A use case map identifying where DcisionAI's decision intelligence creates immediate, measurable value across CastleOak's trading, capital markets, and advisory businesses.
Prepared for CastleOak Meeting · May 2026
Firm Profile
CastleOak Securities — Who They Are
At a Glance
Boutique investment bank and broker-dealer, founded 2006 in New York City. 100% independently owned, 92% minority-owned. ~75 professionals across 5 regional offices.
Offices: NYC · ATL · CHI · SF · LA · PDX
Leadership: David R. Jones (CEO), Patrick de Catalogne (EVP, Fixed Income), Robert Bacon (EVP, Capital Markets)
$5T+
Public Offerings
Total assisted across equity and fixed income markets
$4.3B
Avg Daily Volume
Fixed income trading across all product types
670+
Institutional Accounts
Tier I–IV counterparties across the platform
$79B+
M&A Completed
Middle-market advisory transactions closed
Business Lines
Six Distinct Business Verticals — All Optimization-Ready
Proprietary electronic fixed income trading platform with Bloomberg BOLT integration. Institutional liquidity pool.
Investment Banking & Capital Markets
Underwriting, distribution, and structuring across fixed income products for institutional issuers.
Equity Sales & Trading
60+ global markets. Algorithmic strategies. ~$10B notional share repurchase execution per year.
Financial Advisory / M&A
Middle-market M&A under $1B. Fortune 500 divestitures. $79B+ in completed transactions.
Private Capital Advisory
Secondaries advisory, LP/GP liquidity solutions, fund management, and capital raising for alt asset managers.
Pilot #1 — Highest Probability of Sale
Use Case #1: New Issue Allocation Optimization
The Problem
When CastleOak co-manages or underwrites a bond offering, it must allocate bonds across 670+ institutional accounts — simultaneously balancing underwriter economics, relationship fairness, regulatory concentration limits, and syndicate desk preferences. Today, this is done in spreadsheets, introducing both errors and opportunity cost.
Decision Variables
Allocation amount per account per tranche — a classic multi-objective linear/mixed-integer program with hundreds of decision variables and hard constraints on diversification, fill rate, and revenue-weighted priority.
Minimize dissatisfaction from unfilled institutional orders
Diversification
No single account exceeds defined % of deal size
Regulatory Compliance
Syndicate and regulatory rules enforced as hard constraints
Pilot #2 — Clearest ROI
Use Case #2: Share Repurchase Execution Optimization
CastleOak executed ~$10B in notional share repurchases last year for 25+ corporate clients. Each program requires multi-period scheduling to minimize market impact while remaining inside SEC Rule 10b-18 safe harbor provisions — exactly the problem structure DcisionAI has already proven.
Regulatory Constraints (10b-18 Safe Harbor)
Volume Limit
≤25% of ADTV per day, excluding block trades
Timing Windows
Not in last 10 minutes for active NMS stocks
Price Condition
No bid above highest independent bid in the market
Single Broker Rule
One broker per day for open-market purchases
Financial Impact
$2M–$5M/year
Client savings from 2–5 bps execution improvement on $10B notional
"Mathematically optimal execution" — a compelling story for winning corporate mandates
Pilot #3 — Greenfield Opportunity
Use Case #3: LP Secondary Portfolio Selection
The Problem
CastleOak's Private Capital Advisory (PCA) practice, launched March 2025, advises LPs on secondary market sales of fund interests. Each mandate requires selecting the optimal subset of a client's portfolio to sell — maximizing proceeds while maintaining diversification, respecting NAV discount tolerance, buyer capacity limits, and GP consent requirements.
What DcisionAI Solves
Binary sell/hold decision per fund interest — a classic mixed-integer program. DcisionAI finds the mathematically optimal subset, with full constraint transparency for LP clients who want to see the reasoning behind every recommendation.
Why This Wins
Greenfield — No Incumbent
PCA launched March 2025. No legacy tools, no system to displace.
Binary Selection = Proven Model
Sell/hold per fund interest is the exact structure DcisionAI's PE models solve.
Differentiator for LPs
"Mathematically optimal, not judgment-based" — a transparency story that resonates with institutional LPs.
Every Mandate is Custom
DcisionAI makes each PCA engagement faster and more rigorous.
Use Case #4 — Honorable Mention
Use Case #4: Money Market Fund Portfolio Construction
CastleOak Shares (COSXX, CASXX) are branded money market products that must construct portfolios maximizing yield while maintaining full SEC Rule 2a-7 compliance. DcisionAI solves this as a constrained security selection problem — automatically enforcing all regulatory parameters as hard constraints while optimizing for yield.
SEC Rule 2a-7 Hard Constraints
WAM ≤ 60 Days
Weighted average maturity limit enforced at every rebalance
WAL ≤ 120 Days
Weighted average life constraint across the full portfolio
Daily Liquidity
Minimum daily liquid asset percentage maintained
Issuer Concentration
No single issuer exceeds defined % of fund NAV
Why It's Compelling
Branded Products
COSXX and CASXX — any yield improvement is directly attributable to CastleOak
Removes Manual Compliance
Eliminates manual 2a-7 checking; enables dynamic rebalancing as rates shift
State Street Platform
DcisionAI optimizes the portfolio that trades on State Street Fund Connect
Why DcisionAI
The Fit Is Structural, Not Generic
DcisionAI's architecture was built for exactly the problems CastleOak faces every day — constrained optimization at speed, with regulatory hard constraints that can never be violated.
Speed at Scale
Solver runs in <100ms for inventory-size problems. $4.3B/day of trading decisions demand real-time answers, not overnight batch runs.
Regulatory Hard Constraints
Domain cards with cited regulatory references — SEC 15c3-1, 10b-18, Rule 2a-7. Hard constraints are never relaxed, never violated.
No OR PhD Required
Boutique firms can't hire quant optimization teams. DcisionAI's natural language interface lets traders describe problems — the engine formulates and solves them.
Proven Multi-Period Engine
7-year Roth conversion with 14 variables solved optimally — the same architecture handles 30-day repurchase schedules with 10b-18 compliance windows.
"Our execution is mathematically optimal, not rule-of-thumb." — The story CastleOak tells institutional clients with DcisionAI powering the desk.
Recommended Pilot
Start Here: New Issue Allocation Pilot
Why This Use Case First
1
Weekly Frequency, Immediate Data
40+ new issues in the last 6 months — CastleOak can run a retroactive test against real allocations immediately.
2
Spreadsheet-Based Today
No allocation optimization tool in place. Zero incumbent to displace.
3
670 Accounts = Classic MILP
Revenue-weighted priority, fill rate, diversification, and regulatory constraints — a textbook LP/MILP problem.
4
Low Risk, High Signal
Wrong allocation doesn't lose money — it just leaves relationship value on the table. Safe to test.
Pilot Structure
Scope
Run DcisionAI against CastleOak's last 3 actual new issue allocations — same orders, same account tiers, same constraints.
Measurement
Fill rate improvement · account satisfaction delta · relationship value left on the table