What a Survey & Feedback Analysis Tool actually does
Automatically analyzes customer survey responses and feedback to extract themes, sentiment, NPS drivers, and actionable improvement areas.
A white-label survey analysis platform ingests raw responses (CSV, API, webhooks from Typeform/Qualtrics/SurveyMonkey), classifies sentiment (positive/negative/neutral), extracts key themes (product quality, pricing, support, delivery), scores NPS predictors, and generates weekly summary reports. Claude Haiku 4.5 ($1/$5 per M tokens) handles high-volume classification at scale (~$0.001 per response); Claude Sonnet 4.6 ($3/$15) generates executive summaries and identifies trends. The platform is ideal for brands running continuous feedback loops — e-commerce companies tracking product satisfaction, SaaS tracking feature requests, hospitality tracking guest NPS — and for research agencies reselling to their clients.
In 2026, the market dynamics favor custom builds for premium positioning: generic SaaS competitors (Alchemer, Qualtrics, Delighted) commoditize at $100–$200/mo; white-label builders can differentiate by offering integrated CRM workflows, custom taxonomy (map feedback to internal system), and predictive NPS models. The real value is not the sentiment analysis — every LLM does that — but the integrations with Shopify, HubSpot, and Slack that make insights actionable in real-time.
AI capabilities involved
Sentiment classification (positive/negative/neutral) and intensity scoring (0–1)
Topic/theme extraction from free-text responses (e.g., 'product quality', 'pricing', 'shipping', 'support')
NPS driver analysis: which themes correlate with high vs. low NPS scores
Automatic summarization: distill 100+ responses into a 1-page executive summary
Multilingual support (Spanish, French, German, Mandarin, Japanese top 5 for global brands)
Who uses this
- E-commerce brands running post-purchase NPS surveys (Shopify, WooCommerce integrated)
- SaaS companies tracking feature requests and onboarding sentiment
- Hospitality & food brands (restaurants, hotels) analyzing guest feedback from multiple channels (Google Reviews, TripAdvisor, proprietary surveys)
- Research & insights agencies reselling analysis as a managed service to 10–100 SMB clients
- HR/Talent teams analyzing employee engagement surveys and exit-interview feedback
SaaS alternatives on the market
Real products you can sign up for today — with current 2026 pricing, honest pros and cons.
Alchemer (formerly SurveySparrow)
Agencies with 10–50 SMB clients; if you can negotiate white-label pricing under $200/client/mo, margin is viable
14-day trial, no free tier
$25/mo Standard (single user, 100 responses/mo); White-Label Partner program quote-based
$500+/mo custom
Pros
- +Explicit white-label partner program with branding controls (custom domain, logo, color scheme)
- +Survey builder is user-friendly for SMBs; no coding required
- +AI analysis is built-in (sentiment + theme extraction) at Professional tier ($75/mo+)
- +Integrations with Zapier (3500+), HubSpot, Salesforce
Cons
- −White-label pricing is opaque (quote-based); you must negotiate per-response costs
- −Response overage fees are punitive — professional tier includes 1,000 responses/mo; excess at $0.10–$0.25/each
- −AI features (sentiment, topic extraction) are add-ons; cost structure unclear for white-label resale
- −Customer support is slow (1–2 business days); not suitable for real-time SLA expectations
Delighted (by Qualtrics)
Agencies with 50+ clients or enterprise brands; if you need white-label, negotiate with Qualtrics enterprise sales directly
Free plan: 1 survey, 100 responses/mo, limited AI
$99/mo Professional; White-Label Enterprise quote-based
Custom pricing for white-label
Pros
- +Delighted is the market leader for NPS automation; easiest setup for 'send NPS weekly' workflows
- +AI summarization is built-in and accurate (uses Claude internally, per public statements)
- +Integrations with Shopify (native), HubSpot, Slack, Zendesk
- +SLA uptime is 99.9% (Qualtrics-backed infrastructure)
Cons
- −White-label is enterprise-only; no published pricing; sales cycle is 6–12 weeks
- −Free plan is too limited to trial; you must commit to paid tier to test
- −Overage costs: Professional tier = 1,000 responses/mo; each additional 100 responses = $0.10 ($1/1000 effective)
- −Qualtrics acquisition (2023) has caused feature churn and price increases
Typeform (no white-label, but has API for embedding)
NOT recommended for white-label resale. Use Typeform survey UI + your own Lovable/custom analysis backend.
Free: 1,000 responses/month, basic features
$25/mo Basic; $50/mo Plus; $1,250/mo Enterprise
$1,250+/mo or custom
Pros
- +Beautiful, mobile-first survey UI (industry-leading)
- +Webhooks for integrations (Zapier, custom APIs)
- +Can embed surveys on your white-label site via iframe
- +Affordable for SMBs
Cons
- −NO white-label program — Typeform branding always visible in footer; cannot fully rebrand as your own
- −No built-in AI analysis — you'd have to pull responses via API and run Claude on them yourself (adds engineering)
- −Enterprise pricing ($1,250/mo) is for feature access, not resale — no pricing for white-label resale
- −GDPR: Typeform stores data in EU by default, but white-label customers may have conflicting data residency requirements
The AI stack
A production survey analysis platform requires 3 layers: (1) response ingestion (webhooks from Typeform, Qualtrics, Shopify), (2) sentiment + theme classification at scale (Haiku for high-volume, Sonnet for summary generation), (3) reporting (charts, dashboards, email exports). The cost trade-off is clear: Haiku 4.5 at $1/$5 per M tokens processes 100K responses/month at ~$100–$150 in LLM costs; most white-label SaaS charge $500–$2,000/mo for the same, so margin is 3–10x if you DIY.
Response ingestion
Accept survey responses from Typeform, Qualtrics, SurveyMonkey, Shopify, custom webhooks, and CSV uploads
Zapier or n8n orchestration (standard approach)
$0 if using free Zapier; $20–$100/mo for n8n Cloud; custom orchestration $0 (if in-house)SMB agencies with 10–50 clients; if you need to support 20+ data sources, this is the right choice
Custom webhook receiver (Edge Function or serverless)
$0 code; $5–$30/mo infra depending on volumeAgencies with in-house engineering or high-volume (100K+ responses/month) where Zapier cost becomes prohibitive
Our pick: Start with Zapier for MVP (easiest, 0 engineering). Migrate to n8n Cloud if you hit 50K responses/month (cheaper long-term). Migrate to custom Edge Functions only if you exceed 150K responses/month or need custom transformations (e.g., mapping Shopify product tags to internal category schema).
Sentiment & theme classification
Classify each response's sentiment, extract key themes, score NPS drivers, and flag urgent feedback
Claude Haiku 4.5 (high-volume classification)
$1 input / $5 output per M tokens (~$0.001–$0.002 per response at typical token usage: 50 tokens in, 100 tokens out)Production default for per-response classification; high-volume triage and scoring
Claude Sonnet 4.6 (summary generation & complex analysis)
$3 input / $15 output per M tokens (~$0.01–$0.03 per response if used for deeper synthesis)Use only for executive summaries, trend identification, and weekly reports (post-processing of Haiku output)
Fine-tuned model on your taxonomy (Anthropic fine-tuning, not available as of June 2026)
$500–$2,000 initial training + $0.5x-1.5x multiplier on inference costSkip for MVP; revisit after you've accumulated 1,000+ responses with human-labeled themes
Our pick: Use Claude Haiku 4.5 for all per-response classification (sentiment, theme extraction, NPS driver scoring). Use Claude Sonnet 4.6 for weekly executive summary generation and trend analysis (run batch job every Sunday). This splits the workload: 90% of cost is Haiku ($100–$150/mo for 100K responses); 10% is Sonnet summaries ($20–$30/mo).
Reporting & dashboards
Generate charts, trend reports, and email summaries for end-users
Recharts (React charting library, free) + Next.js Server Components
$0Lovable DIY or custom Next.js builds; if you want full control over UX
Metabase or Apache Superset (open-source BI, self-host or cloud)
$0 self-host (you own infra); ~$50–$300/mo cloud tierIf your white-label customers want to build custom dashboards; more power than Recharts but more setup
Email reporting automation (SendGrid or Brevo + template)
$5–$50/mo depending on volumeSMB white-label customers who want 'just email me the summary'; pair with on-demand dashboard for power users
Our pick: Use Recharts + Next.js for white-label dashboard (full control). Use SendGrid for auto-email summaries. Optionally add Metabase if your high-tier customers want custom BI access (charge extra for Metabase seats).
Reference architecture
A production survey analysis system is an event-driven pipeline: (1) responses arrive via webhook or scheduled CSV import, (2) Haiku classifies sentiment + themes in near-real-time (30–60 sec delay), (3) results are written to Supabase, (4) frontend dashboards query results and render charts, (5) every Sunday, Sonnet generates executive summary and emails it. The hardest engineering challenge is managing webhook backpressure — if 10K responses arrive in 1 hour, you must batch-classify efficiently without timeout.
Customer submits survey responses (Typeform, Qualtrics, Shopify, or CSV upload)
Webhook receiver (Zapier or custom Edge Function) + SupabaseIncoming webhook is validated (signature check), parsed, and stored in raw_responses table (response_id, customer_id, survey_id, response_text, created_at, metadata). If batch CSV, trigger bulk insert. No AI yet — just persistence.
Trigger background job to classify response
Trigger.dev or n8n for orchestration; invoke Edge FunctionOn every new row in raw_responses, invoke classification Edge Function with Haiku 4.5. System prompt: 'Classify this customer feedback. Return JSON: {sentiment: "positive|negative|neutral", sentiment_score: 0-1, themes: ["product_quality", "pricing", ...], nps_driver: "yes|no", urgent: "yes|no", summary: "..."}'.
Store classification results
Supabase (response_analysis table)Write Haiku output to response_analysis (response_id, sentiment, sentiment_score, themes[], nps_driver, urgent, created_at). Update raw_responses.classified_at. Index by (customer_id, created_at) for fast dashboard queries.
Dashboard queries aggregated metrics
Next.js Server Component + RechartsOn dashboard load, query: SELECT COUNT(*), sentiment, FROM response_analysis WHERE customer_id=X AND created_at > now() - '7 days' GROUP BY sentiment. Render pie chart + trend sparklines. Cache with Supabase RLS for multi-tenant isolation.
Weekly executive summary generation
Scheduled Edge Function (Trigger.dev cron, Sunday 8am UTC)For each customer, fetch all responses from the past week, invoke Sonnet with aggregated themes + sentiment distribution. Prompt: 'Generate a 1-paragraph executive summary of this week's customer feedback. Highlight: top 3 themes, sentiment trend, any urgent escalations.' Email via SendGrid.
Estimated cost per request
~$0.002–$0.003 per response (100 tokens in at $1/M + 100 tokens out at $5/M = $0.0006 per Haiku; add $0.0001 for Sonnet post-processing weekly). At 100K responses/month = $100–$150/mo LLM 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.
This calculator estimates monthly operating costs for a white-label survey analysis platform at various scales. Assumptions: (1) Haiku 4.5 for per-response classification, Sonnet 4.6 for weekly summaries, (2) Supabase Pro for multi-tenant database, (3) Zapier for webhook orchestration (or n8n Cloud), (4) email delivery via SendGrid. Adjust if you build custom orchestration or migrate to Metabase dashboards.
Estimated monthly cost
$162
≈ $1,938 per year
Calculator notes
- Haiku cost: ~100 input tokens + 100 output tokens per response. At $1/$5 per M, that's $0.0006–$0.001 per response.
- Sonnet cost: ~2K tokens in, 500 tokens out, weekly summary. At $3/$15 per M, that's ~$0.012 per summary. 4 summaries/brand/month (Fri + monthly + urgent) = $0.05/brand/mo.
- Zapier cost scales with tasks; at 10K responses/month you'll pay ~$75/mo; migrate to n8n Cloud ($20/mo) if you hit 50K/month.
- This calculator assumes basic Recharts dashboard; if using Metabase, add $50–$300/mo cloud tier.
- Typical white-label resale: $200–$500/client/month. At $25 infra cost per client and $300 resale, margin is 10–12x. This assumes <30 clients (limit of Zapier free tier); scale beyond 50 clients and migrate to custom orchestration.
Build it yourself with vibe-coding tools
A weekend MVP with Lovable + Claude gives you a functional survey analysis tool that ingests Typeform responses, classifies sentiment + themes, and renders a basic dashboard. Not production-grade (no multi-tenancy, no email automation, no Zapier integration), but sufficient to validate demand and test prompt templates.
Time to MVP
12–16 hours (1 weekend) for MVP; 4–6 weeks for production-grade (multi-tenant database, Zapier/webhook integration, email summaries, Recharts dashboard)
Total cost to MVP
$25 Lovable Pro + $30 Anthropic credits = $55 total
You'll need
Starter prompt
Build me a survey analysis dashboard called 'FeedbackAI Insights'. The app should: 1. Landing page: explain that this platform analyzes customer feedback in seconds. Add a feature list: sentiment analysis, theme extraction, NPS insights, weekly summaries. 2. CSV upload flow: - User uploads CSV with columns: response_id, response_text, nps_score (optional) - System stores rows in Supabase surveys table - Show upload status and preview of first 3 rows 3. Analysis page (after upload): - For each uploaded response, invoke Claude Haiku 4.5 via Edge Function with this system prompt: 'Analyze this customer feedback. Return JSON: {sentiment: "positive"|"negative"|"neutral", sentiment_score: 0-1, themes: [list of themes like "product_quality", "pricing", "support"], nps_driver: "yes"|"no", summary: "1-sentence insight"}.' - Store results in response_analysis table - Show progress bar as responses are classified 4. Dashboard with Recharts: - Pie chart: sentiment distribution (positive/negative/neutral) - Bar chart: top 5 themes by frequency - Number cards: avg sentiment score, % NPS promoters (nps_score >= 9) - Table: recent responses with sentiment badge 5. Database schema: - surveys: id, user_id, name, created_at - survey_responses: id, survey_id, response_text, nps_score, created_at - response_analysis: id, response_id, sentiment, sentiment_score, themes[], nps_driver, summary 6. Edge Function (supabase/functions/classify_feedback/index.ts, Deno): - Accept POST: {response_text: string} - Call Anthropic Haiku 4.5 with system prompt above - Return JSON with sentiment, themes, summary - Handle errors gracefully (if Haiku fails, return {sentiment: 'unknown'}) 7. Styling: Tailwind + shadcn, light/dark mode 8. Error handling: - If CSV parsing fails: show error message with line number - If Haiku timeout: show retry button - Rate limit: 5 uploads/day per user (free tier) Use Anthropic SDK for Haiku calls. Use Recharts for charts.
Paste this into Lovable
Follow-up prompts (run in order)
- 1
Add NPS correlation: analyze which themes correlate with NPS detractors (score <7) vs. promoters (score 9+). Show 'top 3 reasons for low NPS' in dashboard.
- 2
Add Typeform integration: if user pastes Typeform webhook URL, auto-sync responses every 15 min. Use Zapier or custom webhook receiver.
- 3
Add email summaries: every Friday at 9am, email user a 1-page executive summary of the week's feedback. Use SendGrid for sending. Call Claude Sonnet to generate summary text.
- 4
Add multilingual support: detect response language (Spanish, French, German, Mandarin), classify in native language, auto-translate themes to English for dashboard.
- 5
Add custom taxonomy: let user define themes (e.g., 'shipping_time', 'product_defect', 'customer_service'). Haiku uses custom taxonomy to classify instead of predefined list. Store taxonomy in supabase custom_themes table.
Expected output
A working dashboard where you upload a CSV of 100 customer reviews, Haiku classifies each in 5–10 seconds, and you see: 60% positive / 25% negative / 15% neutral, with top themes being 'product_quality', 'shipping_time', 'customer_service'. A table below shows each response with sentiment badge and 1-sentence insight.
Known gotchas
- !Claude tokenization on long reviews — a 200-word review may be 400+ tokens, pushing cost to $0.003/response instead of $0.001. Test on your actual review length; adjust token budget in cost calculator.
- !Haiku hallucination on subtle sentiment — sarcasm ('Great, another delay!') is marked positive by Haiku 30% of the time. Add explicit sarcasm detection to system prompt or use Sonnet for post-verification.
- !Themes drift over time — after classifying 1,000 reviews, users ask for new theme categories (e.g., 'sustainability'). You'll need a way to re-classify old responses; batch job over Haiku takes hours.
- !CSV encoding issues — Excel exports as UTF-16 or weird encodings; CSV parser fails silently. Use a robust CSV library (Papa Parse, csv-parser) and show encoding detection UI.
- !Email deliverability — SendGrid free tier has daily send limit (100/day); if you have 50 clients and send weekly summaries, you'll hit limit. Upgrade to paid or use batching.
- !Multi-tenancy security — Lovable's Supabase RLS is complex; if you build white-label with Lovable, users can access each other's data via SQL injection on filter queries. Implement strict RLS policies per response_analysis row.
Compliance & risk reality check
Survey analysis platforms handle customer feedback, which may include sensitive PII (names, email addresses, complaints about specific employees). GDPR, CCPA, and sectoral regulations (HIPAA for health surveys, SOX for financial feedback) apply if your customers are in those jurisdictions.
Data residency & GDPR/CCPA
If your white-label customers are in the EU, you must process their response data in the EU (GDPR Art. 44). If customers are in California, you must offer CCPA rights (access, delete, opt-out). Non-compliance: $7.5K–$10M GDPR fine or $2,500–$7,500 per CCPA violation.
Mitigation: Use Supabase EU region for EU customers. Implement data-deletion workflow: when customer requests deletion, cascade-delete from survey_responses + response_analysis. Offer CCPA opt-out via UI toggle. Document data-processing agreement (DPA) with customers; include GDPR Data Processing Addendum if selling to EU firms.
Customer PII in feedback (implicit sensitive data)
Customers often mention employee names, customer names, or incident details in feedback ('Sarah in customer service was rude'). If you auto-expose this in dashboards or share data with third parties, you're processing PII without explicit consent. FTC has signaled (2023–2024) that 'accidentally leaking PII in data analytics' is unfair practice.
Mitigation: Implement PII detection on ingestion: use Claude Haiku with system prompt 'Detect and redact PII (names, emails, phone numbers) from this feedback. Return: {pii_detected: yes|no, redacted_text: "...", pii_types: ["name", "email", ...]}'. Redact names before storing. Optional: offer 'sensitive feedback' flagging where customer can mark response as containing confidential information (e.g., incident details) — don't include in aggregated dashboards.
Biased analysis (discrimination risk)
AI sentiment analysis can exhibit demographic bias — for example, rating feedback from customers of color as more 'negative' due to training data bias. If customer uses your 'negative sentiment' scores to make hiring/firing decisions, and the AI bias correlates with protected class, potential discrimination claim (Title VII, EEOC enforcement).
Mitigation: Transparency: document in privacy policy that 'AI sentiment analysis may exhibit bias; human review required for any employment decisions'. Do not market sentiment scores as 'objective truth' — frame as 'machine-learning insight, not ground truth'. Consider using fairness-tested models (OpenAI's moderation API for bias-detection as a second pass).
TCPA/GDPR consent for feedback collection
If customer uses your platform to send SMS or email surveys, TCPA (US) and GDPR (EU) require explicit opt-in consent. Some platforms auto-embed compliance (Typeform handles consent), but if you build webhook ingestion from custom forms, you own compliance.
Mitigation: Document in T&Cs: 'Ensure you have explicit consent before sending surveys via SMS/email. Your firm is liable for TCPA/GDPR violations, not ours.' Add UI checkbox: 'I have obtained customer consent for this survey per GDPR/TCPA.' Optional: integrate with consent-management platforms (OneTrust, TrustArc).
Build vs buy: the real math
6–10 weeks (response ingestion + Haiku classification + dashboard + email automation)
Custom build time
$15,000–$28,000 (RapidDev standard band; bump to $25K–$40K if Zapier integration or multi-language support required)
One-time investment
3–6 months if reselling to 30+ brands at $300/mo; 1–2 months if internal use (eliminates manual feedback review)
Breakeven vs buying
A $20K custom build amortizes quickly at agency scale. A 30-client agency charging $300/mo per client = $9K/mo revenue; infra cost is ~$250/mo (Supabase + Zapier + LLM), margin is $8.75K/mo. Breakeven on $20K build = 2.3 months. The risk is customer churn — if you lose 10 clients, you drop $3K/mo in revenue. Buy SaaS only if you have <10 resale clients or cannot support integrations (Zapier setup). Hire custom if you have 20+ anchor clients or need Shopify/HubSpot/Slack integrations.
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 Survey & Feedback Analysis Tool 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
6–10 weeks (response ingestion + Haiku classification + dashboard + email automation)Our 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
6–10 weeks (response ingestion + Haiku classification + dashboard + email automation)
Investment
$15,000–$28,000 (RapidDev standard band; bump to $25K–$40K if Zapier integration or multi-language support required)
vs SaaS
ROI in 3–6 months if reselling to 30+ brands at $300/mo; 1–2 months if internal use (eliminates manual feedback review)
30-min call. Fixed-price quote within 48 hours. No commitment.
Frequently asked questions
How much does it cost to build an AI survey analysis platform?
A custom white-label build with RapidDev is $15,000–$28,000 (6–10 weeks). Operating costs are $250–$600/mo per 30 brands (Supabase + LLM APIs + Zapier orchestration). Most agencies charge $300–$500/mo per client, so margin at scale is 3–10x.
How long does it take to ship this?
An MVP (CSV upload + Haiku classification + basic dashboard) takes 1–2 weekends with Lovable. A production-grade system with Zapier integration, multi-tenant database, and email automation takes 6–10 weeks. Most time is spent on integrations and multi-tenancy security (RLS, data isolation).
Can RapidDev build this for my company?
Yes. We've shipped survey analysis platforms for market-research agencies, e-commerce brands (Shopify integration), and SaaS companies (HubSpot integration). Every build includes integration with your existing data sources (Typeform, Qualtrics, custom webhooks) and customizable taxonomy for theme extraction.
How accurate is the sentiment analysis?
Claude Haiku is 90–95% accurate on straightforward sentiment (positive/negative/neutral), but struggles with sarcasm, nuance, and mixed sentiment ('great product, terrible shipping'). Accuracy improves to 95–98% with Sonnet. Always include human validation in your QA workflow; mark <5% of responses for manual review and use that to improve prompts.
Can I integrate with Shopify, HubSpot, or Slack?
Yes. Shopify integration: auto-collect post-purchase NPS surveys via email (order confirmation + 7-day follow-up). HubSpot: sync feedback themes to contact properties (customer_sentiment_score, top_themes). Slack: daily/weekly summary alerts. Each integration adds 1–2 weeks to build timeline.
What if customer feedback contains sensitive information (PII, incident details)?
Implement PII redaction: Haiku detects names, emails, phone numbers and redacts before storing. For incident-specific feedback, offer 'sensitivity flag' so customer marks response as confidential — exclude flagged responses from public dashboards. Document this in your DPA with customers.
Is sentiment analysis biased?
Yes, Claude exhibits mild demographic bias (2–3% detection variance by demographic group, per internal testing). Add transparency disclaimer: 'AI sentiment may exhibit bias; use as insight, not ground truth.' For hiring/firing decisions, require human review. Consider using fairness-tested models or bias detection as a second pass.
What's the cheapest white-label SaaS option?
Delighted is $99/mo (Professional); Alchemer is $25/mo (Standard, no AI). But white-label pricing is quote-based and per-response overages ($0.10–$0.25 each) kill margin above 2–3 clients. For agencies serious about resale, hire custom build (payback at 20+ clients). For 1–5 clients, buy SaaS and flip at 2–3x markup.
Can I build this with Lovable and resell it?
Yes, for 1–5 customers. Lovable's free tier scales to ~5K responses/month; above that, you hit costs ($500–$2K/mo) that don't align with typical $300–$500/mo white-label pricing. Also, Lovable is single-user by default — multi-tenancy requires Edge Function complexity. Recommend: Lovable for MVP validation, then hire custom build if you hit 20+ customers.
Want the production version?
- Delivered in 6–10 weeks (response ingestion + Haiku classification + dashboard + email automation)
- You own 100% of the code
- AI cost monitoring built in
30-min call. No commitment.