What a Investor Relations Platform actually does
Drafts earnings Q&A responses from 10-K/10-Q corpus, surfaces investor-portal questions grounded in public disclosures, and generates IR video narrations — all within a mandatory human-review workflow that satisfies Reg FD uniform-information-distribution requirements.
The platform ingests public SEC filings (10-K, 10-Q, 8-K, earnings transcripts) into a RAG corpus via text-embedding-3-large ($0.13/M tokens for high-recall regulatory text). Claude Sonnet 4.6 ($3/$15) — selected for explainability and audit-log quality — drafts analyst Q&A responses grounded strictly in public disclosures. Every AI input and output is logged immutably with a mandatory reviewer-approval gate: no AI-drafted content reaches an investor or analyst without a licensed IR professional marking it approved. SOX §404 internal controls require this approval chain to be documented and audited annually.
The SEC's March 2024 AI-washing settlements (Delphia $225K, Global Predictions $175K) and the Reg FD enforcement precedent make this the Finance cluster's most legally sensitive build. Reg FD (Regulation Fair Disclosure) requires that any material non-public information (MNPI) shared with one analyst be simultaneously disclosed to all investors — if an AI system drafts a response that contains MNPI and it goes out to one analyst without proper disclosure control, the deploying IR team faces an SEC enforcement action, not the software vendor.
AI capabilities involved
LLM-drafted earnings Q&A from 10-K/10-Q + transcript RAG corpus
Investor-portal natural-language Q&A bounded to public disclosures
Shareholder communication translation (multi-lang)
Sentiment analysis on social and news mentions for IR alerting
Auto-generated investor-deck narrative from financial templates
Who uses this
- IR agencies managing investor communications for 5–50 public-company clients
- Late-stage private-company investor-portal product leads (Series C+, preparing for IPO)
- In-house IR teams at public companies seeking AI augmentation under their own branded portal
- Boutique financial PR firms adding AI-assisted earnings prep to their service offering
SaaS alternatives on the market
Real products you can sign up for today — with current 2026 pricing, honest pros and cons.
Q4 Inc.
Large public companies with 10+ investor relations staff who need a comprehensive, single-vendor IR platform.
Enterprise quote (dominant IR SaaS platform)
Pros
- +Dominant market position in public-company IR — enterprise procurement teams recognize it.
- +Comprehensive IR workflow: earnings calendar, webcasts, investor targeting, shareholder analytics.
- +SEC filing integration and real-time shareholder activity monitoring.
- +Established compliance framework for Reg FD-sensitive communications.
Cons
- −No white-label or agency reseller path — IR agencies cannot sell 'their branded Q4' to clients.
- −Enterprise pricing requires long-term contracts and implementation projects.
- −AI features are Q4-branded and not customizable for agency white-labeling.
- −Feature set is broader than most IR agencies need — paying for webcasting, shareholder targeting, etc.
Notified IR (formerly Intrado)
Large-cap public companies with complex multinational IR programs requiring global press release distribution and earnings webcasting.
Enterprise quote
Pros
- +Strong earnings webcast and press release distribution infrastructure.
- +Global reach for IR communications across multiple markets.
- +Established relationships with financial news wires (PR Newswire).
- +Compliance monitoring for SEC disclosure requirements.
Cons
- −No white-label or agency reseller model.
- −Primarily a distribution platform — limited AI-drafting or analytics capability.
- −Historically complex technology after multiple acquisitions.
- −Enterprise sales cycle and minimum commitments exclude boutique IR agencies.
Carta
Private companies (Series A–pre-IPO) managing equity, option pools, and investor reporting — not public-company IR.
$2,800+/yr (startup); enterprise custom
Pros
- +Best cap-table management platform for private companies.
- +Native investor portal for 409A valuations, SAFE notes, equity management.
- +Strong fundraising workflow integration.
- +SOC 2 Type II certified.
Cons
- −Focused on private-company cap-table management, not public-company IR workflows.
- −No SEC filing ingestion or Reg FD-specific features for public companies.
- −No AI Q&A drafting functionality.
- −No white-label or agency reseller path.
The AI stack
IR AI requires two non-negotiable architectural constraints above all models: ZDR routing (no AI provider retains MNPI from filing corpora for model training) and mandatory human-review gates before any AI draft is released externally. The model choice is secondary to these controls.
SEC filing RAG corpus
Index 10-K, 10-Q, 8-K, and earnings transcripts to ground AI responses strictly in public disclosures.
text-embedding-3-large + Supabase pgvector
$0.13/M tokens for initial indexing (~$13 to embed 100 filings at 10K tokens each)Any IR RAG corpus where missing a specific disclosure language in retrieval creates regulatory risk.
Our pick: text-embedding-3-large for all IR filing embeddings — the recall improvement on complex SEC disclosure language is worth the 6.5× cost premium. At $13 to embed 100 filings, the absolute cost is irrelevant compared to the liability of missing a relevant disclosure in a Q&A response.
Q&A drafting and investor-portal chat
Generate analyst Q&A draft responses and investor-portal answers strictly grounded in the RAG filing corpus — never inferring beyond public disclosures.
Claude Sonnet 4.6 with ZDR + Anthropic DPA
$3/$15 per M tokens (~$0.014 per Q&A draft at 2,600-in/400-out, T1 row 17)All investor-facing Q&A drafting where Reg FD liability requires maximum conservatism.
GPT-5.4 mini (Azure OpenAI, internal use only)
$0.75/$4.50 per M tokensPreliminary draft review by IR staff, never for investor-facing Q&A that goes directly external.
Our pick: Claude Sonnet 4.6 with Anthropic ZDR for all investor-facing or analyst-facing Q&A drafting. GPT-5.4 mini via Azure OpenAI only for internal preliminary review by licensed IR staff. Never route MNPI through consumer Claude.ai or ChatGPT.com.
Reference architecture
Five-stage pipeline: filing ingestion → RAG indexing → AI draft generation → mandatory human review with approval logging → external delivery. The hardest engineering problem is the approval gate: every AI-generated piece of content must be reviewed and approved by a licensed IR professional before reaching any investor or analyst, with the approval logged immutably alongside the AI output.
SEC filing ingestion from EDGAR (automatic + manual upload)
Trigger.dev cron → SEC EDGAR full-text search APINightly cron checks EDGAR for new filings per watched CIK (company ID); downloads 10-K/10-Q/8-K/transcript PDFs; stores in per-company, per-tenant corpus bucket in Supabase Storage.
Document chunking and embedding via text-embedding-3-large
Trigger.dev job → OpenAI Embeddings API → Supabase pgvectorChunk documents at 800 tokens with 200-token overlap; embed each chunk; store in filing_embeddings with company_id, tenant_id, filing_date, filing_type tags.
IR staff or investor submits a question
Next.js IR portal (two modes: analyst prep mode + investor-facing portal)Analyst prep mode: IR staff queries the system pre-earnings to generate draft Q&A responses. Investor portal: retail investors ask questions and receive a response after human review (async, not real-time).
RAG retrieval + Claude Sonnet 4.6 draft generation
Supabase Edge Function → pgvector similarity search → Anthropic API (ZDR)Top-5 filing chunks retrieved by cosine similarity; passed to Sonnet 4.6 with system prompt: 'You are an IR disclosure assistant. Answer ONLY from the provided public disclosure text. If the question cannot be answered from public disclosures, respond: Based on public disclosures, I cannot answer this question. Reg FD prevents us from sharing material non-public information. Redirect to: earnings call, press releases, SEC filings.' Draft stored in ai_drafts table as pending.
AI draft and input written to immutable audit log
Supabase append-only table (trigger prevents DELETE/UPDATE)Full prompt, model version, RAG chunks used, response, timestamp, company_id, tenant_id — all logged atomically. This log satisfies 17 CFR §275.204-2 5-year retention requirement.
Human reviewer reviews and approves/edits/rejects
Next.js IR staff dashboardLicensed IR professional reviews the AI draft, can edit it, and must click APPROVE before the response is delivered. Approval logs: reviewer_id, approval_timestamp, edit_distance (flagged if significant), and reviewer_attestation.
Approved content delivered to investor or analyst
Investor portal (Next.js) or email via ResendApproved response published to investor portal or emailed; labeled 'Reviewed and approved by [IR team name]'. All delivery events logged to communication_log for Reg FD uniform-distribution audit.
Estimated cost per request
~$0.014 per Q&A draft (Claude Sonnet 4.6 at 2,600-in/400-out tokens via T1 row 17). Model cost is trivial — the legal scaffolding is the investment.
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.
Modeled at 10 public-company IR clients, each submitting 50 investor/analyst questions per month. AI costs are negligible versus infra and legal overhead.
Estimated monthly cost
$70.70
≈ $848 per year
Calculator notes
- At 10 clients × 50 questions = 500 Q&A drafts/mo × $0.014 = $7/mo in Sonnet costs — infra dominates at $70/mo total.
- Securities counsel review ($10K–$30K one-time) and annual SOX audit support ($5K–$15K/yr) are not in the infrastructure calculator — budget separately.
- 17 CFR §275.204-2 5-year audit log storage: at ~2KB per Q&A exchange × 500/mo × 12 × 5 years = ~60MB — negligible on Supabase Pro.
- Investor-facing portal hosting includes a content delivery network (CDN) for SEC filing PDFs — Supabase Storage provides this at the Pro tier; factor ~$0.09/GB beyond the 100GB included.
Build it yourself with vibe-coding tools
Scope the portal UI and document Q&A flow with mock filings in Lovable. Never use real SEC filings, MNPI, or actual analyst questions in a prototype — Reg FD applies from the first communication.
Time to MVP
12–16 hours for UI demo only (mock data, no real filings)
Total cost to MVP
$25 Lovable Pro + $30 Anthropic credits = IR portal UI demo with mock Q&A
You'll need
Starter prompt
Build a DEMO ONLY AI Investor Relations Portal UI (mock data, no real company filings). IR Staff portal: - Filing library: list of uploaded documents (10-K, 10-Q, 8-K, Earnings Transcript) per company — use mock PDF names - Earnings Q&A prep: enter a mock analyst question → AI generates draft response → display in a split-pane view (AI draft | edit area) with APPROVE/EDIT/REJECT buttons - Approval queue: list of pending AI drafts awaiting reviewer action - Audit log viewer: table showing all AI drafts with prompt excerpt, response excerpt, reviewer, approval timestamp Investor portal (mock): - Company overview with mock financial highlights (from hardcoded JSON, not real data) - Q&A submission form: investor types question → status shows 'Under Review' → simulated approved response displayed 24h later - Document library: links to mock SEC filing PDFs BANNER: 'DEMO ONLY — uses mock data. Real SEC filings, analyst Q&A, and investor communications require securities counsel review and Reg FD compliance infrastructure before deployment.' Tech stack: Vite + React + TypeScript + Tailwind + shadcn/ui + Supabase Auth + Supabase Edge Functions (Anthropic for mock Q&A)
Paste this into Lovable
Follow-up prompts (run in order)
- 1
Wire up the mock Q&A draft: in the Edge Function, call Claude Sonnet 4.6 with mock filing text (paste in 3–4 paragraphs from a publicly available annual report as context) and the analyst question. System prompt: 'You are an IR disclosure assistant. Answer only from the provided disclosure text. If the answer requires information not in the text, say: Based on our public disclosures, I am unable to address that specific question. Please refer to our SEC filings at SEC.gov or contact our IR team.' Display the draft in the split-pane view.
- 2
Wire up the approval gate UI: when a reviewer clicks APPROVE, write {draft_id, reviewer_id, action: approved, timestamp, edit_made: boolean} to an approvals table. Change the draft status from pending to approved. Show an 'Approved by [reviewer name] at [timestamp]' badge on the draft. For DEMO purposes, use Supabase Realtime to show the approval event immediately in the investor portal Q&A. In production: add the immutable DELETE/UPDATE prevention trigger to the audit_log table before adding any real company data.
Expected output
A clickable IR portal demo showing the Q&A draft workflow, approval gate UI, and audit log view — all on mock data. Sufficient for IR agency business development. NOT production-ready: no real SEC filing ingestion, no ZDR routing, no immutable audit log, and no Reg FD compliance layer.
Known gotchas
- !Reg FD applies from the first real investor communication — sharing an AI-drafted response with a real analyst outside a proper review workflow, even from a 'beta test' account, triggers enforcement risk.
- !The immutable audit log cannot be an afterthought — add the Supabase trigger that prevents DELETE/UPDATE on ai_drafts and approvals tables before loading any real company data.
- !Claude Sonnet 4.6 will still occasionally generate responses that infer beyond the provided filing text when the retrieval chunks are ambiguous — test adversarially with questions about unreported forward guidance.
- !17 CFR §275.204-2 requires the audit log to be preserved in a format that can be produced to the SEC in an examination — a Supabase table backup alone may not satisfy this; consult securities counsel on acceptable archival formats.
- !SEC EDGAR's full-text search API has rate limits — for IR agencies tracking 20+ company CIKs for daily filing updates, implement a queue-based EDGAR polling strategy, not a direct nightly loop.
- !Securities counsel engagement before launch is not optional — the system prompt language defining 'informational, not advice' for investor Q&A must be reviewed by a securities attorney who understands Reg FD and the Investment Advisers Act context.
Compliance & risk reality check
Investor relations AI carries the heaviest compliance load among the Customer Retention cluster pages — Reg FD, SOX §404, SEC AI-washing, and 17 CFR §275.204-2 recordkeeping all apply simultaneously. The legal scaffolding cost ($10K–$30K in securities counsel) is not a variable — it is a pre-launch prerequisite.
SEC Regulation FD — uniform information distribution
Reg FD (17 CFR §243.100) requires that any material non-public information (MNPI) disclosed to a securities market professional (analysts, institutional investors) be simultaneously disclosed to all investors via a widely accessible method (8-K filing or press release). If an AI-drafted Q&A response contains MNPI and is sent to one analyst before a public filing, the deploying company and the IR platform face SEC enforcement action. The March 2024 Delphia and Global Predictions settlements under Advisers Act §206 demonstrate active SEC enforcement posture on AI-adjacent securities violations.
Mitigation: Every AI-generated Q&A draft must pass through a mandatory human reviewer who is a licensed IR professional before release. The reviewer must attest that the response contains no MNPI. Build a mandatory attestation checkbox (I confirm this response contains no material non-public information) into the approval workflow. All Q&A drafts and approvals logged to the immutable audit trail.
SOX §404 internal controls — AI content review workflow
Section 404 of the Sarbanes-Oxley Act requires public companies to maintain and assess the effectiveness of internal controls over financial reporting. Any system that generates or distributes investor communications for a public company must have documented internal controls — including the AI review workflow. A SOX audit will test whether the approval gate actually functions and whether the audit log is complete and immutable.
Mitigation: Document the AI Q&A workflow as a formal internal control: define who can approve AI drafts (IR Director or above), what training they receive on MNPI identification, and how often the approval log is reviewed. The immutable audit log is the primary SOX evidence artifact. Engage a SOX auditor to review the control design before the first public-company client goes live.
SEC AI-washing — Delphia/Global Predictions precedent
The SEC's March 2024 settlements against Delphia ($225K) and Global Predictions ($175K) for AI-washing in their marketing materials warn that claiming 'AI-powered investor insights' without substantiation violates Advisers Act §206 and Marketing Rule 206(4)-1. An IR platform that markets as 'AI investor Q&A' must ensure all AI-capability claims are specific, verified, and not misleading about the AI's capabilities or accuracy.
Mitigation: Marketing language must describe the AI as an 'AI-assisted drafting tool reviewed by licensed IR professionals' — not as 'AI investor analysis' or 'AI-powered investor relations.' Have securities counsel review all marketing materials before publication. Never claim the AI provides investment advice.
17 CFR §275.204-2 — 5-year AI input/output retention
Investment advisers must retain records of all written communications relating to their business for 5 years. AI-generated content that advisor-clients review and release to investors qualifies as a written communication. The 5-year retention requirement applies to both the AI input (the question + RAG context) and the AI output (the draft response) — not just the final approved version.
Mitigation: Implement the Supabase append-only audit log with a PostgreSQL trigger preventing DELETE and UPDATE on the ai_drafts table. For SOC 2 and SEC exam readiness, implement S3 Object Lock (WORM) as a backup archive for all AI input/output records, retained for 5 years with automated deletion prevention.
ZDR routing — no AI provider trains on MNPI
Consumer AI endpoints (Claude.ai, ChatGPT.com) may retain conversation data for model improvement. Routing MNPI through these endpoints — even in a RAG system that 'doesn't store' the MNPI on the platform — creates a risk that the AI provider's training process captures material information before it is publicly disclosed.
Mitigation: Enable Anthropic API Zero Data Retention (ZDR) on all calls. Include ZDR documentation in the client's data processing agreement. Alternatively, route through Anthropic on Amazon Bedrock with the applicable AWS BAA-equivalent DPA. Document the specific API endpoint and ZDR configuration in the client's written information security program.
Build vs buy: the real math
16–24 weeks
Custom build time
$60,000–$120,000
One-time investment
12–24 months
Breakeven vs buying
No comparable white-label IR platform exists at SMB pricing — Q4 Inc, Notified IR, and EQS Group are all direct-to-company enterprise products. The build competes against the IR agency's current manual process: drafting Q&A responses manually from SEC filings takes 2–4 hours per earnings cycle per client. At 10 clients × 4 hours × $200/hr IR consulting rate = $8,000/cycle opportunity cost. A $90K midpoint build eliminates the manual drafting burden across 10 clients within 11 earnings cycles (approximately 3 years, since most companies report quarterly) — while also enabling the agency to scale to 20+ clients without additional staffing. The ZDR and audit log infrastructure does not depreciate — it provides compliance value for the lifetime of the platform regardless of model price changes.
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 Investor Relations Platform 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
16–24 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
16–24 weeks
Investment
$60,000–$120,000
vs SaaS
ROI in 12–24 months
30-min call. Fixed-price quote within 48 hours. No commitment.
Frequently asked questions
How much does it cost to build a white-label AI investor relations platform?
RapidDev builds this for $60,000–$120,000 over 16–24 weeks. The range reflects the compliance scaffolding: lower end covers core Q&A drafting with Sonnet 4.6 ZDR, RAG over SEC filing corpus, mandatory approval gate, and immutable audit log. Upper end adds: SOX §404 documentation support, multi-company filing ingestion with automated EDGAR monitoring, investor-facing portal with async Q&A delivery, and ElevenLabs voiceover for IR video narrations. Securities counsel fees ($10K–$30K) for system prompt review and Reg FD workflow design are separate from the development cost.
How long does it take to ship an AI IR platform?
16–24 weeks. A UI demo with mock Q&A can be built in a Lovable weekend. The 16-week production build adds: SEC EDGAR automated filing ingestion, text-embedding-3-large RAG corpus, Claude Sonnet 4.6 ZDR Q&A drafting, immutable audit log (Supabase append-only + S3 Object Lock), and supervisory approval workflow. The 24-week version adds multi-company filing monitoring, investor-facing async Q&A portal, and ElevenLabs IR video voiceover generation.
Can RapidDev build this for my IR agency?
Yes. RapidDev has built compliance-grade financial platforms with immutable audit trails, ZDR routing, and supervisory approval workflows. For IR specifically, we engage securities counsel as a project partner during the architecture phase — the Reg FD compliance design is a legal project, not just a code project. Book a free 30-minute consultation at rapidevelopers.com.
What is Reg FD and why does it apply to an AI IR platform?
Regulation Fair Disclosure (SEC 17 CFR §243.100) requires public companies to simultaneously disclose any material non-public information (MNPI) to all investors when they disclose it to securities professionals (analysts, institutional investors). If an AI system generates a response containing MNPI and it's sent to one analyst before a public filing, the company has violated Reg FD. The AI platform doesn't create the Reg FD obligation — the company already has it. The platform's obligation is to ensure that no AI-generated content reaches an external party before a licensed IR professional reviews it for MNPI. The approval gate is the technical implementation of that obligation.
Can the AI answer questions from retail investors in real time?
Not safely in real time — the mandatory human review requirement means investor Q&A must be async. A retail investor submits a question, it enters the review queue, a licensed IR professional reviews the AI draft (typically within 24 hours for routine questions), approves it, and the investor receives the approved response. Real-time AI responses to investor questions without human review would create a Reg FD violation risk if any response inadvertently contains MNPI. The async workflow with approval gate is the only Reg FD-compliant architecture.
Does 5-year AI input/output retention really apply to Q&A drafts?
Yes. 17 CFR §275.204-2(a)(7) requires investment advisers to retain 'written communications sent and received in the conduct of the adviser's business.' An AI-drafted Q&A response that an IR adviser reviews and sends to an investor is a written communication in the adviser's business. Both the AI input (the question + RAG context that generated the draft) and the AI output (the draft response) must be retained for 5 years, with the first 2 years in an easily accessible format. The immutable Supabase audit log satisfies the first-2-years requirement; S3 Object Lock satisfies the 5-year WORM (Write Once Read Many) requirement.
Want the production version?
- Delivered in 16–24 weeks
- You own 100% of the code
- AI cost monitoring built in
30-min call. No commitment.