AI Software Development
Complete software products with AI at the core — frontend, backend, auth, billing and the model layer — built, tested and shipped by one team.
What is AI Software Development?
AI software development is building a complete software product in which AI is a core feature, covering the application, data and operations around the model rather than the model call alone. ADM Technology engineers the full stack — interface, backend, authentication, multi-tenancy and billing — alongside the AI layer with function calling, streamed responses, prompt versioning, automated evals in CI, tracing and provider fallbacks, typically launching an MVP in twelve weeks at sub-2s time to first token and 99.9% uptime.
Overview
Full AI products, engineered end to end
A model call is maybe a fifth of an AI product. The rest is the application around it: authentication, multi-tenancy, billing, streaming interfaces, tracing, fallbacks when a provider degrades, and prompts versioned and tested like any other code. We build the whole thing, so you get a product rather than a promising demo.
12 wks
MVP to launch
99.9%
Uptime with model fallbacks
<2s
Time to first token
Exactly what we do, step by step
Every AI Software Development engagement follows a clear, phased path — so you know precisely what work happens, in what order, and what comes out of each stage.
Product Definition & AI Scoping
We define the product, then separate what genuinely needs a model from what is better solved with ordinary code, because AI applied to a deterministic problem only adds cost and unpredictability.
Architecture & Latency Budget
We design the data model, API surface and tenancy structure, then set a latency budget per interaction that dictates model choice, streaming strategy and where results can be precomputed or cached.
Application Engineering
We build the product itself — interface, backend services, authentication, roles, billing and admin tooling — on Next.js and a typed API layer, because the AI feature is worthless without a product around it.
AI Layer Integration
We wire in the model with function calling and schema-validated structured output, streamed token-by-token to the interface with optimistic states, so the feature feels immediate rather than merely fast on paper.
LLMOps, Evals & Observability
We version prompts in the repository, run eval suites in CI so a prompt change cannot silently regress quality, and trace every request with token, latency and error telemetry per feature.
Launch, Scale & Iterate
We deploy through CI/CD with provider fallbacks and rate-limit handling, then use production traces and user feedback to tune prompts, routing and cost as real usage patterns emerge.
What's included in every engagement
The building blocks we bring to your product — proven, production-grade and tailored to your goals.
Exactly what you walk away with
Tangible, hand-off ready assets — no black boxes, full ownership.
Related services
AI Software Development — common questions
Straight answers to what teams ask us most before starting. Need specifics? Talk to a senior engineer.
The application layer is the same disciplined engineering. What changes is that the AI component is probabilistic, so you cannot test it with assertions alone. It needs eval suites instead of unit tests for quality, tracing to understand why a given output happened, streaming interfaces to hide latency, and fallback paths for when a provider is slow or down. We build both layers with equal rigour.
Let's build something exceptional together.
Tell us where you're headed. We'll show you how to get there faster — with production-grade software and a senior team.