Quantitative research and agentic systems for investment firms.
Factor and regime modelling: dimensionality reduction on time series, forward factor prediction, regime change detection, alpha extraction. Alternative data from EDGAR and SEC filings, plus NLP on earnings transcripts and live feeds.
A dedicated machine, virtual or physical, deployed to your infrastructure under your Interactive Brokers credentials. Query the book and place orders in natural language. Entry, exit, and risk rules run as skills you define.
Screens outbound marketing, public, and employee communications against rule libraries at line and document level. On-premise, so nothing leaves your environment.
Traditional portfolio managers lack time to learn UNIX, Python, or agentic modeling from scratch. Our interactive workshops cover agent frameworks, multi-agent orchestration, Python for ML, and Git/UNIX fundamentals to master this quantitative stack.
Founder — Pavan MirlaFormerly a quantitative analyst at CPP Investment Board (~$500B AUM), led the John Hancock Innovation Lab (2014), forward deployment engineer at DataRobot. Independent since 2019, working with hedge funds on signals, quantitative research, and regime/factor detection. Previously Cisco Systems, Manulife Asset Management, John Hancock Asset Management.
Delivery TeamDelivery is supported by a partner team of agent verification engineers, model validators, and QA professionals.
AI for Good
Alongside client work, we build early-stage, mission-driven pilots — not commercial products — applying the same tools to accessibility and communication challenges for kids.
Accessible Math for Dyslexic LearnersCalculus and math instruction adapted for dyslexic students — alternative representations and pacing built around how they actually process information. Piloted with a small group of learners.
Talking-Head Video for Non-Verbal Autistic ChildrenPrivate, on-premise video generation — a local Dell server with GPU, nothing sent to the cloud — that turns a single headshot and a controlled voice into a talking-head video of the child themselves speaking. The idea: seeing and hearing a familiar, controllable version of themselves speak is motivating in a way generic tools aren't. Tested in small early trials.
These are early pilots, not clinical or diagnostic tools — shared here because they're part of why we build what we build.
Sample: trial participant — supermarket. Shared with the family's permission.
Investment Research & Portfolio Construction
Investment research using ML, statistical analysis, NLP, and agentic systems.
Our Research Process
- Multi-factor dimensionality reduction for longitudinal time series data
- Time series modeling and forecasting for factor analysis
- ML for hierarchical clustering and forward factor/premium prediction
- Regime change detection and cross-industry correlation analysis
- Identifying macro factors and anomalies within complex macro datasets
- Building custom indices from raw data using NLP
- Cumulative alpha extraction via longitudinal regressions — theme, industry, factor, and stock level
- Regime transition probabilities to catch rotation early
- New data sources built from EDGAR and SEC filings
Sector rotation insights, thematic investing, and real-time company financial transcript analysis.
What You GetThematic and in-depth research, custom models built for your mandate, and automated research systems that keep running — with alerts when something transitions to a new state.
Upskilling Workshops for Asset Management
Workshops delivered to investment managers, content analysts, and technical analysts.
Workshop Flow
- AI agents and agentic systems
- ML and statistical analysis using Python
- GitHub version control
- Unix ecosystem fundamentals
Goes beyond general AI literacy — hands-on with the same agent frameworks powering production research and trading systems today.
- Claude (Anthropic) agents — tool use, the Model Context Protocol (MCP), and multi-agent orchestration
- OpenAI agents — function calling, the Assistants/Agents SDK, and custom GPTs
- Orchestration patterns — multi-agent pipelines, agent handoffs, and human-in-the-loop review
- Custom skills & plugins — building and deploying internal tools agents can call
- Applied to research — agents that pull filings, draft memos, screen for compliance, and monitor markets in real time
Compliance Tools for External Communications
Screens all outbound communications — marketing, public, and employee — against thousands of rules.
Workflow- Rules matched per line and per document
- Anomalies flagged with recommendations per failure
- Recommendations feed model retraining and rule updates
Built as private, on-premise systems for enterprise use. Compliance officers use it as a first-pass screener before external sharing.
Screening & Review Process
Feeds back into Approved Outcomes — the system only learns under supervision.
Private Agentic Trading Computer
A dedicated trading computer — virtual or physical — running entirely on your own infrastructure: private GPU, isolated VMs, your own Interactive Brokers Gateway credentials. No portfolio or order data goes to external LLM providers.
Trading Flow
Fundamental data, filings, options data, and Bloomberg & CNBC live transcription feeds.
Capabilities- Natural-language control of Interactive Brokers — a custom skill invokes portfolio, options, and order actions, improving as reviewed tasks refine the skill
- Option strategy recommendations and exit strategy design
- Portfolio performance, market impact, and real-time event monitoring
- Screeners for opportunity identification and risk management
Every entry and exit passes a defined checklist before it executes, and each evaluation is recorded. Rules are yours to set and change; model and rule updates require approval.
Tasks run continuously, with anomalies and hot event notifications delivered via Telegram.
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