Web Apps That Think, Analyze, and Generate
AI-powered web application development from Davao City, Philippines. I build full-stack apps with embedded AI features — recommendation engines, document intelligence, semantic search, and LLM workflows — using Next.js, Python, and TypeScript.
You're doing manually what software should do automatically.
Reading through 50-page contracts to find the one clause that matters. Writing product descriptions for 200 SKUs one at a time. Building a spreadsheet report that should be a live dashboard. Copying data between systems because they don't talk to each other.
Most of this work is high-volume, low-variation, and mentally draining. It's also exactly where AI-powered web applications outperform humans — processing thousands of documents in minutes, generating consistent output at scale, and spotting patterns in data that would take days to find manually.
A production web app with AI built into the workflow.
I design and build full-stack AI web applications — not demos, not Jupyter notebooks, but real tools deployed to production that your team logs into and uses daily. From Davao City, Philippines, I deliver remotely with async-first communication that works across US, AU, and EU time zones.
- Document analyzers that extract structured data from unstructured PDFs and contracts
- Content generation platforms that produce on-brand output at scale
- AI dashboards that surface insights from your data without a data science team
- Recommendation engines that increase average order value or content engagement
- Predictive tools that forecast demand, churn, or risk from historical patterns
AI Web App Types I Build
Document Intelligence
Upload PDFs, contracts, invoices, or reports — the app extracts specific fields, flags anomalies, compares against templates, and produces structured summaries. Processes 10,000+ documents per day.
Content Generation Platform
A web interface where your team generates product descriptions, blog drafts, email sequences, and ad copy using your brand voice and style guidelines — not generic prompts.
AI Analytics Dashboard
Connect your data sources, define the metrics that matter, and get a live dashboard with natural-language Q&A. Ask questions like "which product line had the worst return rate last quarter" and get an answer.
Recommendation Engine
Collaborative and content-based filtering that recommends products, articles, or services based on user behavior and item similarity. Increases engagement and average order value measurably.
Predictive Tools
Forecast demand, identify at-risk customers before they churn, score incoming leads by close probability, or flag transactions by fraud risk — using patterns in your historical data.
AI Web App Pricing, Scoped to Deliver
One-time project fee
Single AI feature integrated into a new or existing web interface.
- Single AI feature (analyzer, generator, or classifier)
- Clean React / Next.js frontend
- One LLM API integration (OpenAI or Claude)
- File upload support (PDF, CSV, DOCX)
- Results export (JSON, CSV, PDF)
- 2 weeks post-launch support
One-time project fee
Full AI web application with multiple features, user auth, and data persistence.
- Up to 4 AI features in a unified application
- User authentication and role-based access
- Database with persistent history and search
- Admin panel for configuration and content management
- API layer for integration with existing tools
- Deployment on Vercel, Railway, or your cloud
- 30 days post-launch support with weekly check-ins
Scoped per project
Multi-tenant platforms, large-scale document pipelines, or full AI product builds.
- Unlimited feature scope — built to your spec
- Multi-tenant SaaS architecture (if applicable)
- High-volume async processing pipelines
- Custom model fine-tuning option
- Full CI/CD pipeline and infrastructure setup
- On-premises or VPC deployment available
- Retainer option for ongoing development
From Requirement to Production App
Requirements Workshop
An async or live session to define what the app does, what data it processes, who uses it, and what "done" looks like. You get a written spec with wireframes before any code starts.
Architecture Design
The right stack for your use case: which LLM, which database, which processing approach. A document-heavy app needs a different architecture than a real-time recommendation engine.
Sprint Development
Built in 1-week sprints with a working demo at the end of each. You see real progress — not a big reveal at week 8. Feedback is incorporated in the next sprint.
AI Behavior Testing
The AI layer is stress-tested with edge-case inputs, adversarial data, and volume load. Outputs are validated against expected results before the app is considered production-ready.
Deploy & Document
Deployed with logging, error alerting, and a written handoff guide. The codebase is documented enough for another developer to maintain without requiring you to come back to me.
What Sets These Apps Apart From Demos
Structured AI Output
The apps use function calling and JSON schema enforcement so AI output is always machine-readable — not free text that requires another parsing step. Data goes directly into your database.
Handles Real Volume
Background job queues handle large batches asynchronously — 500 documents don't time out the browser. Users see progress indicators while processing runs in the background.
Built on Your Data
Every app is grounded in your actual data sources — your documents, your database, your APIs. The AI doesn't operate on assumptions; it operates on facts you provide.
Full-Stack Ownership
Frontend, backend, database, AI layer, deployment — all delivered as a single cohesive system. No separate frontend developer and AI engineer who don't communicate.
Explainable Outputs
Where decisions matter (risk scoring, contract flagging, churn prediction), the app surfaces the reasoning — which clause triggered the flag, which factors drove the score.
Remote-First Delivery
Every project is managed async with Loom demos, written updates, and GitHub PRs. Clients in the US, UK, and Australia get the same communication quality as if I were in the same office.
AI Apps Built and Shipped
"We were manually reviewing 80–100 contracts per week for specific clause deviations. Johnbert built a document intelligence app that does it in seconds per document — it extracts the relevant clauses, compares them against our standard template, and flags deviations with a severity score. We review the flagged ones. The rest are auto-approved. It's saved us roughly 30 hours a week."
"We had 2,400 product listings with outdated, inconsistent descriptions. Johnbert built a content generation tool that took our product data, fed it through our brand guidelines, and generated new descriptions for every SKU in a single batch run. Quality was high enough that we used 90% with only light edits. Took 3 weeks to build what would have taken a copywriter 6 months."
"We needed a dashboard our non-technical managers could actually use — not Tableau, not Power BI. Johnbert built a natural-language analytics dashboard where managers type a question and get a chart and a summary. Sales trends, inventory gaps, store performance — all accessible without knowing SQL. It changed how our regional managers make decisions week to week."
Questions About AI Web Application Development
Johnbert Oñez
AI Solutions Engineer & Full Stack Developer · 6+ yrs · 50+ projects
Based in Davao City, Philippines. Specialises in production AI systems, full-stack web applications, and WordPress. Remote-first, async-friendly, fixed-fee projects.
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AI systems, SaaS platforms, WordPress solutions — whatever the scope, I bring craftsmanship and precision from day one.