What We Do

Offerings

Boutique AI and data offerings built for financial services: senior-led, AI-augmented, and delivered end to end.

Strategy

AI Strategy

From a prioritized roadmap to an efficient operating cost base. Strategy that produces executable outcomes.

AI Use Case Prioritization & Roadmap

A structured approach to identify, prioritize, and implement AI use cases with proper governance.

~6 weeks · Strategy to roadmap

Start small, scale smart.

Begin with 2 to 3 low-risk use cases to validate the framework. Prove value quickly, then expand with confidence.

Our Approach

  1. 1

    Curated Use Cases

    We arrive with a library of industry-specific AI use cases so you're not starting from scratch.

  2. 2

    Readiness Assessment

    Evaluate organizational and use-case-level readiness across data, tech, people, and governance.

  3. 3

    Value & Feasibility

    Score each use case by business impact and ease of implementation to focus on what matters.

  4. 4

    Prioritize & Roadmap

    Sequence use cases into a phased plan with quick wins first, then scale.

  5. 5

    Risk & Governance

    Embed AI risk controls, policies, and audit-ready governance from day one.

Agentic AI Cost Optimization

Reduce AI operating costs 40–60% while improving governance and performance. Typical payback in under 3 months.

2 weeks · Rapid diagnostic
4 weeks · Standard assessment

The Challenge

Financial services firms overspend on AI by defaulting to premium models for every task, allowing vendor sprawl across business units, and duplicating data pipelines, compounding costs that erode ROI. 60–80% of AI requests are routine but get routed to premium models.

Our Approach

Model-Use Case Alignment

Audit current and planned AI use cases, benchmark task complexity, and recommend model routing strategies. Use Haiku for extraction, Sonnet for analysis, Opus for complex reasoning. Each step runs on the most cost-efficient model that meets quality thresholds.

40–60% reduction in API costs

Vendor & Platform Rationalization

Catalog all AI vendor contracts, API agreements, and platform licenses across the firm. Identify redundancy where multiple teams pay for overlapping tools and recommend consolidation paths: fewer vendors, shared infrastructure.

20–30% reduction in redundant spend

Data Architecture for AI Efficiency

Trace data lineage into AI workflows to map where data is sourced, duplicated, embedded, and stored across initiatives. Recommend shared infrastructure: centralized vector stores, common data prep pipelines, single-source-of-truth architecture.

15–25% reduction in storage & retrieval costs

What the Client Gets

Model Routing Matrix

Use-case-to-model mapping with cost projections

Vendor Landscape & Roadmap

Current state, target state, consolidation plan

AI Data Flow Assessment

Duplication analysis and shared infra recommendations

Cost Governance Framework

Policies, review cadence, and decision rights

Execution

AI Implementation

From proven concept to production: turning prioritized use cases into working systems.

AI Use Case Implementation

Turn AI strategy into production-ready solutions, from proof of concept through deployment, integration, and ongoing optimization within your existing infrastructure.

4–6 weeks · 3–4 use cases

Our Approach

1

Proof of Concept

Stand up a working prototype against real data. Validate accuracy, latency, and user experience before committing to production.

2

Deploy

Production-grade deployment with the right model routing, observability, and cost controls from day one.

3

Integrate

Wire AI into existing workflows, data platforms, and systems of record so users get value inside the tools they already use.

4

Optimize

Continuous tuning on prompts, models, and workflows as usage scales and the business learns what works.

Implementation is the natural follow-on to AI Use Case Prioritization & Roadmap. Senior-led delivery, no junior handoffs. The same people who shape the strategy build it.

Change Management

Operating Model & Change Management

The roles, playbooks, and training that turn AI adoption from pilot into habit.

AI Education, Training & Hackathons

From executive education to hands-on enablement across your organization and portfolio companies.

1/2 day to 1 day

AI Governance Training

Executive-level workshops that move AI risk and governance from theory to boardroom-ready action.

  • Executive workshop on AI risk management
  • Risk appetite and adoption strategy for deal and investment teams
  • Mapping AI workflow risks and controls
  • Post-deployment risk assessment and controls audit
A few hours to a few days

AI Hackathon Design & Facilitation

Turn experimentation into actionable outcomes. We design, run, and synthesize high-impact AI hackathons end to end.

  • Challenge themes designed around your workflows
  • Starter kits with prompt templates and tool access
  • Keynote on AI: what's working, what's hype
  • Roaming facilitation and judging panel
  • Post-event synthesis: top ideas, feasibility, and next steps
1/2 day per function

Function-Centric Training Program

Role-specific AI education tailored to how teams actually work, not generic slideware.

  • Tailored sessions by function: finance, operations, sales, HR, legal
  • Hands-on workshops, not slides and lectures
  • Custom prompt libraries per department
  • AI Champions program to sustain adoption internally
  • Adoption metrics dashboard and monthly check-ins
1/2 day to 1 day

Tool-Centric Training Program

Platform-specific enablement across your AI stack, so teams know when to reach for which tool and how to use it safely.

  • Claude, ChatGPT, Copilot: when to use which and why
  • Workflow automation with AI agents and integrations
  • Data privacy and security best practices per tool
  • Building effective prompts for domain-specific use cases
  • Evaluating and onboarding new AI tools safely

Ready to start?

Senior-led engagements, right-sized by design. Let's discuss where AI can move the needle for your firm.

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