Research, methods, failures, and build notes for teams earning AI visibility and shipping AI products. No recycled playbooks. No unexplained scores.
Turn one SEO or AI visibility finding into a task with a reason: evidence, an owner, the fix and an acceptance check, worked through our own Radar audit.
The library is organized around decisions, not content categories invented for search engines.
Start with decision-stage visibility, evidence architecture, and what an AEO score can and cannot tell you.
Read foundationsInspect how Radar scores, how entity resolution works, and how we test our own audit architecture.
Read the methodsFollow experiments across engines, referrals, citations, and volatile recommendations.
Read field notesThe most recent field notes, newest first. Filter by what you are working on, or search the whole library.
OpenAI is retiring the gpt-4o-mini snapshot Radar used, so Radar's ChatGPT results now use gpt-6-luna. What we tested, what we fixed, and why scores may move.
Before you act on an AI visibility identity finding, check whose business it describes. Four checks for agencies, in order, tested on a wrong-company audit.
Our proposed AI Visibility Evidence Standard, v1: eight things every AI visibility report should disclose, tested on a real Radar audit of our own site.
A page took 64,191 Search Console impressions and zero clicks in 87 days. What the data shows, what it cannot show, and why it explained none of our lost traffic.
AI in a B2B company should run as a directed loop: a human sets the question, Radar measures, Vector qualifies, Hive builds, and a human decides. Full model.
How to evaluate a GEO agency before you sign. Four green flags, five red flags, and the baseline evidence any credible AI search partner should show you first.
An AI visibility audit can look credible while measuring the wrong company. What one failure taught us about entity resolution and audit integrity.
Can you trust the score an AI visibility tool gives you, tomorrow as well as today? We test our own scoring weekly and audited our whole stack. Here is the proof.
What is a good AEO score? See what AEO checkers actually measure, how grades work, real data from 59 audits, and the fixes that raise a failing score.
Buyers ask AI which option to choose before they reach your site. Decision-stage AI visibility is whether you win that recommendation, not just appear.
Grok answers brand questions for 117M monthly users per the SpaceX S-1. We measured what it says, and it got our own brand wrong. Check yours today.
You cannot control what AI recommends about your brand. Here is why AI answers are volatile by design, and the five things to measure instead.
Publishing more content will not make you visible in AI answers. Evidence architecture, your claims, entities, sources, and structure, is what gets you cited.
Most AI visibility scores are opaque grades you cannot defend. Here is how Radar scores AI visibility in three separated layers, with a trail behind every number.
Google Preferred Sources is the one AI visibility lever your audience controls, not the model. Here is how it works, who qualifies, and how to earn it.
The experience designer is evolving from executor to orchestrator. Why legacy UX did not get replaced by AX Design, it got extended, and how to make the climb.
Google says llms.txt is unnecessary. Chrome Lighthouse audits it anyway. We fact-checked five studies on what llms.txt really does for AI visibility in 2026.
When AI engines link your brand to the wrong same-named entity, you still get cited, but the citation points at someone else. Here is how brand disambiguation fails and how to fix it.
Reddit is the most-cited source in AI answers. The Reddit Brand Monitor finds what is said about your brand there, scores it, and flags AI-generated seeded posts.
The YouTube Brand Monitor tracks what is said about your brand on YouTube, scores it on five dimensions, and reads the actual transcripts so you see the coverage AI models may draw on.
A failing AEO score is only useful if you can fix it. Here is how Radar fix prompts and the AI advisor turn audit findings into shipped fixes, no rewrite required.
We analyzed 59 real AEO audits from the Radar platform. The average score is 26 out of 100 and 9 in 10 sites scored below 40. Here is why, and how to fix yours.
AI monitoring tracks what AI says about your brand. AI technical readiness checks whether AI can reach and read your site. Some tools now do both. Here is how the stack fits together.
AI agencies that retain your IP can cost several times more over 3 years. Contract clauses, red flags, and the Build + Platform + Performance model that ends vendor lock-in. 2026 guide.
A dated note on what Google announced about information agents at I/O 2026, what we inferred about freshness, and why we no longer present that inference as fact.
Programmatic Anthropic Agent SDK hooks reference. The core events, full TypeScript signatures, PreToolUse, PostToolUse, Stop, SubagentStop, UserPromptSubmit with worked examples.
A dated note on Gemini 2.5 and YouTube video understanding: the real 2025 dates, what our YouTube Brand Monitor measures, and the claims we withdrew in October 2026.
First findings from the Radar Brand Index. 50 named brands assessed with Radar's AI Readiness Score. Stripe and BetterUp lead at 88/100. Hims and Hers hit 4/100. Radar could not scan three of them at all. Here is what the data says.
Static AI SEO checks never query an LLM. They infer visibility from proxies. Static analysis cannot tell you what ChatGPT says about your brand. Only asking ChatGPT can.
After a free AI visibility check: when one $5 audit is enough, when a pack or Pro fits, and when to hand the work to a strategy sprint. Prices as of October 2026.
Radar by Pixelmojo is generally available. Free tier runs 6 technical readiness tools. Paid from $5 per audit unlocks 7 more LLM-powered tools. Here is the launch story and the pricing logic.
How a Radar audit runs 13 AI visibility tools in staged batches, reads their results together, and orders the fixes. What one audit catches that separate checks miss.
Original benchmark data from 82 real Radar platform audits across 6 core industries. Average AI readiness score: 45/100. Only 1 domain has scored an A so far. Here are the findings.
Track what ChatGPT, Perplexity, Claude, and Gemini say about your brand. Free and paid methods, tools compared, and step-by-step setup.
Compare 10 AI visibility tools with pricing checked in September 2026: engines tracked, AI crawler and llms.txt checks, free options, and how to choose.
Rankings and backlinks do not show what ChatGPT, Claude, Gemini or Perplexity say about you. What SEO tools measure, what AI answers need, and how to check both.
AI Technical Readiness is the infrastructure layer that ensures AI can crawl, parse, and cite your site. Monitoring shows symptoms. Technical readiness checks the infrastructure behind them.
How fixes on our own site became Radar, told from the git history: what we changed from October 2025 to March 2026, and what that history cannot prove.
A dated note on Radar's private beta, 16 March to 2 April 2026: who requested access, what we recorded, and why we withdrew the "50 users" figure.
Ghost Protocol gives GitHub Copilot a team of 8 named specialist agents running 9 disciplined execution patterns. One installer script. Agents execute, leave clean results, disappear.
Radar now runs 13 AI visibility tools in staged batches with DIY implementation features: AI prompt generator, 5 implementation threads, llms.txt and schema generators, single-tool re-verify, and progress tracking. Audit, understand, fix.
AEO Page Auditor scores pages for answer engine readiness. Answer Engine Citation Tester checks if AI engines cite your URL. Both now generate AI-ready implementation prompts via Radar.
How to use Radar as of October 2026: run the free technical check, unlock the full 13-tool audit, read the evidence in AI answers, and turn findings into fixes.
A dated note on our 27 March 2026 blog retrofit: what we changed in 21 posts, what we expected it to do for AI citations, and what the evidence since says.
Case study: extending Vector from chat-only to email replies, broadcasts, and delivery tracking in a single Thread-Based Engineering session. Real code, real architecture.
The unified framework connecting Thread-Based Engineering to Agentic Experience Design. How thread autonomy levels map to trust patterns and supervision models.
How we use Radar, Vector, and Hive to monitor AI visibility across SEO, GEO, and LLM channels. Real GSC data and the 3-layer monitoring framework.
Radar by Pixelmojo runs 13 AI visibility tools in staged batches, generates cross-tool insights, and produces AI-ready implementation prompts you paste into Claude, ChatGPT, or Cursor.
Task completion alone is a vanity metric for AI agents. The five-pillar AX metrics framework that separates agents users tolerate from agents users trust.
Too much anthropomorphism can undermine trust. A practical framework for AI agent personality, tone calibration, and voice design that builds trust.
LLMs show a 39% average performance drop in multi-turn conversations. Learn 4 conversation flow layers: state machines, context persistence, handoff topologies, and state recovery for production agents.
Only 6% of companies fully trust AI agents for core processes. This guide covers the six trust patterns, progressive autonomy, and recovery cycles that make agentic experiences trustworthy.
AX (agentic experience) design explained: what it is, how it differs from UX, and how to design for an era where AI agents are the users.
A dated note on how pixelmojo.io's robots.txt treated AI crawlers from November 2025 to October 2026, and why we withdrew the claim that blocking them raised citations.
Four features we built so AI agents and search engines can read our site directly: connected JSON-LD, an MCP endpoint, a knowledge API and one-source FAQ.
Check how ChatGPT, Perplexity, and Claude see your website. 10 AI visibility tools (most free, AEO page auditor and citation tracker from $5): bot access checker, robots.txt analyzer, citation tracker, Reddit monitor, YouTube monitor, llms.txt validator, llms.txt generator, AI readiness scorer, AEO page auditor, and answer engine citation tester.
The Philippines AI development market is not about cheaper rates. It is about owned products, senior methodology-driven teams, and a timezone you can plan around. A practical evaluation framework for CTOs.
54% of engineering leaders plan to hire fewer juniors. The math looks right until you factor in who fixes AI-generated technical debt. A framework for CTOs rethinking their talent pipeline.
We built a cross-site knowledge graph connecting two domains via JSON-LD entity linking. Here is the architecture, the code patterns, and the real analytics from the first week.
An AI-native agency builds with AI at the core, not bolted on. The definition, real examples, and how it differs from a traditional shop.
UX design is splitting into a new discipline called AX (Agentic Experience). This guide covers the six patterns, four protocols, and product redesigns shaping interfaces in 2026.
What you can control in generative engine optimization as of October 2026: AI crawler access, quotable pages, a clear entity, and measurement that does not fool you.
Organic clicks are falling as AI answers replace search. Where your traffic actually went, what the shift means, and how to recover.
Static llms.txt files go stale the moment you publish new content. This guide covers what llms.txt actually does, why 844K sites got it wrong, and how to build a dynamic version in Next.js.
A dated note on the changes we made to our own site for AI search between October 2025 and February 2026, and the results claims we withdrew in October 2026.
Learn the difference between SEO, AEO, and GEO, and how brands can move from ranking in search to being cited in answers and recommended by AI engines.
How our contact form scores, answers and routes leads as of October 2026, where human review belongs, and when a team needs Vector instead.
A dated note on our February 2026 Claude Code hooks guide: why its config examples would not have worked, the correct shape from the official reference, and what we run instead.
CLAUDE.md is just layer one. The five-layer context hierarchy, memory patterns, and subagent strategies that separate productive AI coding from prompt guessing. With working examples.
AI tools hallucinate 19.7% of package names. Attackers register them as malware. Learn how slopsquatting works and layered defense strategies.
A dated note on our February 2026 Lakbay AI case study: what the travel concierge is, what its repository history shows, and the timeline, speed and security claims we withdrew.
Prevent AI-generated technical debt with Claude Code using CLAUDE.md optimization, security-first prompting, and production guardrails. Research-backed strategies that deliver +5-10% improvement.
How we govern AI-assisted development: two mandatory human checkpoints, the gates every change passes before it merges, and what the 2025 data says about AI code. As of October 2026.
84% of developers use or plan to use AI tools, yet only a third trust their accuracy, and 45% of AI-generated code samples failed security tests. The 2026 research data, the risks, and governance patterns that work.
The definitive guide to Thread-Based Engineering: 7 thread types, governance alignment, a production case study (Lakbay AI), and the research on AI code quality, including the Veracode finding that models failed to write XSS-safe code 86% of the time.
AI is no longer just answering questions; it is taking action. The shift from assistive chatbots to agentic AI co-workers is the defining technology transition of 2026. What this means for businesses and why self-verification changes everything.
We compared CrewAI, AutoGen, LangGraph, and 4 SaaS platforms on real 3-year costs. One option costs 47% less than enterprise SaaS. Full tables inside.
Why single AI agents fail at scale and how multi-agent orchestration solves it. Architecture patterns, shared intelligence, and why 95% of AI pilots show no measurable impact.
Stop spending months on discovery. These 7 AI prompts help product teams validate ideas in 48 hours, identify fatal flaws early, and build products people actually want to pay for. Copy-paste ready frameworks included.
What separates a production AI agent from a demo, shown through Vector, the sales agent on our own site: channels, qualification, guardrails, hand-off, and what retrieval can and cannot fix. As of October 2026.
Nielsen Norman Group reports that critical thinking, creativity, and taste are becoming the key differentiators as AI tools handle routine UX tasks. Learn how top UX professionals are evolving into Strategic Experience Architects who orchestrate AI while focusing on strategic thinking and business outcomes.
Intent detection and risk-tier routing sound smart but ignore human psychology. Gartner reports 64% of customers prefer companies not use AI for service. Industry observations suggest AI-first approaches significantly increase abandonment compared to human-first strategies. Route by emotional value, not transaction stakes.
How to budget for marketing design: what drives agency cost, how to compare quotes on the same terms, and how to audit what you spend now. Prices dated October 2026.
How AI-native and traditional design agencies differ in process, pricing and fit, how to test an agency's AI claims, and what to ask before you sign. As of October 2026.
A dated note on our 2025 AI copilot stack guide: the install commands and tool names that were wrong, the results and ROI figures we withdrew, and what we use today.
A dated note on our September 2025 AI Design Control Tower post: the idea of connecting product data to design decisions, and the results and offer we withdrew in 2026.
What computational thinking means for design teams, how it shows up in components, tokens and rules, and what to look for when you hire. With examples from our own system.
Where AI actually helps in growth marketing as of October 2026, from acquisition to retention, how to start with one workflow, and where people still need to decide.
An enterprise-ready guide to designing SaaS for Southeast Asian markets. Sourced data from Google-Temasek-Bain, GSMA, and Statista on localization, payment ecosystems, mobile-first design, accessibility compliance, and conversion optimization across ASEAN.
What shapes buying decisions, how technology is changing where people research, and which research methods answer which questions. With our own dated data, as of October 2026.
Discover the specialized growth marketing tools and technologies that separate high-performing agencies from the competition. This comprehensive guide reveals the modern tech stack that drives predictable ROI and client retention for forward-thinking agencies.
Transform your marketing from cost center to revenue engine. This definitive guide reveals why growth marketing outperforms traditional campaigns, with actionable frameworks, tools, and strategies for building systematic, data-driven growth that scales predictably.
Discover why award-winning beautiful designs often have terrible conversion rates. This comprehensive guide reveals how to escape the 'pretty but pointless' trap and build growth-driven design that transforms visitors into customers through strategic, data-driven approaches.
Philippines IT-BPM hit $40B in 2025. Filipino teams now build AI products, not just creative assets. The complete evaluation guide for global CTOs.
5 branding strategies optimized for ChatGPT, Perplexity, and AI Overviews. Build a brand AI search engines actually cite and recommend.
Master UX design for the Philippine market with sourced data from DataReportal, BSP, and GCash. 97.5M internet users, 88% mobile web traffic, 57.4% digital payments; practical frameworks for mobile-first design, local payment integration, and cultural considerations that drive conversion.
The Aesthetic-Usability Effect means users perceive beautiful designs as more usable. This guide covers the psychology, practical playbook, and real-world examples from Apple, Airbnb, and Google.
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