AI product development

Model-Backed Products, Built for You to Sell

Not a demo that impresses in a meeting and breaks on a real customer. We build the parts around the model (ingestion, a reviewer workspace, billing, the boring infrastructure) with the same rigor as the model call itself.

See what we've shipped Discuss your product

What "AI product" actually means here

The Model Is 10% of the Actual Work

A model call is easy to prototype and hard to sell. What turns it into a product a customer will pay for is everything around it, including authentication, multi-tenancy, a way to review and correct outputs, and billing that doesn't break at renewal.

We treat the reasoning pipeline as one component among several, not the whole product. A reviewer workspace so a human can check and correct outputs before they reach a customer. Multi-tenant architecture so one account's data never leaks into another's. Billing that handles usage-based pricing correctly the first time, since that's the part most prototypes skip. As an AI product development company, TectSoft builds custom AI solutions and generative AI features as shippable, sellable products, not demos.

What's included

  • Model integration and reasoning pipeline
  • Reviewer or operator workspace for human oversight
  • Billing and subscription management
  • Multi-tenant architecture from the start
  • Production hardening and monitoring, not just a happy-path demo

Built with

ClaudeChatGPTReactNode.jsPostgreSQLSupabaseVercel

How it runs

Four Stages, From Prototype to Something You Can Bill For

  1. 01

    Scope the Product

    What the model needs to do, who reviews its output, and what a paying customer expects from day one. Written down before any code.

  2. 02

    Architecture the Pipeline

    Multi-tenancy, auth and the reasoning pipeline designed together, so adding a second customer later doesn't mean rebuilding the first.

  3. 03

    Build the Reviewer Workspace

    A place for a human to check the model's output before it reaches a customer, built alongside the model integration rather than bolted on after.

  4. 04

    Harden and Launch

    Billing, monitoring and load testing before real customers touch it, so the first paying account isn't also the first stress test.

Common questions

About AI Product Development

Calling an API is the easy part and takes an afternoon. Turning that into something you can charge customers for means multi-tenancy, a way to review and correct what the model produces, usage-based billing, and monitoring for when the model gets something wrong at scale. That's most of the actual build.

That's what the reviewer workspace is for, a place for a human to check outputs before they reach a customer, especially early on while you're still learning where the model tends to struggle.

We work with Claude and ChatGPT depending on what the product needs, and we're not tied to one provider. The right model for a given task matters more than a default choice.

Tell us what you're trying to build. One call gets you a scope and a fixed number.

Discuss Your Product