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Next.js & AI Development

Applications where AI does a job, not a demo.

React and Next.js applications with Claude or OpenAI wired into the product logic from the start — content pipelines, search, support, automation. If the AI could be removed without anyone noticing, it should not have been there.

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01 / 04 · What's included

Built properly, handed over cleanly.

  • Next.js App Router applications, server-rendered and fast
  • Claude and OpenAI integrations inside real product workflows
  • SaaS dashboards and internal tools
  • Headless CMS builds on top of WordPress or a database
  • MCP servers so your own tools can be driven by AI clients
  • Automated content and publishing pipelines

02 / 04 · How it runs

Four steps. No surprises.

  1. 01
    Scope
    We separate what genuinely needs AI from what's a database query wearing a chatbot costume.
  2. 02
    Architecture
    Data model, API routes, and integration points agreed before a line of UI code is written.
  3. 03
    Build in slices
    Working, deployable increments — you see progress weekly, not a single reveal at the end.
  4. 04
    Ship & monitor
    Deployed with error tracking and usage logging, so problems surface before a user reports them.

03 / 04 · Spec

1–2 weeks, typically.

Best for
Products that need speed, custom logic, or AI doing real work.
Stack
Next.jsReactTypeScriptClaude APIOpenAIMySQL / PostgresTailwind

04 / 04 · Start

Have a Next.js & AI project?

Send a short brief and you'll get scope, timeline, and a straight answer — before any invoice.

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See it running on Kohenor News ↗

Sub-services

8 areas

App Router Application Development

Server-rendered Next.js applications built for speed from the first commit.

  • Dashboards, marketplaces, and internal tools on the App Router
  • Server components and streaming where they genuinely improve load time
  • Deployed to Vercel, your own server, or Hostinger Node hosting

AI Integration & Prompt Engineering

Claude or OpenAI wired into product logic that does a job, not a demo.

  • Drafting, classification, extraction, and agent workflows built into real features
  • Prompt design, evaluation, and guardrails so output stays reliable in production
  • Provider-agnostic architecture — swap models later without a rebuild

MCP Server Development

Expose your own application as tools an AI client can call directly.

  • Tools scoped to the actions you actually want an AI client to take
  • Authentication so a public endpoint doesn't mean public write access
  • The pattern behind this studio's own Kohenor News platform

Headless CMS Integration

WordPress or a database as the content source, Next.js as the fast, fully custom front end.

  • Content modelled once, rendered anywhere — web, app, or partner feed
  • ISR/ on-demand revalidation so editors publish without a redeploy
  • A clean split between who edits content and who owns the front end

SaaS Dashboards & Internal Tools

Admin panels and analytics views with real auth, not a spreadsheet with a login screen.

  • Role-based access, audit trails, and proper session handling
  • Data visualisation built for the metrics your team actually checks
  • Built to be handed to your own developers afterwards, not locked to us

API & Database Architecture

The schema and API layer decided before a line of UI code is written.

  • REST, GraphQL, or tRPC — chosen for the client consuming it, not by default
  • Postgres or MySQL schema design with migrations you can read
  • Caching layers so the same query isn't paid for twice

Automated Content Pipelines

RSS-to-article, document-to-summary, or data-to-report pipelines with AI doing the drafting.

  • Ingestion, drafting, and publishing stages with a human review queue
  • Structured data and social distribution built into the pipeline, not an afterthought
  • Running daily on this studio's own news platform before we recommend it

Performance & Core Web Vitals Optimization

Making an existing Next.js app fast, not just functional.

  • SSR/ISR strategy audited against what the page actually needs
  • Bundle, image, and font audits with measurable before/after numbers
  • Edge caching and route-level optimisation where it moves the metric

How it runs

  1. 01
    Scope
    We separate what genuinely needs AI from what's a database query wearing a chatbot costume.
  2. 02
    Architecture
    Data model, API routes, and integration points agreed before a line of UI code is written.
  3. 03
    Build in slices
    Working, deployable increments — you see progress weekly, not a single reveal at the end.
  4. 04
    Ship & monitor
    Deployed with error tracking and usage logging, so problems surface before a user reports them.

Questions

Both — the choice depends on the task, not brand preference. We'll recommend whichever fits your budget and latency requirements, and can swap providers later without a rebuild if the architecture is done right.