Key takeaways (impact)
- Objective benchmarking for advisors: Advisors get clear percentile based positioning, module scores, and strength and weakness profiles, without subjective interviews.
- Compliance safe by design: Scoring is fully deterministic, and generative AI is not used in the scoring algorithm, which preserves transparency and regulatory credibility.
- Faster iteration on assessment quality: AI is used upstream to help experts generate and refresh questions, modules, weights, insights, and report templates as markets evolve.
- Actionable next steps, not just a score: After deterministic scoring, the platform generates context driven action plans grounded in an expert knowledge base.
The problem
Choosing a financial advisor is harder than it should be. Regulations limit what advisors can advertise, the industry lacks standardized evaluation frameworks, and high net worth families often default to trust, referrals, or superficial signals.
That opacity creates four gaps: clients cannot reliably compare advisors, advisors cannot benchmark against peers, firms lack a consistent improvement framework, and matching families to advisors becomes guesswork.
The key idea
Point93 is built on a simple principle: the evaluation must be deterministic and benchmark aligned, and AI should help design the assessment, not evaluate the people taking it.
Our solution
Point93 is a structured, multi module self assessment that measures an advisor across capabilities, philosophy, operations, and stewardship, then compares results against peers and expert derived best practices.
The system sits on four pillars: expert knowledge ingestion, AI assisted questionnaire creation, deterministic scoring, and a comprehensive reporting engine.
Architecture overview
1) Expert knowledge as the foundation
Point93 starts with practitioner expertise. An experienced advisor provided frameworks, evaluative guidelines, scoring philosophies, operational best practices, risk and compliance considerations, and service quality indicators that form the backbone of the assessment model.
This corpus is processed into a semantic RAG pipeline using vectorization and dot product retrieval, optimized for high precision recall of expert principles when questions and modules are created or refined.
2) AI assisted assessment creation (upstream, expert controlled)
The questionnaire spans 17 modules, each with 30 to 40 questions, using multiple formats, including multiple choice, rating scales, free form responses, Likert style questions, and scenario based selections.
AI is used heavily in creation to generate initial and replacement questions, update modules, propose scoring weights and point allocation, and produce insight areas, report structures, and feedback templates.
Crucially, this is expert supervised, and knowledge is sourced from the partner advisor, not the public internet.
3) Deterministic scoring and benchmarking (no generative AI in scoring)
Once an advisor completes the assessment, Point93 applies a fully deterministic scoring engine with defined weights, validated scoring logic, proficiency thresholds, benchmarks from expert knowledge, and comparative markers from peer data.
Outputs include percentile rankings, module level scores, benchmark comparisons, peer charts, weighted aggregate scores, and strength and weakness profiles.
No part of the scoring algorithm involves generative AI, which is a deliberate credibility and regulatory safety decision.
4) Reporting that is usable, not just “data”
After scoring, advisors receive a detailed report delivered digitally and via email, with radar charts, bar graphs, percentiles, peer overlays, benchmark maps, narrative insights, action items, and strength and risk zones.
5) AI generated action plans (the only end user facing AI)
After deterministic scoring is complete, AI uses the advisor’s results plus peer averages and benchmarks to propose concrete improvements across operations, strategy, communication, portfolio management, and practice management, grounded in the expert knowledge base.
How it works, end to end
- Experts shape the evaluation foundation: Partner advisor knowledge is ingested into the RAG knowledge base.
- Admins iterate the assessment quickly: When creating or refining modules, RAG retrieves the most relevant expert principles, then AI helps draft questions, weights, and templates.
- Advisors complete the assessment: 17 modules, 30 to 40 questions each, mixed formats for higher fidelity.
- Deterministic scoring runs: Transparent, repeatable scoring and benchmarking, producing percentiles and comparisons.
- Report plus action plan is delivered: Visuals, narrative insights, and AI generated improvement plans.
Results and early value
In early usage, the platform delivered clear benchmarking, visibility into operational blind spots, a structured improvement path, and professional grade reports for advisors.
For firms, it provided a standardized evaluation framework, training and quality improvement tooling, identification of top performers and outliers, and consistent onboarding evaluations.
Lessons learned
- Deterministic evaluation is essential in regulated industries, since compliance and credibility depend on transparent logic.
- Quite simply, if there isn’t a clear need for AI, don’t use it. AI belongs upstream in assessment design, not inside the scoring engine.
- Expert knowledge beats generic internet data for credibility and relevance.
- Mixed question types improve fidelity beyond MCQs alone.
What’s next
Point93 is designed to evolve into a marketplace for advisor family matching, including AI driven matching, expanded scoring dimensions, reassessment tools, firm level integrations, and enhanced benchmark models.
Point93 was engineered to make advisor evaluation transparent, fair, and future ready by combining expert grounded assessment design with deterministic scoring, benchmarking, and actionable reporting.
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