What a Financial Planning Software actually does
Augments RIA workflows with LLM-generated client meeting summaries, plan narratives, and bounded Q&A — without crossing into SEC-prohibited 'AI investment advice' territory.
The system ingests client brokerage-statement PDFs (parsed by GPT-5.4 nano), exports from MoneyGuidePro or eMoney, and feeds them through a Claude Sonnet 4.6 pipeline that generates plan-narrative sections and meeting action items. Every AI input and output is captured in an immutable audit log to satisfy 17 CFR §275.204-2 five-year recordkeeping requirements. The 'informational, not advice' boundary is enforced in the system prompt and surfaced on every client-facing output.
The SEC's March 2024 AI-washing settlements — Delphia ($225K) and Global Predictions ($175K) under Advisers Act §206 and Marketing Rule 206(4)-1 — redrew the line for any RIA-adjacent AI product. In 2026, the proposed Predictive Data Analytics rule (still not final as of June 2026) adds further supervisory obligations. The market gap is real: no white-label SaaS for small RIAs exists because the compliance burden is too high for a SaaS vendor to abstract away; this is why hire-agency dominates for anyone past proof-of-concept.
AI capabilities involved
Client meeting summary + action-item extraction
Brokerage statement + 1099 document OCR and extraction
Plan-narrative generation from MoneyGuidePro/eMoney exports
Risk-tolerance questionnaire scoring with explanation
Bounded client-portal Q&A (informational, not advice)
Who uses this
- Small RIA principals (1–20 advisor firms) seeking branded client-portal AI augmentation
- BD/RIA enterprise-tech buyers evaluating an AI co-pilot under their own brand
- Independent wealth management agencies adding AI narrative drafting to eMoney or MoneyGuidePro workflows
- FinTech founders building an advisor-productivity layer that sells into RIA networks
SaaS alternatives on the market
Real products you can sign up for today — with current 2026 pricing, honest pros and cons.
eMoney Advisor
Mid-to-large RIA firms that need deep custodian connectivity and are comfortable under the eMoney brand.
~$3,600/yr per advisor
Pros
- +Fidelity-owned, deep custodian integrations and data aggregation.
- +Financial planning scenario engine with Monte Carlo simulations.
- +Client portal with account linking included.
- +Established compliance-reviewed workflows.
Cons
- −No white-label or reseller tier — advisors sell under the eMoney brand.
- −Pricing is per-advisor at enterprise scale, not per-end-client.
- −AI features are limited to eMoney's own roadmap; no custom prompt control.
- −Fidelity ownership creates conflict-of-interest optics for some independent RIAs.
MoneyGuidePro
Solo and small-team RIAs wanting solid goal-based planning at under $2K/yr.
~$1,295/yr per advisor
Pros
- +Goal-based planning with 'Play Zone' what-if scenarios.
- +Envestnet ecosystem integration for enterprise use.
- +Strong adoption among fee-only RIAs.
- +Lower price point than eMoney.
Cons
- −No white-label tier; Envestnet ownership limits independence.
- −AI features are basic narrative templates, not LLM-generated.
- −No API access for custom integrations without enterprise agreement.
- −Client-portal branding is MoneyGuidePro's, not yours.
RightCapital
Price-sensitive solo advisors or emerging RIAs who need core planning without paying eMoney rates.
$125–$185/mo per advisor
Pros
- +Lowest price point in the category with solid planning engine.
- +Good tax-optimization and student-loan planning modules.
- +Faster onboarding than eMoney.
- +Modern UI favored by younger advisor clients.
Cons
- −No white-label tier; advisor clients see the RightCapital brand.
- −No LLM-based AI features; planning is rule-based only.
- −Limited custodian integrations compared to eMoney.
- −Less enterprise depth for complex planning cases.
The AI stack
Production financial planning AI requires four layers: document ingestion, narrative generation, client-portal Q&A, and an immutable compliance audit trail. The compliance layer — not the AI API cost — is the expensive part of the build.
Document extraction (OCR)
Parse brokerage statements, 1099s, and eMoney/MoneyGuidePro PDF exports into structured data for the planning engine.
GPT-5.4 nano
$0.20/$1.25 per M tokensStandard brokerage-statement parsing where tables are single-column or clearly separated.
Claude Haiku 4.5
$1/$5 per M tokensComplex multi-page statements where structured JSON output quality matters more than cost.
Our pick: GPT-5.4 nano for standard statement extraction; upgrade to Haiku 4.5 if document complexity requires consistent structured output.
Plan narrative generation
Generate client-ready plan narratives, meeting summaries, and action-item lists from structured planning data.
Claude Sonnet 4.6
$3/$15 per M tokensAll client-facing narrative generation where audit-log explainability and accurate hedging language are mandatory.
GPT-5.4 mini
$0.75/$4.50 per M tokensInternal advisor-facing summaries where the output is not client-visible and compliance review is manual.
Our pick: Claude Sonnet 4.6 for all client-facing outputs. GPT-5.4 mini is acceptable for internal advisor notes with added system-prompt guardrails requiring mandatory human review before sharing.
Client-portal Q&A (RAG)
Answer client questions about their plan, bounded strictly to their own uploaded documents and the 'informational, not advice' constraint.
Claude Sonnet 4.6 + text-embedding-3-small RAG
$3/$15 per M tokens + $0.02/M for embeddingsClient-facing Q&A where a hallucinated account balance or tax figure is a direct regulatory liability.
GPT-5.4 mini + text-embedding-3-small
$0.75/$4.50 per M + $0.02/M embeddingsHigh-volume portals where the cost of Sonnet is prohibitive and the system prompt is rigorously tested.
Our pick: Claude Sonnet 4.6 with ZDR routing for all client-facing Q&A. The ~$0.014/query cost is immaterial versus the compliance risk of a cheaper model that hallucinates financial figures.
Compliance audit trail
Capture every AI input and output in an immutable append-only log to satisfy 17 CFR §275.204-2 five-year retention and FINRA Rule 3110 supervision.
Supabase append-only table + RLS
$25/mo Supabase ProSmaller RIAs (under 100 advisors) who need functional audit logging without enterprise infrastructure costs.
AWS S3 Object Lock (WORM) + DynamoDB
$50–$150/mo at typical document volumesRIAs with existing AWS relationships or SEC-exam experience who need defensible WORM storage.
Our pick: Supabase Pro for early-stage builds; graduate to S3 Object Lock as firm scales past 50 advisor seats and approaches their first SEC exam or SOC 2 audit.
Reference architecture
The pipeline runs: document upload → OCR extraction → planning engine → LLM narrative → supervisory-approval gate → immutable audit log → client-portal delivery. The hardest engineering challenge is the approval gate: every AI-generated client-facing output must be reviewed and approved by a human advisor before delivery, with the approval timestamped and stored alongside the AI output.
Advisor uploads client documents (brokerage statements, 1099s, plan exports)
Next.js frontend + Supabase StorageFiles stored in a per-client RLS-protected bucket; a Trigger.dev job fires on upload to begin extraction pipeline.
Document OCR extraction via GPT-5.4 nano
Supabase Edge Function (Deno)Each page of the PDF is sent to GPT-5.4 nano with a structured JSON schema; output is validated and written to the client_documents table.
Planning engine processes structured data
Supabase serverless functionDeterministic financial calculations (net worth, cashflow, goal-gap analysis) are computed from structured extraction; no AI involvement in the math itself.
LLM narrative generation via Claude Sonnet 4.6
Anthropic API with ZDR routing via Supabase Edge FunctionSonnet receives the structured plan data + a system prompt enforcing 'informational, not advice' framing; output is streamed and temporarily staged, not yet delivered.
AI input/output written to immutable audit log
Supabase append-only table (DELETE disabled via RLS policy)Full prompt, model version, output, timestamp, and client ID are written atomically; no update or delete allowed on any audit row.
Advisor supervisory review and approval
Next.js advisor dashboardThe staged AI output is presented to the advisor for review; approve/edit/reject action is timestamped and logged alongside the original AI output.
Approved content delivered to client portal
Next.js client-facing portalOnly approved content surfaces in the client portal; the client sees a 'Reviewed by your advisor' badge on all AI-generated sections.
Estimated cost per request
~$0.05 per client memo (Sonnet 4.6 narrative: $0.014 RAG query + $0.01 document extraction + $0.026 narrative generation). Model cost is trivial versus the $45K–$85K compliance scaffolding build cost.
Cost calculator
Drag the sliders to model your actual usage. The numbers update in real time so you can stress-test economics before writing a single line of code.
Models at 200 advisor seats + 500 active client plans. The LLM API spend is negligible — the dominant costs are infra, ZDR routing, and the compliance audit trail.
Estimated monthly cost
$70.22
≈ $843 per year
Calculator notes
- At 20 advisors × 25 clients × 3 AI outputs/mo, total API spend is ~$21/mo — infrastructure is the dominant line item at ~$70/mo.
- ZDR routing (Anthropic Zero Data Retention) adds ~10% to Anthropic API costs but is non-negotiable for client financial data.
- Document extraction cost assumes 5 pages per plan update; adjust for firms with complex multi-custodian clients.
- Audit log storage grows at ~0.5MB per client per month; at 500 clients, Supabase Pro's 8GB database handles ~16 months before requiring the $100/mo scale-up.
Build it yourself with vibe-coding tools
A weekend Lovable build can scaffold the advisor UI, client portal, and document upload flow — giving you a functional demo to show RIA prospects. It cannot satisfy SEC recordkeeping or Reg BI supervisory requirements.
Time to MVP
12–16 hours (1 weekend for demo; 14–18 weeks for production)
Total cost to MVP
$25 Lovable Pro + $30 Anthropic credits + $0 Supabase free tier = working demo
You'll need
Starter prompt
Build a white-label AI Financial Planning Software SaaS with two portals: an advisor portal and a client portal. Advisor portal features: - Document upload (accept PDF brokerage statements and planning exports) - Document list showing extraction status per client - AI-generated plan narrative viewer with APPROVE / EDIT / REJECT controls - Client roster with health-score indicator (green/yellow/red based on goal progress) - Audit log viewer showing all AI inputs/outputs per client Client portal features: - View approved plan narrative sections - Q&A chat interface bounded to their own documents only (display 'informational only, not financial advice' disclaimer) - Document upload for the advisor to review Tech stack: - Vite + React + TypeScript + Tailwind CSS + shadcn/ui - Supabase Auth (multi-tenant: advisor role and client role) - Supabase Storage for uploaded documents - Supabase tables: clients, documents, plan_narratives (status: pending/approved/rejected), audit_log (append-only) - Supabase Edge Functions for AI API calls Security requirements: - Row-level security on all tables — advisors see only their own clients - audit_log table: no DELETE or UPDATE allowed via RLS policy - All AI API calls go through Edge Functions, never from the client browser Do NOT implement real financial calculations — use placeholder data. Focus on the workflow: document upload → AI generation → advisor review → client delivery. Add a prominent banner: 'DEMO ONLY — not SEC-compliant. Do not use with real client data.'
Paste this into Lovable
Follow-up prompts (run in order)
- 1
Wire up the document extraction: in the Supabase Edge Function, send each page of the uploaded PDF to the OpenAI API using GPT-5.4 nano with this prompt: 'Extract the following fields from this brokerage statement page as JSON: account_name, account_number, total_value, positions (array of {symbol, quantity, value}), statement_date. Return only valid JSON.' Store the structured output in a document_extractions table linked to the document ID.
- 2
Wire up plan narrative generation: in a second Supabase Edge Function, take the structured extraction output for a client and call the Anthropic API (Claude Sonnet 4.6) with this system prompt: 'You are an AI assistant for a Registered Investment Advisor. You help advisors prepare informational plan summaries for client meetings. IMPORTANT: You provide informational summaries only. You do not provide investment advice, recommendations to buy or sell securities, or tax advice. Every output must include the disclaimer: This summary is for informational purposes only and does not constitute investment advice. Always flag any data gaps or inconsistencies for advisor review.' Generate a 3-paragraph meeting-prep summary. Store the full prompt + response + model version + timestamp in audit_log.
- 3
Add the supervisory approval gate: when an advisor clicks APPROVE on a narrative, write the approval event to audit_log with advisor_id, client_id, narrative_id, action='approved', timestamp. Change the narrative status to 'approved'. Show approved content in the client portal with a 'Reviewed by your advisor' badge and the approval timestamp.
- 4
Add the client-portal RAG Q&A: use Supabase's pgvector extension. When documents are extracted, embed each section using text-embedding-3-small and store vectors in a document_embeddings table. For each client Q&A message, retrieve the top-3 relevant chunks via cosine similarity, then call Claude Sonnet 4.6 with the system prompt and retrieved chunks as context. Log every Q&A exchange to audit_log. Cap at 5 messages per client per day.
- 5
Add Stripe Billing: set up Stripe for $299/advisor/mo subscription. Wire the Stripe webhook to an Edge Function that updates the advisor's subscription_status in Supabase. Block AI API calls if subscription_status = 'expired'.
Expected output
By end of weekend you have a functioning demo: advisors can upload documents, see AI-generated summaries, approve them, and clients can view approved content and ask bounded questions. This is enough to show RIA prospects — but the audit trail is not SEC-grade until a Trigger.dev background processor, S3 Object Lock, and legal-reviewed system prompts are added.
Known gotchas
- !The Lovable build will not produce an SEC-compliant audit log — Supabase tables can be modified by default. You need to add RLS policies that disallow DELETE/UPDATE on audit_log before touching any real client data.
- !Claude Sonnet 4.6 will still occasionally generate confident-sounding financial projections even with 'no advice' system prompts — test adversarially before showing to any RIA.
- !ZDR routing (required for client financial data under GLBA) adds 10% to Anthropic API costs and is not available by default — enable it in the Anthropic dashboard before going live.
- !FINRA Rule 3110 requires that supervisory procedures specifically name AI tools used and how their outputs are reviewed — the 'approve' button is not sufficient without a written supervisory procedure that references it.
- !PDF extraction via GPT-5.4 nano degrades significantly on scanned (non-text) PDFs — most brokerage statements from older custodians are image-based, requiring an OCR pre-processing step.
- !MoneyGuidePro and eMoney exports do not include an API — you're working with downloaded PDFs, which means extraction accuracy is your liability if figures differ from source.
Compliance & risk reality check
Financial planning AI for RIAs carries the heaviest SEC regulatory load in the Finance & Fintech cluster — not because the AI models are dangerous, but because the deploying RIA's fiduciary duty extends to every AI-generated output their clients see.
SEC Advisers Act §206 + AI-washing enforcement (Delphia/Global Predictions precedent)
The SEC's March 2024 settlements against Delphia ($225K) and Global Predictions ($175K) established that marketing an AI tool as providing 'AI-powered investment advice' without substantiation violates Advisers Act §206 and Marketing Rule 206(4)-1. Global Predictions had positioned itself as 'the first regulated AI financial advisor' — the SEC found the AI-capability claims unsubstantiated and misleading. Any white-label tool built on this page carries the same risk if the reselling RIA uses 'AI advisor' language.
Mitigation: Enforce 'informational summary, not investment advice' in every system prompt and every client-facing UI element. Include a product one-pager for RIA clients that distinguishes 'AI planning assistant' from 'AI advisor' with specific language reviewed by securities counsel. Never claim the tool 'recommends' securities or strategies.
SEC Marketing Rule 206(4)-1 + performance claims
The Marketing Rule prohibits testimonials and performance claims in investment advisor marketing unless specific conditions are met — including disclosure of whether the promoter is a client and whether compensation was paid. AI-generated 'outcome summaries' that could be read as performance claims are in scope.
Mitigation: All AI-generated narrative sections must carry a disclaimer: 'AI-generated summary for informational purposes only. Past scenarios do not predict future outcomes.' Have the RIA's compliance officer review the prompt templates before deploying to their clients.
17 CFR §275.204-2 Books and Records Rule (5-year retention)
Investment advisers must retain records of all written communications relating to their business, including AI-generated content that advisors review and share with clients. The rule requires 5-year retention with the first 2 years in an easily accessible place. An AI input/output log that can be deleted or modified does not satisfy this requirement.
Mitigation: Implement append-only audit logging with DELETE and UPDATE disabled via database row-level security. For production, use S3 Object Lock (WORM) for the AI input/output archive. Include the AI tool version in every log entry so SEC examiners can reproduce the model's behavior.
Reg BI (Regulation Best Interest) — best-interest standard
Reg BI requires broker-dealers (and by SEC interpretation, extends to RIA best-interest standards) to act in the best interest of retail customers at the point of making a recommendation. AI-generated plan summaries that a broker presents to a client as their basis for a recommendation implicate Reg BI.
Mitigation: The supervisory-approval gate is mandatory — no AI output should reach a client without advisor review. Build the 'Reviewed by your advisor' UI label and timestamp into the client portal. Document the supervisory procedure in writing and have RIA compliance counsel sign off.
FINRA Rule 3110 — supervisory procedures for AI content
FINRA-registered firms must establish and maintain written supervisory procedures (WSPs) that specifically address the supervision of AI-generated content sent to clients. The WSP must identify which personnel are responsible for reviewing AI outputs and at what frequency.
Mitigation: Draft a WSP addendum that names your tool, describes the human-review step (the approval gate), identifies who approves (licensed supervisor), and sets a minimum review frequency. This is legal documentation, not code — budget $3K–$8K for securities counsel.
GLBA Safeguards Rule + Regulation P (financial privacy)
The Gramm-Leach-Bliley Act Safeguards Rule (updated in 2021) requires financial institutions, including RIA custodians, to implement a written information security program covering AI models that process nonpublic personal information (NPI). Reg P requires annual privacy notices to clients.
Mitigation: Enable Anthropic ZDR (Zero Data Retention) on all API calls; store no client NPI on Anthropic servers. Include the AI tool in the RIA's annual Safeguards Rule assessment. Issue or update the client privacy notice to disclose AI-assisted plan preparation.
Build vs buy: the real math
14–18 weeks
Custom build time
$45,000–$85,000
One-time investment
12–18 months (depending on advisor seats and ARPU)
Breakeven vs buying
eMoney at $3,600/yr per advisor and MoneyGuidePro at $1,295/yr per advisor have no white-label tier — so the correct comparison for a reseller is: build a branded platform vs. not being in the market at all. At 20 advisor-clients paying $299/mo each, a white-label platform generates $71,760/yr revenue with ~$8,400/yr infra COGS = ~88% gross margin. A $65K midpoint build recoups in 13 months and then throws off ~$63K/yr net. The math improves as model prices fall — Claude Sonnet 4.6 has already dropped 67% from the Opus 3.1 era; a further 50% decline in the next 24 months (T8 trajectory) pushes gross margin past 92%. The compliance scaffolding cost does not benefit from model deflation, but it also doesn't recur — it's a one-time build amortized over the platform's lifetime.
Skip the DIY — RapidDev builds the production version
A Lovable MVP gets you a demo. Production needs auth that doesn't leak data, AI calls that don't bankrupt you, observability when models drift, and code you can audit. That's what we ship.
Discovery call (free)
30 minWe map your exact Financial Planning Software use case: who uses it, target volume, AI model choice, integrations, compliance scope. You get a detailed scope document and fixed-price quote within 48 hours.
AI-accelerated build
14–18 weeksOur engineers use Claude Code, Lovable, and custom tooling to ship 3–5x faster than agencies. You see weekly progress in a staging environment — not a black box.
Launch + handoff
1 weekWe deploy to your infrastructure, transfer the GitHub repo, set up CI/CD and monitoring, and train your team. You own 100% of the source code, prompts, and model configurations.
What you get
Timeline
14–18 weeks
Investment
$45,000–$85,000
vs SaaS
ROI in 12–18 months (depending on advisor seats and ARPU)
30-min call. Fixed-price quote within 48 hours. No commitment.
Frequently asked questions
How much does it cost to build an AI financial planning software platform?
RapidDev builds this for $45,000–$85,000 over 14–18 weeks. The range reflects the compliance scaffolding load: a platform with a basic supervisory-approval gate and immutable audit log sits at the low end; one with full FINRA WSP documentation, S3 Object Lock for §204-2 retention, and multi-custodian data aggregation sits near $85K. Budget an additional $5K–$15K for securities counsel to review prompt templates and supervisory procedures — that's separate from the development cost.
How long does it take to ship an AI financial planning tool?
14–18 weeks for a production-grade build. The timeline is driven primarily by SEC compliance scaffolding — immutable audit logging, supervisory-approval workflow, and legal review of 'informational, not advice' prompt templates — not by the AI integration itself. A demo can be scaffolded in a weekend on Lovable, but that demo cannot be used with real client data.
Can RapidDev build this for my RIA or fintech company?
Yes. RapidDev has shipped 600+ applications including financial-data platforms with compliance-grade audit trails. We build the full stack: document extraction, LLM narrative pipeline with ZDR routing, supervisory-approval gate, and immutable audit logging. We scope the compliance documentation requirements with you and recommend securities counsel for Advisers Act and Reg BI review. Start with a free 30-minute consultation at rapidevelopers.com.
Does the SEC require me to disclose when AI wrote a client communication?
As of June 2026, the SEC has not issued a final rule specifically requiring disclosure of AI-authorship in advisor communications, but the existing Marketing Rule 206(4)-1 and Reg BI best-interest standard require that all client-facing content be accurate and not misleading. The SEC's AI-washing enforcement actions (Delphia, Global Predictions) signal that unsubstantiated AI-capability claims in marketing are the primary risk, not the AI authorship per se. Best practice: label all AI-generated content as 'AI-assisted summary, reviewed by your advisor' and include the advisor's attestation timestamp.
Why does no white-label financial planning SaaS exist for small RIAs?
The compliance overhead kills the SaaS economics for any vendor trying to build a generic white-label layer. eMoney and MoneyGuidePro know their RIA clients personally and can walk them through FINRA WSP updates — a white-label SaaS resold to dozens of RIAs inherits each RIA's unique compliance profile, making the 'one-size-fits-all' model unworkable. The gap is real and persistent, which is why a custom build remains the dominant path for agencies trying to serve this market.
What happens if Claude or GPT hallucinates a financial figure in a client plan summary?
That is precisely why the supervisory-approval gate is non-negotiable. No AI output should reach a client without an advisor reviewing the numbers against source documents. In the build, the 'pending' stage holds AI outputs until an advisor marks them approved; the client portal only displays approved content. Additionally, the audit log captures the exact AI output — if a client later disputes a figure, you can demonstrate what the AI generated versus what the advisor approved.
Is there a cheaper build option that still satisfies basic SEC requirements?
The cheapest defensible production build is approximately $45K and requires: immutable audit logging (Supabase RLS-locked table), ZDR routing (Anthropic's Zero Data Retention), a supervisory-approval gate with timestamped approvals, and 'informational only' disclaimers on every AI output. Cutting below $45K means cutting one of these layers, which creates SEC exam risk. The model API cost at $0.05/client memo is irrelevant — the compliance scaffolding is where the money goes.
Want the production version?
- Delivered in 14–18 weeks
- You own 100% of the code
- AI cost monitoring built in
30-min call. No commitment.