Services

Technical SEO, programmatic SEO, TypeScript implementation, AI-assisted automation, and performance work for Next.js websites.

About

India-based Next.js Technical SEO Engineer specialising in programmatic SEO, AI-assisted automation, and web performance for remote teams worldwide.

Tools

Technical SEO, programmatic SEO, TypeScript implementation, AI-assisted automation, and performance work for Next.js websites.

Writing

Technical SEO, programmatic SEO, TypeScript implementation, AI-assisted automation, and performance work for Next.js websites.

AP / ServicesRemote / India

SEO automation

AI-assisted SEO workflows with evidence and human review

I build bounded workflows that use AI to reduce repetitive SEO handling while keeping source evidence, validation rules, and a human decision point visible.

Discuss your project
01

A good fit when

AP®

Design and implementation for repeatable SEO workflows that combine deterministic checks with carefully scoped AI assistance: metadata drafting, content QA, evidence-linked issue classification, and internal-link suggestions for review.

  • Teams draft or review metadata across many pages and need consistent validation
  • Content QA involves repeatable checks for missing facts, structure, or page requirements
  • Technical issues need triage with source URLs, snippets, and rule-based evidence attached
  • Internal-link ideas should be generated from an approved page set and reviewed before publishing
  • A team wants reusable agent instructions or QA checks that fit its existing workflow
02

What you get

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01

A scoped assisted workflow

A defined task such as metadata draft generation, content QA, issue classification, or internal-link suggestions, with permitted inputs, expected outputs, and explicit exclusions.

02

Evidence and quality controls

Outputs retain source URLs and supporting evidence where available, pass deterministic format and policy checks, and mark uncertainty instead of filling gaps with invented facts.

03

Reusable review and handover

Human approval points, escalation rules, repeatable QA, and documented agent instructions or skills so a person remains responsible for publication and prioritization.

03

How the work moves

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  1. 01

    Choose a bounded task

    Map the human workflow, data sources, privacy constraints, common errors, and decision that should remain with a reviewer.

  2. 02

    Prototype with controls

    Combine deterministic rules with an AI step only where it helps. Attach source evidence, validate output shape, test edge cases, and define a safe fallback.

  3. 03

    Review and operationalize

    Compare results with human-reviewed examples, document approval and correction paths, then package repeatable checks or agent instructions for the team's workflow.

04

Relevant capabilities

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AI-assisted metadata drafting and validationContent QA with source checksEvidence-linked technical issue classificationHuman-reviewed internal-link suggestionsReusable agent instructions and skillsDeterministic SEO checksSearch/CMS data workflowsPython and Node.js automationLogging, exceptions, and handover
05

Common questions

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What does AI do in an SEO workflow?

It can draft metadata from approved page facts, classify issues against a supplied taxonomy, check content against defined requirements, or suggest links from an approved URL set. Deterministic checks and human review remain part of the workflow.

Will AI publish changes or guarantee rankings?

No. The default deliverable is a reviewed suggestion or QA result, not autonomous publication. Automation cannot guarantee crawling, rankings, traffic, or business outcomes.

Can you create reusable agent skills?

I can document and package repeatable instructions and checks for an agreed agent workflow, with scope, allowed sources, validation rules, and a human approval step.

Have a defined brief, or just a stubborn problem?

Let's work out the right next move.

Start a conversation rawat@apnatva.dev