Household, portfolio and interaction data brought into one governed picture, ready for advisors, AI and auditors alike.
- Client and portfolio data integration
- Governed, AI-ready data modeling
- Access control and audit readiness
Assistants that prepare, summarize and draft for advisors, with every output cited and the advisor making every client call.
- Meeting prep and briefing generation
- Cited answers from governed sources
- Advisor review on every client-facing output
Research assistants grounded in your sources, with every answer traced to where it came from, at review-passing standard.
- RAG over research, filings and internal sources
- Domain-tuned models for financial language
- Cited, auditable responses by default
Agentic analysis across scattered reports and documents, turning reading backlogs into decisions with sources attached.
- Document understanding across unstructured reports
- Orchestrated analysis, validation and synthesis
- Self-checked outputs before anyone sees them
One governed home for the rules that apply to you, with change detected and its impact surfaced before deadlines.
- Rules library architecture, structured plus vector
- Change tracking across regulatory sources
- Impact mapping and alerts before deadlines
Client documents extracted, validated and routed at scale, cutting the paperwork between a yes and a funded account.
- Document extraction and validation at scale
- Onboarding workflow automation
- Exception routing to human reviewers
Decision Intelligence · Delivered in Enterprise Advisory · Agentic CIO Advisor
The Challenge
- Enterprise data scattered across many reports
- Limited access to senior-level expertise
- Manual analysis slowing strategic decisions
Our Approach
- Document understanding across unstructured reports
- RAG grounding against an enterprise knowledge base
- An orchestrator running analysis, validation and synthesis
- A reflection agent self-checking every output
Outcomes
Research AI · Delivered in Pharma · Intelligent Research Assistant
The Challenge
- Massive specialist literature across sources
- Time-consuming manual research across resources
- Regulatory grade content required
- Limited traceability and explainability
Our Approach
- Intelligent processing with OCR, parsing and summarization
- RAG with vector and semantic search
- A domain-tuned LLM for specialist language
- Agent orchestration across research sources
Outcomes
Regulatory Intelligence· Delivered for a Compliance Leader · Rules Library and Reg-Change Tracking
The Challenge
- No internal store of federal and state rules
- Regulatory change tracked manually, after the fact
- Costly reliance on third-party software
Our Approach
- A canonical rules library, structured plus vector
- 100+ sources tracked, changes diffed and versioned
- Impact mapped with alerts before deadlines
Outcomes
Production-Grade Delivery
Human in the Loop, Always
Trust as a Design Input
Production, Not Pilots
Discover
Design
Build
Run & Scale
No. Every client-facing judgment belongs to an advisor; the system prepares, cites and drafts so the advisor walks in ready.
They are built for it: grounded answers with citations and audit trails, so review checks sources instead of hunting for them.
Client data stays in your environment under your access controls, with household-level separation designed into the data model.
The full trail: what was retrieved, what was drafted, what the advisor changed and who approved it.
Adoption is designed rather than hoped for: the copilot lives inside the advisor’s existing workflow and earns trust by showing its sources from day one.
