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AI Integration: Custom AI Development

For the use cases no off-the-shelf tool covers, because they depend on how your business works.

AI Built Into Your Software

Off-the-shelf AI tools handle common jobs well, but your most valuable use case may be specific to your business. A general tool won’t search your own knowledge or sit inside your customer portal.

We build AI directly into your applications and systems. That can mean search and Q&A over your own knowledge, or new features in your product or portal. We connect to model APIs from providers like Anthropic, OpenAI and Google, then add the testing, monitoring and cost controls any production software needs.

Diagram of an AI feature in three layers: your application, the AI model, and the tests and cost caps that control it.

What We Can Build

AI features inside your existing applications, portals and internal tools

Search and Q&A that retrieve answers from your own documents and data

Direct API connections to Anthropic, OpenAI, Google and other providers

Testing, monitoring and cost controls built in from the start

How a Custom Build Runs

1

Scope the Feature

We study your application’s architecture and agree what the feature must do and what good output looks like.

2

Choose the Model

We pick a provider for each use case by quality, speed and cost, and design so you can switch later.

3

Build and Evaluate

We build it into your codebase, test it against real examples and project API costs at your actual volumes.

4

Monitor and Document

After launch we watch quality and spend per feature, and we document everything for whoever maintains it.

Why Choose System Strats

We come from the systems side, not the hype side. We already build the integrations, data pipelines and automations that businesses run on, so an AI feature gets the same engineering discipline as the rest of your software.

Frequently Asked Questions

We work with the major providers: Anthropic’s Claude, OpenAI’s GPT models and Google’s Gemini. We choose for each use case based on quality, response time and cost, with no loyalty to any one vendor. We also design integrations so you can switch models later without rebuilding.

No. Modern AI development builds on hosted model APIs, so you have no models to train and no GPU clusters to run. It does need solid software engineering, meaning clean integration, testing and monitoring. We deliver that and document it for whoever maintains the system.

We treat cost as a design constraint from day one. That means matching the model to the task, caching repeated work, limiting each request to the context it needs and monitoring spend per feature. We also project costs against your real volumes before launch, so there are no surprises.

Yes. We regularly work inside existing codebases and systems we did not originally build. We start by understanding the architecture, then add AI in a way that fits the patterns already there.

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