Independent SaaS Project
Satiaphic — turning customer feedback into operational improvement workflows.
A multi-tenant B2B SaaS project that ingests Google Maps reviews and QR/link/widget feedback, then organizes them into triage workflows, AI-assisted feedback analysis, proposals, work items, and resolution tracking.
Visit Satiaphic ↗189
Commits
3
User Roles
~40
Permissions
6-stage
AI Pipeline
6
Vercel Cron Jobs
20
SaaS Tools Researched
Problem
The gap this fills
Businesses collect reviews and customer feedback across public review platforms and direct feedback channels, but often lack a structured way to detect recurring issues, prioritize improvements, assign work, and track resolution.
Satiaphic was designed to connect feedback collection with operational follow-through, turning passive review data into practical internal workflows.
My Role
Product Builder & Full-Stack Developer
- ▸Product research and competitive analysis (20 SaaS tools)
- ▸SaaS workflow design and product architecture
- ▸Full-stack application development
- ▸Multi-tenant architecture implementation
- ▸Authentication and role-based permissions system
- ▸AI-assisted feedback analysis pipeline
- ▸Dashboards and operational workflow UIs
- ▸Billing and subscription workflow integration
- ▸Security-focused controls and audit tracking
- ▸Go-to-market materials and product documentation
Workflow
Product workflow
The product focuses on the full feedback lifecycle: from collection to triage, analysis, prioritization, execution, and follow-up.
01
Google Maps Reviews
02
QR / Link / Widget Feedback
03
Feedback Inbox
04
AI Theme Discovery
05
Proposals
06
Work Items
07
Resolution & Changelog
Features
Core features
01
Feedback Inbox
Triage, filter, assign, prioritize, and resolve feedback items. Provides a structured operational view of all incoming feedback across channels.
02
Google Maps Review Ingestion
Pulls public review data into the product workflow, making external review activity part of the internal feedback process.
03
QR / Link / Widget Feedback Collection
Supports direct customer feedback collection through public forms, QR codes, and embeddable widget touchpoints.
04
AI-Assisted Theme Discovery
Integrates LLM APIs into topic extraction and structured analysis, combined with deterministic clustering and quality review before proposal drafting.
05
Proposals and Work Items
Converts recurring feedback themes into internal improvement proposals and execution tasks assigned to team members.
06
Team Roles and Permissions
Supports owner, admin, and member role separation with approximately 40 granular permissions controlling access to features and data.
07
Billing and Subscription Workflows
Includes plan-based feature gating and subscription-related flows through DodoPayments integration.
08
Dashboards and Analytics
Gives business users visibility into feedback trends, resolution progress, and operational follow-up metrics.
Architecture
Technical architecture
Satiaphic is built as a multi-tenant SaaS application where business-level data isolation is enforced through authenticated session context and tenant-scoped database access.
Frontend
- Next.js
- React
- TypeScript
- Tailwind CSS
Backend
- Next.js API route handlers
- Nodemailer / SMTP
Database
- MongoDB
- Native MongoDB driver
AI
- LLM APIs
- LLM-assisted feedback analysis pipeline
- Deterministic clustering
Payments
- DodoPayments
Background Jobs
- Vercel Cron (6 scheduled jobs)
Hosting
- Vercel
Security
- Zod validation
- Rate limiting
- Tenant scoping
- Env-based secrets
AI Pipeline
How the AI pipeline works
Satiaphic integrates LLM APIs into a 6-stage feedback analysis workflow. The system combines LLM-assisted feedback analysis with deterministic clustering, quality review, and proposal drafting. LLM-based processing handles topic extraction only. Product logic, clustering, and execution are deterministic. No custom model training. No RAG.
01
Collect
Business-specific feedback gathered via Google Maps, QR codes, links, and embedded widgets.
02
Extract
LLM API extracts topics, signals, and patterns from raw feedback text.
03
Cluster
Related items are grouped using deterministic clustering logic.
04
Review
Generated themes are reviewed for quality before surfacing to users.
05
Propose
Improvement proposals are drafted from validated themes.
06
Execute
Proposals link to operational work items for team follow-through.
Security
Security-focused controls
The application includes layered controls for authentication, authorization, validation, and data integrity. The approach is appropriate for a multi-tenant SaaS product handling business-level data.
Research
Product and market research
Satiaphic was shaped through competitive research across 20 SaaS tools in review management and feedback management. The research helped define the product gap: many tools collect or display feedback, but fewer connect feedback directly to internal improvement workflows.
Categories analyzed included review management, feedback management, product feedback, and customer operations tools — informing Satiaphic's positioning around operational feedback follow-through rather than just feedback display.
Honest Assessment
What I'd improve next
Satiaphic is a working product, but it has gaps. These are the things I'd prioritize in the next phase.
01
Public demo environment with seeded data so visitors can see the product without a real account.
02
Automated tests around critical flows — particularly the AI pipeline, billing, and permission checks.
03
A demo video and screenshot set for the case study and marketing pages.
04
Integration options for Slack, Jira, or Linear to connect feedback workflows to existing team tools.
05
Public documentation covering the architecture, tenant model, and AI pipeline in more depth.
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