The AI-Native Investment Desk
A Multi-Agent Research, Risk, and Execution Platform for Asset Managers, Family Offices, and Bank Treasuries
A Wohlig Transformations Whitepaper
Executive Summary
The buy-side data and workflow stack — Bloomberg, Refinitiv, Capital IQ on top, FactSet in the middle, scattered Python notebooks at the bottom — is structurally a 1990s product retrofitted with cloud features. It is expensive (₹15–30 lakh per seat for the senior tier), siloed, and fundamentally a “dashboard for humans to do the work themselves.”
A new generation of multi-agent AI architectures changes what is possible. By combining a Bloomberg-style desktop terminal, a fleet of specialized analyst agents, 100+ real data connectors, broker connectivity, and audit-grade logging, Wohlig builds AI-native investment desks that compress days of analyst work into minutes while running entirely inside the customer’s perimeter.
This paper outlines the capability, architecture, governance layer required to operate it on real money, and engagement model.
1. The Problem with the Buy-Side Stack Today
Vendor cost — Bloomberg / Refinitiv / Capital IQ at ₹15–30L/seat per year.
Workflow fragmentation — 5–10 tools per analyst — research, screener, risk, OMS, journals.
Research bottleneck — Analyst teams cover a fraction of the universe; ideas die in the queue.
Discretionary risk discipline — Sizing and exposure checks skipped under time pressure.
Idea-to-execution lag — Days from thesis to sized order; missed entry windows.
Audit deficit — No comprehensive record of why a position was taken.
2. The Pattern: An AI-Native Desk
2.1 A native desktop terminal
A cross-platform, branded desktop application — not a browser tab — that provides a Bloomberg-class cockpit for analysts and PMs. Real-time market data, charting, news, screening, position view, and agent interaction in one window.
2.2 A multi-agent fleet
Specialized agents for the work that humans currently do:
Director / Strategy — frames the question, decomposes into tasks.
Research — pulls filings, earnings calls, sell-side notes.
Quant — technical, statistical, factor, and backtest analysis.
Macro — interest rates, GDP, credit, monetary policy.
On-chain / Sentiment — for crypto and retail-driven names.
Risk — sizing, exposure, drawdown, scenario.
Execution — order routing, paper trading, broker integration.
Persona panel — bull / bear / Buffett / Graham / Lynch personas for adversarial review.
2.3 100+ real data connectors
Equities, crypto, futures, FX, macro (FRED, IMF, World Bank, DBnomics), alternatives (maritime, satellite, geopolitical), corporate filings, earnings call transcripts, on-chain whale flows, multi-venue crypto market data via CCXT.
2.4 Broker connectivity
Real and paper trading via Zerodha, Angel One, Alpaca, IBKR, plus 12+ additional venues. Strategy export to TradingView Pine v6, MT5, and TDX for hybrid workflows.
2.5 Persistent memory and bias detection
Every analyst’s decisions, theses, and outcomes feed a persistent trade-journal layer. The platform identifies systematic biases (over-trading, anchoring, loss aversion patterns) and surfaces them in weekly reviews.
2.6 Audit-grade logging
Every agent decision, every data pull, every order — recorded with inputs, intermediate reasoning, outputs, and timestamps. Replayable. Regulator-ready.
3. Reference Architecture
┌──────────────────────────────────────────────────────────────┐
│ Native Desktop Terminal (Customer Branded) │
│ │
│ Charting · News · Screener · Positions · Agent Workspace │
└──────────────────┬───────────────────────────────────────────┘
│
┌──────────┴──────────────────────────────────────┐
│ Agent Orchestrator │
└──────────┬──────────┬──────────┬──────────┬─────┘
│ │ │ │
┌──────▼─┐ ┌──────▼─┐ ┌──────▼─┐ ┌──────▼─┐
│Research│ │ Quant │ │ Risk │ │ Exec │
└──────┬─┘ └──────┬─┘ └──────┬─┘ └──────┬─┘
│ │ │ │
┌──────────▼──────────▼──────────▼──────────▼────┐
│ Tooling & Data Layer │
│ │
│ 100+ Connectors · QuantLib · Backtests · MCP │
└──────────────────────┬─────────────────────────┘
│
┌──────────────────────▼─────────────────────────┐
│ Governance, Risk & Compliance Layer │
│ Pre-trade checks · Kill-switch · Audit log │
│ RBAC · Four-eyes · Model risk mgmt (SR 11-7) │
└──────────────────────┬─────────────────────────┘
│
┌────────────▼────────────┐
│ Broker / OMS / PMS │
└─────────────────────────┘
Deployed inside the customer’s cloud, on-prem, or hybrid. Identity through customer SSO. Secrets in customer secret manager. AI inference can be routed to a customer-controlled model endpoint.
4. The Wohlig Productionization Layer
Open-source agent frameworks ship without the controls a regulated desk requires. Wohlig adds:
Model Risk Management — Aligned to SR 11-7, RBI/SEBI expectations. Documented model inventory, validation, monitoring.
Pre-trade risk and kill-switches — Hard limits on size, sector, exposure, drawdown. Automated halt on anomaly.
Best-execution audit — Order-routing rationale captured for every fill.
PII and data-residency controls — All data and inference inside customer perimeter.
Role-based access and four-eyes approval — High-risk theses and orders require dual sign-off.
LLM output guardrails — Respect SEBI IA / RA boundaries; suppress unauthorized advice patterns.
Immutable audit trails — Append-only logs for every agent decision and order.
Evaluation harness — Quality scoring on a held-out test set before any prompt or model change.
This layer is non-negotiable for any deployment that touches real capital, and it is where Wohlig’s experience makes the difference.
5. Outcomes Customers Can Expect
3–5x research throughput per analyst.
30–60% reduction in vendor data and terminal spend through feed consolidation.
Idea-to-execution time compressed from days to hours.
Improved risk discipline — every order pre-checked against exposure rules.
Audit and regulator readiness out of the box.
Bias-aware analyst development — measurable improvement in decision quality over time.
6. Engagement Model
Phase A — Foundation (6–8 weeks). Stand up the desktop terminal, data connectors, agent orchestrator, and governance layer in a paper-trading environment. Train two analysts.
Phase B — Pilot (8–12 weeks). Run real research with the platform alongside the existing stack. Validate quality, audit trails, and risk controls. Begin small live-money cohort if appropriate.
Phase C — Scale (ongoing). Roll out to the full investment team. Expand asset coverage, agent specialization, and broker connectivity. Wohlig stays as managed service or advisor as the customer prefers.
7. Target Customers
Asset managers and AIFs (PMS, Cat-III hedge funds) in India and SEA.
Family offices and UHNI desks.
Crypto-native funds and prop desks.
Fintech brokers and wealth platforms.
Bank treasury, private banking, and wealth management.
Sell-side research and equity-research boutiques.
8. About Wohlig
Wohlig Transformations is a digital transformation, cloud, and AI consulting firm founded in 2016, with offices in India and London. We have shipped 10+ generative-AI solutions in production, completed 20+ cloud migrations, and hold 40+ Google Cloud certifications. We serve governments (Maharashtra, Gujarat, ONDC), enterprises (Lodha, Eros Now, Hungama), and high-growth consumer companies (Swiggy, Ninjacart, PW Live).
Web: www.wohlig.com.
To discuss your investment-desk modernization, reach Wohlig at chintan@wohlig.com.