H1 2026 — AI-Driven Fund Operations & Research Infrastructure
7
Projects
48K+
Lines of Code
Automated
Fund Operations
Project 1
Fund Daily Report Dashboard
5,354
Lines of Code
125
Excel Files Parsed
3
Dev Phases
Development Timeline
- Phase 1 — Excel parsing engine & data normalization
- Phase 2 — Flask dashboard with chart components
- Phase 3 — Benchmark comparison & risk metrics
Technical Highlights
- Replaced static Excel reports with a live Flask dashboard featuring 5 benchmark index comparisons
- Built an intelligent Excel parser handling 125 heterogeneous file formats with auto-detection
- Implemented rolling return analysis, drawdown visualization, and risk-adjusted performance metrics
Business Impact
Fund managers now have real-time access to portfolio analytics that previously required hours of manual Excel work.
Project 2
Fund Operation Agents
10,253
Lines of Code
90%+
Time Saved
$0.003/share
Precision
Development Timeline
- Phase 1 — IB XML parser & trade reconciliation
- Phase 2 — NAV calculation engine with fee waterfall
- Phase 3 — Investor packet generation & PDF assembly
Technical Highlights
- Automated the full monthly NAV cycle: IB XML parsing, fee calculations, investor statement generation
- Achieved $0.003/share precision matching manual calculations by fund administrators
- Built a multi-agent pipeline that coordinates data extraction, validation, and report assembly
Automation Pipeline
Business Impact
Reduced the monthly close process from 6-10 business days to under 1 hour, freeing fund operations staff for higher-value work.
Project 3
General Fund Admin
11,723
Lines of Code
0.0009%
Precision
1,485
Formulas
Development Timeline
- Phase 1 — Core NAV engine & share class accounting
- Phase 2 — Fee waterfall implementation (mgmt + performance)
- Phase 3 — Maples format export & cross-fund templates
Technical Highlights
- Engineered 1,485 formulas for multi-class NAV calculation with management & performance fee waterfalls
- Achieved 0.0009% precision against the Maples administrator benchmark
- Designed for scalability — template system allows rapid onboarding of additional fund structures
System Architecture
Business Impact
Attempted to extend the system to other funds using the Maples format, proving the platform's generalizability for multi-fund operations.
Project 4
Trading Agents (Tauric Research)
15
Agents
26
Bloomberg Codes
6
Stocks Analyzed
Development Timeline
- Phase 1 — Agent architecture & Bloomberg API integration
- Phase 2 — Fundamental & technical analysis agents
- Phase 3 — Report synthesis & recommendation engine
Technical Highlights
- Architected a 15-agent system where specialized agents handle fundamental, technical, and sentiment analysis
- Integrated 26 Bloomberg data codes for real-time market data, financials, and consensus estimates
- Produced institutional-grade research reports with buy/sell recommendations for 6 equities
Business Impact
Delivered a multi-agent research system that mirrors the workflow of a full equity research team, dramatically reducing analysis turnaround time.
Project 5
SlideForge
32
Slides Polished
101
Images Processed
4
QA Rounds
Development Timeline
- Phase 1 — Slide analysis & layout detection
- Phase 2 — Image processing & design optimization
- Phase 3 — Multi-round QA & style consistency
Technical Highlights
- Built an automated pipeline that transforms rough slides into polished, professional presentations
- Processed 101 images with intelligent cropping, resizing, and placement optimization
- Implemented a 4-round QA loop ensuring consistent visual quality across all slides
Business Impact
Reduced presentation preparation time from days to minutes while maintaining professional design standards.
Project 6
AI Builder Camp
3
Course Days
9
Total Hours
4
Cert Tiers
Development Timeline
- Phase 1 — Curriculum design & learning objectives
- Phase 2 — Hands-on project modules
- Phase 3 — Certification framework & assessment
Technical Highlights
- Designed a 3-day, 9-hour curriculum teaching AI fundamentals to young learners
- Created a 4-tier certification system rewarding progressive skill mastery
- Developed hands-on projects allowing students to build and deploy their own AI applications
Business Impact
Bridged the gap between AI theory and practice for young learners, creating a reusable curriculum framework for future cohorts.
Project 7
Knowledge Pipeline (Project Clone)
233
Videos Processed
18.3
Audio Hours
305K
Words Extracted
Development Timeline
- Phase 1 — Video ingestion & audio extraction pipeline
- Phase 2 — MLX-accelerated Whisper transcription
- Phase 3 — Topic segmentation & knowledge base indexing
Technical Highlights
- Processed 233 financial education videos into a searchable, structured knowledge base
- Leveraged Apple Silicon MLX acceleration for 3x faster transcription than cloud APIs
- Extracted 305,000 words of structured content with topic segmentation and key concept tagging
Business Impact
Created a searchable knowledge base from 233 videos that would have taken weeks to manually review, enabling instant retrieval of investment insights.
This report covers work completed from January to June 2026.
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