From Data to Decisions
Eight actionable initiatives to transform our financial intelligence systems from data collectors into decision engines
Current systems answer simple questions. Sales needs: "Show me all biotech companies in Boston that raised Series B in 2024 AND have former Pfizer executives"
Graph-based RAG system connecting Mars company data → people profiles → deal history → location. Natural language interface for complex multi-hop queries.
Sales can find hyper-targeted prospects 10x faster. Convert hours of manual filtering into seconds of precise results.
Research Foundation: Knowledge graph construction (9 papers), query-specific GNN methods (2 papers), linear graph RAG (3 papers)
We detect agreements AFTER filing. Sales wants to know BEFORE the deal closes. Currently missing 30-90 day windows for early engagement.
Temporal pattern analysis across 301 agreement types. LLM-powered formal contract inference to predict M&A, IPOs, and distress events before public announcement.
First-mover advantage on high-value deals. Get to buyers/sellers 30-90 days before competitors see the filing.
Research Foundation: Formal contract inference (2 papers), prompt robustness (1 paper), temporal prediction models (4 papers)
Mars processes 1.9M news items. Sales manually checks dashboards multiple times per day and still misses time-sensitive opportunities.
LLM-powered agent framework monitoring feeds 24/7. Intelligent filtering for "Tier 1" events (>$100M deals, strategic M&A). Instant Slack/email alerts with context.
Never miss a high-value deal. Sales responds in minutes, not hours. Reduced dashboard fatigue by 90%.
Research Foundation: LLM-powered AI agent frameworks (2 papers), cognitive-aligned models (1 paper), event detection (3 papers)
Mars has 251k people profiles, but bios are unstructured text blobs. Can't search by education, past employers, or board experience.
Layout-aware LLM parsing extracting: education (school/degree/year), employment history, board seats, specialties, certifications. Batch process all 251k profiles.
People-first prospecting. "Show me all Stanford CS grads who worked at Google" becomes possible. Massive value-add for recruiting/BD use cases.
Research Foundation: Layout-aware parsing (1 paper), resume information extraction (1 paper), named entity recognition (4 papers)
Tester processes full year for $80 now. Scaling to 10M events = $1,000+. Mars/M&A/Agreements all face similar cost constraints at scale.
KV cache eviction optimization, prompt compression, and efficient fine-tuning techniques. Batch processing improvements for H200 GPUs (150-180 prompts/sec → 500+).
10x throughput at same cost. Process entire SEC corpus daily instead of weekly. Enables real-time processing for all systems.
Research Foundation: KV cache optimization (2 papers), prompt training (1 paper), model acceleration (3 papers), inference fragility (1 paper)
Legal teams need: "Find all employment agreements similar to this one" or "Show me covenant terms trending in tech M&A." Currently impossible with keyword search.
PostgreSQL + pgvector embeddings for 26M filings. Semantic similarity search across entire corpus. Natural language queries return ranked relevant agreements.
Legal intelligence product. Benchmark terms, find precedents in seconds. New revenue stream from law firms and corporate legal departments.
Research Foundation: Vector search optimization (4 papers), semantic similarity methods (2 papers), document retrieval (5 papers)
Tester detects 30 event types but treats them independently. No portfolio management, risk assessment, or adaptive learning from outcomes.
Multi-agent architecture: Event detection agents, portfolio manager agent, risk agent. Thompson sampling for continuous learning. Bayesian optimization for alpha discovery.
Autonomous trading system. Move from event detection to full alpha generation. Potential licensing to hedge funds/quant firms.
Research Foundation: Multi-agent trading systems (1 paper), Thompson sampling (1 paper), Bayesian optimization (2 papers), time series foundation models (2 papers)
Using generic LLMs (OpenAI, Claude, Grok) for specialized financial tasks. Paying for tokens that understand general knowledge but not our specific domain nuances.
Pre-train or fine-tune smaller models (7B-13B) on 26M SEC filings, 1.9M financial news items, 25k M&A deals. Domain-specific vocabulary for financial entities/events.
50-80% cost reduction. Better accuracy on financial tasks. Own our models = competitive moat. Can license to other fintechs.
Research Foundation: Domain-adapted pre-training (3 papers), prompt flow training (1 paper), multitask finetuning (2 papers)
These initiatives are grounded in 249 recent research papers from arXiv, covering advances in LLMs, knowledge graphs, vector search, and financial AI from 2024-2025.