AI Development

AI-powered web applications

An AI product is still a web application. It needs accounts, billing, permissions and support tooling, plus a few challenges of its own that ordinary apps don’t have.

Discuss Your Project

Calling a model API takes a few lines of code. What takes the time is the product around it. Responses arrive slowly, so interfaces need to stream text as it’s generated. Requests can run for a long time, so they don’t always fit the usual web request cycle. Users need to be able to cancel, retry, edit and give feedback. And since you pay for every generated word, abuse and heavy use cost you real money.

Unit economics deserve attention early. If a power user can cost you more in model fees than they pay in subscription, your best customers become your biggest loss. Per-plan usage limits, credit systems, smaller models for simpler tasks and caching repeated work all help. We model those numbers before you set your prices.

Designing for a system that’s sometimes wrong

The interface needs to help people use AI output sensibly. That means showing sources, making it obvious when content has been generated, making correction easy and collecting feedback that you can use to improve. Rather than one big “ask anything” box, products usually work better when AI is applied to specific tasks with a clear result. For existing products, see AI integration.

Problems specific to AI products

Slow responses

Waiting for ten seconds in front of a blank screen feels broken unless responses stream as they generate.

Margins eaten by usage

Model costs rise with usage, and flat-rate pricing can be punishing without limits.

Long-running jobs

Agent tasks can last minutes, which calls for background processing and progress updates.

What we build

Streaming interfaces

Responsive chat and document interfaces with retry, stop and edit controls.

Usage and billing

Metering, plan limits and credits, connected to Stripe.

Background agents

Queued, resumable jobs with progress and results shown to the user.

Feedback loops

Ratings and corrections, stored and used to improve your evaluations.

Safety controls

Abuse prevention, content filtering and rate limiting.

Where it fits

  • Vertical AI tools for a specific profession
  • Internal research and drafting platforms
  • Adding AI as the main feature of a new SaaS

Technology

  • Next.js
  • React
  • Laravel
  • Python
  • Claude
  • OpenAI
  • Vercel AI SDK
  • Server-Sent Events
  • Stripe
  • pgvector

AI Web Apps questions

How do we stop AI costs getting out of control?
Set usage limits per plan and per user, use cheaper models where they’re good enough, cache repeated work and monitor spending from day one.
Will another AI product just replace us?
It’s a real risk for thin wrappers around a model. Lasting products tend to have proprietary data, deep workflow integration, or specialist domain knowledge that a general assistant lacks.
Should we start with one model provider?
You can start with one, but keep the design provider-independent, so you can switch or mix models as prices and capabilities change.

Related services

Interested in AI-Powered Web Applications?

Outline what you need, and we will reply by email with questions or next steps.

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