regime analysis across 5 market periods, and slippage modeling at the trade level. No lookahead bias. Reports Sharpe, max drawdown, ruin rate, and win rate at each R:R level.

Python Statistics Walk-Forward Monte Carlo

Built by someone who trades,
not just builds.

I'm a licensed securities professional - Series 7, 24, 57, and 63 - and a former equity trader at a NYC prop firm. Today I manage risk at an equity and options trading firm.

I built Paragon Lab because the gap between "we should use AI" and "AI is running in production" is enormous - and most AI shops don't have the domain knowledge to bridge it for financial services firms.

When your agents touch live trading workflows, the builder needs to understand markets. Not just software.

The systems in the Case Studies section run every trading day on a live desk. That's the standard I hold every client project to.

Book a Discovery Call

Start with a 20-minute intro call. No pitch, no commitment. Just a conversation about what you're trying to automate and whether we're a fit.

Location
New York, NY · Remote-friendly