What WestStack builds
AI solutions for wealth managers and family offices
These are practical examples of the systems we can scope and build. They are not generic AI demos or assembled no-code workflows — each is bespoke, custom software your firm owns, engineered around private data, real financial-services workflows, human oversight, and measurable business value.
I The systems
№ 01
Private Firm Knowledge Assistant
- Problem
- Policies, research, client notes, planning documents, and investment memos live across too many systems.
- What we build
- A secure internal assistant that searches approved firm knowledge, synthesizes answers, and respects permissions.
- Example workflow
- An advisor asks, “What do we already know about this family entity structure?” and gets a sourced summary from internal material.
- Why it matters
- Less time hunting for context, faster onboarding, and fewer repeated questions across operations and advisory teams.
- Related reading
- How a personal AI infrastructure model gives a firm knowledge assistant lasting memory
№ 02
Advisor Meeting-Prep Assistant
- Problem
- Preparing for client meetings requires manually pulling CRM notes, prior decisions, portfolio context, open tasks, and planning history.
- What we build
- A briefing workflow that assembles relevant context into a pre-meeting summary with open issues, likely questions, and follow-up prompts.
- Example workflow
- Before a quarterly review, the advisor receives a concise brief covering prior meeting notes, planning topics, portfolio changes, and next actions.
- Why it matters
- Advisors spend more time advising and less time assembling context from disconnected systems.
№ 03
Compliance-Aware Content Review
- Problem
- Client-facing emails, commentary, marketing copy, and advisor notes need review against firm policy and approved language.
- What we build
- A controlled review assistant that flags risky language, suggests compliant alternatives, and routes edge cases to a human reviewer.
- Example workflow
- An advisor drafts a market update; the assistant checks claims, disclosures, tone, and prohibited phrases before compliance review.
- Why it matters
- Faster review cycles with better consistency and a clearer audit trail for human oversight.
№ 04
Research & Deal Memory System
- Problem
- Investment research, diligence notes, manager conversations, and deal context are hard to reuse once they scatter across folders and inboxes.
- What we build
- A research memory layer that captures, tags, searches, and summarizes institutional knowledge across approved sources.
- Example workflow
- A CIO asks what the firm previously concluded about a manager, sector, or diligence question and gets a grounded summary with source links.
- Why it matters
- Better continuity, less duplicated diligence, and faster access to the firm’s own investment thinking.
№ 05
Investment Memo & RFP Drafting Assistant
- Problem
- Investment memos, IC packets, client commentary, and RFP responses take hours to assemble from research, data, notes, and approved language.
- What we build
- A drafting assistant that turns approved sources, diligence notes, portfolio data, and prior responses into structured first drafts with citations and human review.
- Example workflow
- An investment team uploads diligence notes and approved materials; the assistant drafts an IC memo or RFP response section, flags missing inputs, and links back to sources.
- Why it matters
- Faster document production with more consistent structure, reusable firm language, and less manual copy/paste across sensitive materials.
№ 06
Workflow Automation Agent
- Problem
- Document-heavy handoffs, data collection, task routing, and recurring operational processes consume staff time and create avoidable errors.
- What we build
- A human-in-the-loop automation that reads inputs, prepares outputs, updates systems, and escalates exceptions.
- Example workflow
- Operations receives a document packet; the agent extracts key fields, checks completeness, drafts follow-up tasks, and queues exceptions for review.
- Why it matters
- More operating leverage without removing judgment from sensitive financial workflows.
№ 07
Local / Hybrid AI Infrastructure
- Problem
- Some workflows are too sensitive, expensive, or operationally important to depend entirely on public AI APIs.
- What we build
- A hybrid architecture that routes sensitive or high-volume work to private/local models and uses cloud models where appropriate.
- Example workflow
- Client-identifiable context stays inside the firm’s controlled environment while lower-risk tasks can use managed cloud models.
- Why it matters
- Better privacy control, resilience, and cost management for production AI workflows.
- Related reading
- Why a hybrid local/cloud LLM architecture is becoming an enterprise necessity
II Example first builds
Representative scenarios, not claimed client case studies — how a narrow, production-ready first build could be scoped before expanding to a larger program.
i.Multi-family office knowledge brain
Firm contextA family office team manages entity structures, planning docs, reporting rules, and recurring operational details for complex families.
First buildA permissioned knowledge assistant that helps staff retrieve entity context, prior decisions, reporting logic, and planning history from approved internal sources.
Success measureReduced research time, faster staff onboarding, and fewer repeated questions across the service team.
ii.RIA advisor-prep workflow
Firm contextA growing RIA has advisors spending hours before meetings gathering CRM notes, portfolio context, prior recommendations, and planning tasks.
First buildA meeting-prep assistant that creates a sourced briefing packet and flags open issues before each review.
Success measurePrep-time reduction, advisor adoption, and improved consistency in client meeting follow-through.
iii.Compliance-safe content review
Firm contextA wealth firm wants advisors to move faster on client communications without increasing compliance risk.
First buildA review assistant that checks drafts against firm policy, disclosure rules, and approved language before human review.
Success measureFaster review cycles, fewer avoidable edits, and a clearer review trail.
iv.Investment memo and RFP drafting
Firm contextAn investment or OCIO team repeatedly prepares manager diligence summaries, IC packets, client commentary, and RFP responses from similar source material.
First buildA drafting workflow that pulls from approved research, prior responses, portfolio context, and firm language to create sourced first drafts for human review.
Success measureDrafting time saved, source coverage, review quality, and consistency across recurring documents.
Correspondence
Not sure which solution fits?
That is exactly what the AI Opportunity & Workflow Audit is for. We map the workflows, evaluate data and privacy constraints, prioritize the best opportunities, and recommend one narrow, production-ready first build with clear scope and success metrics.