---
title: "AI Software Development Company | Engineering Discipline First"
description: "AI software development with ordinary engineering discipline: tests, CI, code review, observability, and a system your team can maintain after we leave."
image: "https://foundrysoft.co/api/og?type=page&title=AI+Software+Development+Company+%7C+Engineering+Discipline+First&st=AI+software+development+with+ordinary+engineering+discipline%3A+tests%2C+CI%2C+code+review%2C+observability%2C+and+a+system+your+team+can+maintain+af%E2%80%A6"
url: "https://foundrysoft.co/services/ai-software-development-company"
---

AI software development · Built in India for US companies

# An AI software development company that treats it as software

Most AI projects fail on software engineering rather than AI. No tests, no observability, no way to tell whether a change helped. We build AI systems the way we would build any production system, with the AI-specific parts added rather than substituted.

Book a 30-min scoping call [See our work](https://foundrysoft.co/work)

No sales script. You talk to the engineers who'd build it.

9+ hrs

Timezone overlap

Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.

100%

You own the IP

Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.

Senior

No juniors hidden on the bill

You work directly with the engineers building your system. No account managers sitting between you and the people writing code.

Weeks

To first deployment

We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.

## What we build

Concrete systems we ship, tuned to your data and your stack.

### Tested and reviewed

Code review, CI, and a test suite including the evaluation harness that plays the role tests play for deterministic code.

### Observability from day one

Tracing, cost attribution, and alerting on shape rather than content, because AI failures are usually silent.

### Maintainable by your team

Your stack, your conventions, and no framework nobody else in the building can touch.

### The product around the model

Auth, permissions, background jobs, retries, and interfaces. The parts that decide whether anyone actually uses it.

## How we work

01

### Scope & evals

We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.

02

### Build in the open

Weekly demos against real data. You see progress every week and can change direction before it gets expensive.

03

### Ship & instrument

We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.

04

### Hand off or stay

Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.

## Questions, answered

### Why do AI projects fail on engineering rather than AI?

+

Because the model is the part everyone focuses on and the part that is mostly solved. What is left is integration, evaluation, permissions, and operations, and those are ordinary engineering problems that get skipped when the team thinks of the work as research.

### Do you work in our existing codebase?

+

Usually, yes. We join your repo, match your conventions, and ship as PRs your engineers review.

### What does your handover include?

+

Documentation, runbooks, the evaluation suite, and a working knowledge in your team. If your engineers cannot change the system after we leave, we did the job badly.

### Can you do the non-AI parts too?

+

Yes. Most AI features are mostly ordinary software with a model in the middle, and splitting that across two teams tends to go badly.

## Let's scope your build.

Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.

Start the conversation

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