LLM Optimization for Crypto & Web3 Websites

Turn your docs, knowledge base, and community content into reliable answers: production-ready RAG, clean embeddings, smart chunking & metadata, and evaluation that mirrors real user tasks.

Why LLM optimization matters for crypto

Crypto content is technical, versioned, and spread across docs, GitHub, governance forums, blogs, and support portals. Out-of-the-box RAG fails when sources are noisy, chunks are arbitrary, or metadata is missing. Our program aligns information architecture, search-in-docs SEO, and LLM retrieval rules so your assistants answer correctly, consistently, and with citations. The result: faster onboarding, fewer tickets, and higher developer conversion.

↓ 45%
Repeat support tickets
+38%
Task success in evals
2.3×
Docs discoverability
-30%
Hallucination rate

Program pillars

  • Source governance: authoritative repos only, versioning rules, de-duplication, canonical paths.
  • Embeddings & chunking: semantic chunk sizes, overlap, titles, and structured metadata for precise recall.
  • Prompt & retrieval policy: citation-first answers, JSON output for UI, guardrails for unsupported claims.
  • Evaluation & monitoring: golden tasks, regression suites, drift alerts, and feedback-to-docs loops.

RAG architecture we deploy

  1. Ingestion: docs/KB, SDKs, GitHub, forum threads—normalized to canonical routes.
  2. Processing: markdown/HTML cleanup, semantic chunking, embeddings, metadata enrichment.
  3. Retrieval: hybrid (dense + sparse) search, recency weighting, version filters.
  4. Answering: instruction prompts with citations, tool-use for calculators or API calls.
  5. Feedback: user thumbs, unresolved intents → content backlog and schema updates.

Evaluation that matches real user tasks

Golden questions

Curated tasks from support, community, and analytics; pass/fail with strict citations.

Regression tests

Nightly checks for drift when docs change, models update, or sources are added.

Human-in-the-loop

Reviewer workflows for sensitive claims (YMYL) and ambiguous intents.

SEO + LLM synergy

Good RAG starts with clean information architecture. We align Web3 SEO and search-in-docs patterns (IA, snippets, internal anchors, schema) so the same improvements that help Google also help your assistant retrieve and cite the right chunk. One roadmap, two compounding channels.

Adjacent services

Web3 SEO

Docs & developer SEO that boosts retrieval and ranking.

Explore Web3 SEO →

Crypto Content & Copywriting

Clear, compliant docs & guides built for snippetability.

View Content →

Web3 Content Marketing

Topic clusters that feed both SEO and LLM knowledge.

See Web3 Content →

FAQ

We’re vendor-neutral. We select DBs and models based on latency, scale, and your privacy/compliance needs. Pipelines are designed to be swappable.

We restrict sources, require citations, and route sensitive queries to human review or approved pages. No price predictions or promotional claims.

Yes. We can call calculators, SDK examples, or support APIs via tool-use so answers include the right actions or code.

Task-level accuracy, citation rate, drift alerts, feedback loops to docs, and impact on support load & developer activation.
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