Oakstone AI OS
Modern Operating Architecture
The internal 'operating system' of Oakstone Capital—a coherent layer connecting data, tools, and workflows to augment institutional intelligence.
Design Philosophy
Augmenting Institutional Intelligence
Oakstone AI OS is built to 10x analyst productivity while ensuring data-driven, repeatable, and explainable decision-making.
One Coherent Layer
Better Decisions
Institutional Memory
Scalable Delivery
AI OS Core
Intelligence Engine
The Blueprint
5-Layer Architecture
01
Experience Layer
Front-end interfaces and specialized AI agents that serve as the primary touchpoint for users.
- Chat-style assistants
- Dashboards & portals
- IC & Partner tools
02
Workflow Layer
Orchestration pipelines managing deals, mandates, reports, and internal approvals flow.
- Deal/Mandate pipelines
- Task routing
- Approval workflows
03
Intelligence Layer
The cognitive core utilizing LLMs, machine learning, and quantitative risk engines.
- LLMs & ML Models
- Scoring engines
- Rules-based logic
04
Data Layer
Unified data spine connecting proprietary infrastructure with external market datasets.
- Crowdax + Impala Data
- RAG/Embeddings
- External API feeds
05
Integration Layer
Secure connectors to external tools, cloud storage, and on-chain transactional data.
- CRM/ERP connectors
- Cloud storage sync
- Audit & logging
Internal Capabilities
What AI OS Delivers
Knowledge Intelligence
Semantic search across central document indexes including IMs, decks, and contracts.
One-click IC Memo drafting
Version-aware tagging
Metadata extraction
Deal & Mandate Co-Pilot
Specialized modes for Credit, Venture, and Infrastructure deal analysis and scoring.
Automated executive summaries
Risk/Mitigant analysis
Valuation modeling
Financial Analytics
Model reading capabilities that explain logic, suggest sensitivities, and identify anomalies.
Scenario narratives (Base/Downside/Upside)
Sensitivity analysis
Automated QA
Market & Macro
Powered by Impala Market feeds for yield curves, GDP, inflation, and turnover analytics.
Dynamic market briefs
Reinvestment risk analysis
Yield curve models
Credit & NPL Intelligence
Predictive ML models for credit risk scoring and early warning signs of distress.
Repayment history analysis
Risk clustering
NPL probability scoring
Climate & ESG
Analysis of climate risk assessments, NDCs, and bankable infrastructure projects.
Impact pathway mapping
PGESI reporting
ESG indicators
Internal Ecosystem
Specialized Mini-Apps
The OS ecosystem contains a set of purpose-built mini-applications tailored to specific investment and advisory workflows.
DealRoom AI
Deal intake, parsing, scoring, and memo generation.
ModelGuard
Financial model reading, QC, and scenario suggestions.
MacroLens
Macro and bond market AI assistant (Impala).
CreditBrain
Credit scoring and NPL prediction for private credit.
VentureRadar
Startup intelligence and portfolio monitoring.
ReportBot
Automated LP letters, board packs, and impact reports.
Evolution
Implementation Roadmap
Phase 1: Foundation (0–6 Months)
KnowledgeHub & Core Data Spine
Establishing Crowdax data infrastructure, RAG search across firm documents, and initial DealRoom AI parsing modules.
Phase 2: Deep Integration (6–18 Months)
Intelligence Deepening
Integrating Impala Market data (MacroLens), deploying PhD-driven credit scoring (CreditBrain), and automated reporting suites.
Phase 3: Externalisation & Scale (18–36 Months)
Client-Facing Infrastructure
Launching LP dashboards, ClimateDesk for regional partners, and integration of on-chain transactional data via Boolean.
