40+ useful SEO pages
71 crawlable tech review routes are generated from static content and data.
Self-review
Self-review for 40+ useful SEO pages.
This audit checks whether the build has enough useful pages, clear intent, fast navigation, internal links, and lightweight tool pages for AI and technical buyers.
Build audit
Current pass/fail review
20 tech tools
20 dedicated calculator, checker, planner, and checklist pages target practical buyer searches.
Onsite traffic paths
5 role-based entry pages route visitors from search intent to tools, comparisons, reviews, and newsletter signup.
Unique purpose
Reviews, rankings, X vs Y pages, best-for pages, category hubs, tools, and guides each answer a different buying question.
Fast navigation
The tech review site is data-driven, statically routable, and avoids blocking fetches on review, comparison, and tool pages.
Internal links
Tools point to related cost, spec, lab, red-flag, and comparison pages so readers keep moving toward a decision.
SEO fit
Page titles use buyer-language queries while body copy adds who it is for, when to use it, output, thresholds, and next steps.
Quality score
The review score is not a vibe check.
Decision clarity
A reader should know what to buy, skip, or compare within the first screen.
Evidence quality
Scores need workflow tests, benchmark notes, practical constraints, and failure modes.
Fit guidance
Every page should say who the choice is for, who should avoid it, and when the answer changes.
Operating cost
AI and hardware reviews need price, time, power, maintenance, and switching-cost judgment.
Navigation value
Pages should route readers to the next useful review, comparison, or buying guide.
Buyer tools
Quick checks before a shortlist
AI Modelsai model cost calculator
AI model cost calculator
Use this before choosing a default model. The useful answer is not the cheapest token price; it is the cheapest solved task with acceptable latency and failure rate.
AI Modelsllm context window plannerLLM context window planner
Long context helps only when the model still follows instructions near the end of the prompt. This planner forces a fit check before a bigger context tier becomes the easy answer.
AI Modelsprompt routing savings estimatorPrompt routing savings estimator
Routing is useful when easy prompts are common and failure is observable. It is wasteful when every task is rare, expert, or hard to classify.
AI Modelsinference latency budget plannerInference latency budget planner
A fast model can still feel slow if retrieval, tool calls, retries, and post-processing are not budgeted. This planner keeps the whole user path visible.
AI Modelsmodel eval sample size plannerModel eval sample size planner
Small evals can still be useful if they are realistic and repeated. This tool makes the sample deliberate: enough cases to catch regression, not so many that no one maintains it.
AI Toolsrag chunk size plannerRAG chunk size planner
Chunking is not a magic number. The right size depends on the shape of the source and whether the model needs local detail, full sections, or cross-document synthesis.
AI Toolsembedding storage cost estimatorEmbedding storage cost estimator
Embedding cost is rarely just the first import. Refresh cycles, duplicate content, metadata, backups, and permission filters decide whether the system stays manageable.
AI Toolsapi rate limit plannerAPI rate limit planner
Rate limits are product constraints. This planner helps choose batching, backoff, queueing, and multi-model fallback before launch traffic teaches the lesson.