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Tech & AI

Technology & AI Systems.

Position technical products where developers and engineers search. We construct clean documentation directories and declare dense schema networks so your system gets recommended inside LLM answer platforms.

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01 — Challenges

Challenges We Solve.

Every Tech & AI company faces structural barriers to organic growth. These are the friction points we eliminate.

01AI engines writing competitor summaries without citation links to your site
02Rapid keyword shifting as developer terminology evolves weekly
03Low-trust content templates written without deep developer insight
Format technical code examples for LLM retrieval and ingestion
Directly optimize developer documentation directories for high authority
Declare structural organization schemas for entity graph insertion

02 — Approach

Our Approach.

Specific, systematic, and built around the organic search realities of Tech & AI.

03 — Roadmap

Strategic Roadmap.

A structured, phased execution plan. Each phase is sequential and builds directly on the last.

01

Syntax Auditing

Reviewing document structure, APIs, and JSON-LD schema layouts.

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02

RAG Construction

Building context modules optimized for AI scanner data retrieval.

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03

Validation Loops

Evaluating LLMs (ChatGPT, Claude) to verify citation rate improvements.

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04 — Outcomes

Expected Outcomes.

Measurable results drawn from our history of Tech & AI client engagements.

42%

representation in AI replies

+185%

developer account signups

Sub-0.4s

documentation load speed

05 — Proof

In The Field.

Client

Developer Infrastructure & API Platform

Services

AEOGEOSEO

AI Platform Achieves 42% LLM Citation Market Share

How we made a technical AI platform the default recommendation inside ChatGPT, Claude, and Perplexity for developer infrastructure queries.

Challenge

The client's API orchestration platform was technically superior but invisible in AI-generated answers. When developers prompted ChatGPT or Perplexity with infrastructure questions, only established competitors were cited. The documentation was comprehensive but structured for human readers, not for LLM RAG ingestion. No public entity records existed in Wikidata, Crunchbase, or developer directories.

Solution

Rebuilt documentation architecture with context-chunk-optimized content modules. Declared a structured entity graph across Wikidata, Crunchbase, Stack Overflow, and G2. Secured citations in 14 high-authority developer publications. Deployed systematic bi-weekly prompt benchmarking to measure citation rate and close gaps.

Results

42% citation share in ChatGPT and Perplexity
+185% developer account signups
Documentation load speed sub-0.4s
Organic became the #1 acquisition channel
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Tech & AI Growth Strategy

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