Agree on the outcome before generating the work.
Goals, standards, security boundaries, and success metrics are explicit before a build begins.
Define the right problem.
Goals, constraints, and success metrics first.
About · AI product studio Makati, Philippines · founded 2024
One team owns the outcome.
Pixelmojo is an AI product studio founded in 2024 by Lloyd Pilapil in Makati, Philippines. Radar is our self-service AI visibility platform, which we operate. Vector and Hive are custom builds: you own the application code, and we maintain the intelligence platform behind it.
Scoped up front · Shipped in checkpoints · Owned by you
One context · four disciplinesStudio active
Strategy
One partnerNo vendor chaos.
One team stays accountable from concept to launch.
Design + Engineering
One shared context.
Fewer handoffs. Better decisions. Faster delivery.
Founded by
LP
Thirty years of design. Twenty years in product.
What reached productionProducts, platforms, and brand systems that reached production. Not concepts left in a slide deck.
40+Projects delivered
20+Years in product
30+Years in design
AI should remove handoffs. It should not create another vendor, another black box, or another layer nobody owns.
That is why Pixelmojo combines product strategy, design, engineering, and AI in one studio. The people shaping the problem stay close to the people shipping the solution. Context survives from the first conversation to the final handoff.
02 / Founded by
Thirty years of design. Twenty in product.
From art direction to enterprise product systems to AI strategy, the through-line has stayed the same: make complex systems clear enough to use.
Product20+years
Through-lineMake complex systems clear enough to use.
Design30+years
Built with
- Salesforce
- Parsons
- Egis
Founded by Lloyd Pilapil
From art direction to enterprise product systems to AI strategy, Lloyd has spent his career making complex work understandable, usable, and ready to ship.
Recent confidential work includes a complete brand ecosystem for an AI startup, a custom logistics tracking system, and a real-estate earnings platform with predictive analytics. Each reached production. Each was delivered on time.
A closer look · 2:25
Meet Pixelmojo.
Lloyd Pilapil, founder of Pixelmojo, and Scout, the guide to Radar, take a closer look at Radar, Radar Sites (a visual website builder in development), Vector and Hive.
Read the transcript
When someone asks AI who to choose, does it understand your business? Lloyd, over here! I'm Lloyd, founder of Pixelmojo. Meet Scout, our guide to Radar. I found something. Come look. This says you only work with big companies. But your website says you help smaller teams too. That's quite a difference. Radar helps you inspect AI answers and the evidence behind them. So we fixed the website and we're done? We make a useful change, then check again. New evidence tells us what to do next. Right, no guessing. And we're building Radar Sites, a visual website builder. Let me get this side. New leads? Send them all to sales. First, find the fit. Vector qualifies incoming leads and routes the next step around your sales process. This one needs a closer look. Hive helps AI co-workers share context and hand work to the right place. Even when a person needs to take over? Especially then. That's Pixelmojo. AI products, visibility, brand and growth. With Vector and Hive, you own the custom application. We maintain the intelligence. But I keep the magnifying glass. Deal. Welcome to Pixelmojo. One more thing. A closer look? Always.
This is an animated film. The AI answer, website and lead scenes are illustrative examples, not a real client result. More in our videos and on the Pixelmojo YouTube channel.
04 / The operating principles
Fast work still needs hard edges.
AI increases output. Our operating model protects the things output alone cannot guarantee: judgment, security, coherence, and ownership.
Keep strategy close to implementation.
Designers and engineers work from the same constraints, so intent does not dissolve between handoffs.
Review what matters at every checkpoint.
Speed is useful only when the code, interface, and result can survive production.
Leave with the system, not a dependency.
Application code, assets, documentation, and operational knowledge transfer to your team.
Thread-based engineering
AI-native. Governance first.
Parallel AI work moves inside defined constraints, not around them. Quality and security boundaries exist before the first prompt, and human review remains part of every shipping decision.
Map the whole system.
Users, data, architecture, goals, and existing constraints become shared context.
Define what cannot break.
Security boundaries, code standards, and review criteria are set before generation.
Move faster without fragmenting.
Specialized workstreams share constraints and return reviewable outputs.
Verify before shipping.
Architecture, behavior, security, and user experience pass accountable review.
Less reworkConstraints travel with the work.
Clearer ownershipEvery decision has an accountable reviewer.
Production confidenceQuality is designed in, not inspected at the end.
05 / How projects run
Three accountable stages. Concept to launch.
Every engagement has a defined checkpoint, timeframe, deliverable, and owner.
Week 0~ 9 to 16 weeks concept to launch
Stage 01
Scope and map.
Align on the goal, map the system, and define the result that matters, whether the work is a product, a brand system, AI visibility, or qualified pipeline.
- Goal and stakeholder alignment
- Scope, standards, and guardrails
- Success metrics and delivery plan
Timeframe1-2 weeks
Outcome: one scoped plan everyone has agreed to before the build starts.
06 / Work that reached production
Different systems. The same standard.
Some client work stays confidential. The useful proof is what shipped, who owned it, and whether it survived the handoff.
AI startup · Brand ecosystem
One identity from company story to product interface.
Brand foundations, visual grammar, product surfaces, and launch materials delivered as a coherent system.
Across every engagement
On time. In production. Owned by the client.
No invented case-study metrics. No concepts presented as shipped work.
Logistics · Operations platform
A custom tracking system built around the real workflow.
Operational visibility, usable status signals, and a maintainable application handed to the team.
Case studyReal estate · Predictive analytics
Turn complex earnings data into a decision surface.
Predictive modeling and clear product design brought into one production platform.
Case study07 / The production stack
Battle-tested tools. Chosen for the system.
Technology is selected for reliability, security, maintainability, and fit, not trend value.
AI and intelligence
- Anthropic Claude
- OpenAI
- Google Gemini
- Claude Code
- Custom AI pipelines
Engineering
- Next.js + React
- TypeScript
- Supabase
- Vercel
- Tailwind CSS
Design and quality
- Figma
- Storybook
- Chromatic
- Automated testing
- Human review gates
Integration
- MCP
- Custom MCP servers
- Secure APIs
- Client systems
- Operational handoff
The stack can change. The standard does not: secure defaults, clear ownership, documented decisions, and a system your team can keep operating.
Self-serve · live today
Start with what AI sees.
Run Radar’s six technical readiness checks free. See the gaps before deciding what to build.
Get My Free SnapshotOne conversation · no pitch deck
Bring us the constraint.
Product, qualification, autonomous operations, visibility, brand, or growth. We will tell you where we can help and where we cannot.
Book a strategy call