Problem
Airtable bases often decay quietly. Duplicate fields, weak relationships, status sprawl, null-heavy data, and improvised workflows pile up until the base becomes hard to trust, hard to extend, and expensive to untangle.
Constraint mapping
Airtable Base Doctor
A schema-aware Airtable diagnostics and redesign app that scores base health, surfaces structural issues, and turns the detected model into generated interfaces, migration plans, and shareable artifacts.
Product screens
Report Export
The recommendation set can be packaged into a print-friendly artifact for stakeholder review and rollout planning.
Health Score Overview
The opening surface frames overall base health, top recommendations, and generated opportunity areas in one pass.
Redesign Workspace
Analysis findings roll directly into proposal variants, migration planning, impact review, and guarded write-back guidance.
Health Score Overview
The opening surface frames overall base health, top recommendations, and generated opportunity areas in one pass.
Report Export
The recommendation set can be packaged into a print-friendly artifact for stakeholder review and rollout planning.
Redesign Workspace
Analysis findings roll directly into proposal variants, migration planning, impact review, and guarded write-back guidance.
Execution Snapshot
The strongest signal first, then the operating context around it.
Build Narrative
A clean story from constraint to shipped outcome.
01
Build
I built a schema-aware product that connects to Airtable through OAuth, analyzes schema and sample records, scores base health across four lenses, and turns the findings into generated interfaces, redesign proposals, migration guidance, and exportable artifacts.
System design
Outcome
The result is a differentiated Airtable review product that feels closer to a consultant's internal toolkit than a generic dashboard. It demonstrates how diagnostics, generated interfaces, migration planning, and constrained AI assistance can live inside one production-grade app.
Production outcome
Framing
Defining the product and the operating constraints.
The product sits at the intersection of Airtable consulting, schema design, and AI-assisted workflow tooling. The hard part was not just detecting problems in a base, but packaging those findings into outputs that operators and stakeholders could actually understand and use. I built the system in layers: deterministic analysis first, generated previews second, redesign planning third, and AI synthesis as an explicitly separate assistive layer. That sequencing kept the product explainable while still allowing richer planning and export surfaces.
Systems Index
Next.js
TypeScript
Airtable
OAuth
Prisma
PostgreSQL
OpenAI
Key features in scope
OAuth-based Airtable connection with cached schema and sample-record analysis
Health scoring across schema quality, workflow quality, data quality, and UX opportunities
Generated dashboard, intake form, and client portal previews from detected schema structure
Redesign workspace with proposal variants, issue-resolution mapping, migration planning, and impact previews
Exportable redesign brief and PDF-style report with evidence and sample-record previews
Guarded write-back suggestion layer that defines what could be assisted later and what must stay manual
Role and product posture
Role: End-to-end solo build across product design, Airtable integration, auth, analysis-engine architecture, persistence, generated preview systems, redesign workflow, and report/export surfaces.
Category: AI / Workflow Tooling
Engineering
Building the core system and choosing where to be opinionated.
I built a schema-aware product that connects to Airtable through OAuth, analyzes schema and sample records, scores base health across four lenses, and turns the findings into generated interfaces, redesign proposals, migration guidance, and exportable artifacts.
Systems Index
Next.js 16
React 19
TypeScript
Tailwind CSS v4
shadcn/ui
Recharts
Next.js Route Handlers
Better Auth
Architecture choices
Next.js 16 App Router application split into marketing, demo, and authenticated workspace route groups
OAuth-backed Airtable integration with encrypted token storage, schema snapshots, sample-record fetches, and webhook-triggered reanalysis
Deterministic analysis engine that profiles each base and runs schema, workflow, data-quality, and UX-opportunity checks
Generated asset pipeline that derives dashboard, form, and portal previews from detected schema patterns
Redesign workspace that composes proposal generation, resolution mapping, migration planning, impact previewing, evidence inspection, and export artifacts
PostgreSQL + Prisma persistence for users, bases, snapshots, analyses, generated assets, request logs, and webhook events
Key decisions
Keep deterministic findings separate from AI synthesis so trust, scoring, and evidence stay inspectable
Ship a public demo workspace first so the product can prove value before OAuth onboarding friction
Treat redesign guidance as suggestion-first, with explicit consent and scope boundaries for any future write-back path
Cache schema snapshots and analysis runs so repeated reviews stay fast and historically comparable
Hardening
Turning the build into something resilient enough to matter.
The result is a differentiated Airtable review product that feels closer to a consultant's internal toolkit than a generic dashboard. It demonstrates how diagnostics, generated interfaces, migration planning, and constrained AI assistance can live inside one production-grade app.
Systems Index
Shows product judgment beyond CRUD by connecting analysis, generated surfaces, and rollout planning into one system
Demonstrates thoughtful integration design across OAuth, encryption, caching, webhook reanalysis, and audit-friendly persistence
Balances deterministic systems with AI assistance in a way that preserves operator trust and control
Presents a high-signal portfolio narrative: technical depth, strong UX, and commercially legible workflow tooling
Results after shipping
Delivered a polished workflow that moves from health scoring to redesign planning inside one coherent product surface
Built reusable analysis modules, proposal generators, and export artifacts instead of a one-off demo experience
Made complex Airtable cleanup easier to explain through shareable briefs, PDF-style reports, and sample-record previews
Produced a strong portfolio piece that combines integration work, deterministic analysis, generated UX, and carefully bounded AI assistance
Constraints
Airtable access had to stay safe and permission-aware while the product remained read-only
Analysis needed to feel credible without pretending heuristic detection was perfect
Complex schema changes had to be explained clearly enough for non-developers to review
Redesign guidance needed to stay useful even when optional AI or export layers were absent
Lessons
What the build taught me.
01
Diagnostic products get much stronger when findings connect directly to downstream artifacts and rollout plans
02
Keeping deterministic evidence separate from AI synthesis makes trust easier to maintain
03
Read-only integrations can still feel high leverage when the review, redesign, and export surfaces are strong
04
A strong demo mode is often the fastest way to communicate scope for integration-heavy products
Retrospective
If I pushed this further, I would add a fully consented write-capable integration lane for low-risk additive changes and expand the real-base redesign workspace beyond the demo bundle.
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