Skip to content
AI Services

AI Software Development Whole Applications, Not Chat Boxes

AI software development is the discipline of building a complete application around a model, rather than bolting a chat box onto an existing screen. We build the interface, data layer, permissions, background jobs, and observability that let an entire team depend on an AI feature.

  • Production-grade from the first sprint
  • Access control enforced at retrieval
  • Full audit trail on every generation
Discuss an AI BuildAll Services

How it runs · 5 steps

  1. Model the domain
  2. Design the AI interface
  3. Build the retrieval layer
  4. Wire in observability
  5. Evaluate and ship

Built with

  • Next.js
  • React
  • TypeScript
  • Astro
  • FastAPI
  • Python
  • Node.js
  • BullMQ

More in AI Services

Questions first?Talk to an engineer

An AI feature is still software

It needs authentication, roles, error states, audit history, and a way for a user to disagree with the output. Teams that skip that layer end up with something impressive in a sales meeting and unused by the operations staff it was built for — because there is no way to correct it, no record of what it did, and no one accountable when it is wrong.

We build the application first and treat the model as one component inside it. That ordering is what makes the difference between a feature people rely on and a tab nobody opens twice.

The Problem

What this usually fixes

  • No way to correct a wrong answer

    Users hit one bad output, lose confidence, and route around the feature permanently. Without an override path there is no way back.

  • The model can see everything

    Retrieval that ignores permissions will happily quote a document the reader was never allowed to open. Access control belongs at retrieval time, not in the prompt.

  • No history of what it did

    When an output is questioned weeks later, there is no record of the inputs, the sources, or the model version that produced it.

  • Quality regressions nobody can locate

    Something got worse after a change and there is no trace linking the two, so the debate is about opinions rather than a diff.

How We Work

The process

Each step produces something you can review — a document, an environment, or working software — rather than a percentage in a status report.

  1. Model the domain

    Users, permissions, and the records the AI will read and write — settled before any interface exists, because these are the expensive decisions to reverse.

  2. Design the AI interface

    How uncertainty is displayed, how sources are cited, how a user overrides a result without leaving their workflow.

  3. Build the retrieval layer

    Ingestion, chunking, embedding, and refresh strategy, with access control enforced at query time rather than after the fact.

  4. Wire in observability

    Every generation logged with inputs, sources, cost, latency, and model version, so any output can be reconstructed later.

  5. Evaluate and ship

    Accuracy measured against the agreed set, load and failure paths tested, then deployed with monitoring already running.

What You Get

Why teams choose this

  • Adoption, not novelty

    Built for the operations staff who use it daily rather than the meeting where it is demonstrated once.

  • Permissions that hold

    Retrieval respects the same access rules as the rest of your application, enforced in the query rather than requested in the prompt.

  • Answers you can audit

    Every generation is reconstructible — inputs, sources, cost, and model version — which is what makes AI usable in a regulated process.

  • One team, one codebase

    The application and the model layer are built together, so there is no seam where two vendors each believe the gap belongs to the other.

Stack

Technologies we build with

Tools we have delivered production work on, not a capability matrix. We pick per project and will explain the trade-off behind each choice.

Application
  • Next.js
  • React
  • TypeScript
  • Astro
Backend
  • FastAPI
  • Python
  • Node.js
  • BullMQ
AI layer
  • LangGraph
  • Claude
  • GPT
  • pgvector
Data
  • PostgreSQL
  • Redis
  • S3
Industries

Who this is for

Wherever an AI output has to be trusted by a team rather than admired by an audience.

  • Healthcare
  • Financial services
  • Insurance
  • Legal & professional services
  • Logistics
  • SaaS products
  • Manufacturing
  • Real estate
Questions

AI Software Development: common questions

Related reading

What is the difference between AI development and AI software development?

AI development describes the whole lifecycle of getting a model to solve a business problem, including feasibility, model selection, and evaluation. AI software development is specifically the engineering of the application around that model — screens, permissions, data pipelines, background jobs, and audit history. In practice they are two halves of one project, and the second is where most of the effort goes. A model integrated without an application layer is a prototype, not a product.

Can you add AI to our existing product?

Yes, and it is the more common request. We work inside your codebase, match your existing patterns, and add the model layer behind your current authentication and permission model rather than beside it. The main constraints are usually your data model and how cleanly the relevant records can be retrieved with access control applied. We assess both during scoping and will tell you if a preparatory refactor is the cheaper first step.

How do you stop the AI from showing users data they should not see?

Access control is enforced at retrieval time, in the query, before any content reaches the model. The retrieval layer filters candidate documents by the requesting user's permissions, so a document the user cannot open is never a candidate for the answer. Instructing a model not to reveal something is not a security control — it is a request, and it fails under adversarial phrasing. The same rule applies to tool access in agentic features.

What happens when the model provider changes or deprecates a version?

Your evaluation set is re-run against the new version before anything migrates, and the results are compared against the thresholds agreed at build time. If quality holds, we migrate; if it drops, we either adjust prompts and retrieval or stay on the current version until we can. Because prompts are versioned and every generation is logged with its model version, a regression after a migration is traceable to a specific change rather than a mystery.
Related

Services that pair with this

Have a model that needs a product around it?

Bring us the prototype, the codebase, or just the problem. We will scope the application layer that makes it usable.

Discuss an AI BuildSee Our Work