DcisionAI
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Financial services beachheads

Certified decisions for the hardest constraints

High-constraint, audit-required decisions in wealth management, private markets, and fund administration, where dashboards describe and copilots suggest, but neither produces proof.

Where We Start: Beachheads

High-constraint, audit-required domains under-served by dashboards and copilots. Financial services is the hardest first market, win here and every subsequent domain is a deployment problem, not a technology problem.

Sample run

RIA / Wealth Management

Multi-account household optimization

Scenario

A household holds $4.2M across taxable, IRA, and Roth accounts. The advisor must allocate across 14 model portfolios subject to client ESG preferences, asset location rules, single-position concentration under 5%, tax-loss carry-forwards, and Reg BI documentation.

What DcisionAI returns

DcisionAI returns the optimal allocation and names the binding constraint, asset location is binding; moving $180K of corporate bonds from taxable to IRA is worth $11,400/year in after-tax yield, and produces the Reg BI rationale automatically.

Why this beachhead

High-frequency, high-stakes decisions with SEC Reg BI audit requirements. Current tools produce allocations, not defensible documented rationale.

Sample run

PE / VC

Capital deployment in private credit

Scenario

A $380M EM private credit fund must deploy $95M across 8 pipeline deals, subject to a 10.5% yield floor, a 40% climate mandate, sector concentration caps, a 4.0 risk ceiling, and a 30% single-deal cap.

What DcisionAI returns

Deploy across 5 deals at 11.5% weighted yield. Three deals excluded with explicit reduced costs. The deployment constraint binds at a shadow price of $0.328 per $1M. A certificate of optimality proves no feasible $95M allocation yields higher.

Why this beachhead

Infrequent but asymmetric decisions over a 10-year fund life, logic today lives in senior partners’ heads, not institutional memory.

Sample run

Fund Administration

Waterfall under structural ambiguity

Scenario

A fund's LPA language is ambiguous on whether carry is deal-by-deal or European. A spreadsheet cannot detect the ambiguity, it produces a number under whichever interpretation was hard-coded.

What DcisionAI returns

The pre-solve gate flags the ambiguity, surfaces both interpretations, and computes LP-level distributions under each, deal-by-deal triggers GP catch-up in year 3; European in year 6. The timing difference is $4.1M across this LP cohort.

Why this beachhead

Rule-bound, spreadsheet-dependent, and audit-required. Calculation errors are legal and reputational risk, not operational friction.

The Wedge

RIA, PE/VC, and fund administration share one substrate, constrained optimization with auditability. Prove the pipeline and seed the context graph in financial services; adjacent domains use the same agents, solver, and graph with new problem types, not new infrastructure.

Beyond financial services

The same six-agent pipeline, discovery, research, model build, solve, explain, applies wherever decisions have binding constraints and audit requirements. Financial services is the wedge; the context graph compounds across every domain.

See a decision with mathematical proof

Walk through a sample run, infeasibility diagnostics, shadow prices, and audit-ready outputs.