AI Development

AI Application Development and AI-Powered SaaS

New products and internal tools where AI is central to the value — designed and built by software engineers who treat the model as one component in a reliable, secure system.

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  • LLM apps
  • AI SaaS
  • AI agents
  • RAG
  • OpenAI
  • Claude
  • Azure OpenAI
  • Next.js
  • ASP.NET Core
Illustration of a brain drawn as a circuit board

What we build

  • AI-powered SaaS

    Multi-tenant SaaS products with AI at the core: authentication, billing, usage limits and per-tenant data isolation included.

  • AI assistants

    Domain assistants that answer from your knowledge base, draft content and guide users through complex tasks.

  • AI agents

    Agents that plan multi-step tasks and act through your APIs, with approval steps and full audit trails.

  • AI automation tools

    Internal tools that automate document-heavy or repetitive processes, with human review built in.

  • AI search products

    Semantic and hybrid search over large content collections with cited answers.

  • AI analytics

    Natural-language questions over business data, with generated queries checked and constrained by your backend.

Engineering principles for AI products

  • Start narrow. Prove one AI capability works for real users before building around it.
  • Model-agnostic design. Provider calls sit behind an interface so you can switch or compare models.
  • Evaluate continuously. Keep a test set of real inputs and check quality before every change.
  • Keep humans in the loop wherever an action is costly or hard to reverse.
  • Measure cost per task from day one so pricing and margins stay healthy.
  • Secure by default. Tenant isolation, permission-aware retrieval and server-side keys.

Models & APIs

  • OpenAI
  • Anthropic Claude
  • Azure OpenAI
  • Embedding APIs

Application

  • Next.js
  • React
  • TypeScript
  • Flutter

Backend

  • ASP.NET Core
  • Node.js
  • PostgreSQL
  • Vector search

Platform

  • Azure
  • AWS
  • Docker
  • Stripe
  • n8n

From idea to production

  1. Discovery

    Define the user, the task, and what "good" looks like with real examples.

  2. Proof of capability

    A focused prototype that tests model quality on your data.

  3. MVP build

    Production foundations: auth, tenants, billing, monitoring.

  4. Iterate

    Improve with usage data, evaluation results and cost metrics.

From the blog

Guides on AI Application Development

Frequently asked questions

What is the difference between AI integration and AI development?

AI integration adds AI features to software you already have. AI application development means building a new product or tool where AI is the core — for example an AI-powered SaaS, an internal assistant, or an agent-driven workflow.

Do you train custom AI models?

No. We build applications on established model APIs such as OpenAI, Anthropic Claude and Azure OpenAI, combined with your data through retrieval and tool use. We do not claim proprietary models.

Can you build an MVP for an AI SaaS idea?

Yes. We usually recommend a narrowly scoped first version that proves the AI capability with real users, built on a SaaS foundation — authentication, tenants, billing — that can grow with the product.

What stack do you use for AI applications?

Typically a Next.js or React front end, an ASP.NET Core or Node.js backend, PostgreSQL with vector search where needed, and LLM APIs — deployed on Azure or AWS.

Validate your AI product idea

We can help you scope a first version that proves the core AI capability before you invest in the full product.

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