AI Application Development and Integration

Most businesses do not need an AI startup. They need AI working inside the software they already run: the report that writes its own first draft, the workflow that summarizes itself, the platform that turns an hour of copy-and-paste into one click.

That is the AI work Sandcastle does. We are a custom software team, in business for more than two decades, and we build AI features the same way we build everything else: scoped, tested, and delivered into your real application. We work primarily with OpenAI’s APIs, including the ChatGPT models, and we put the engineering around them that makes the output dependable enough to trust in production.

It is the same discipline behind our custom application development work, applied to AI.

What We Build

• AI integration services for your existing application. We integrate the OpenAI API into the platform you already have, whether that is a custom web application, a WordPress site, or an internal tool.
• AI-powered WordPress plugins. Custom WordPress plugin development with AI at the core, including subscription billing and role-based access when you want to sell the result.
• ChatGPT and OpenAI API integration. Prompt design, model selection, structured output, and the server-side code that calls the API, validates what comes back, and handles failures.
• LLM integration with structured, constrained output. JSON-schema responses, enforced character limits, hardcoded required language, and code-side validation, so the model cannot wander off format.
• Web-search-grounded AI. For features that need current real-world data, we use search-enabled API paths and gate the output on real search evidence, so the model cannot quietly make things up.
• AI workflow automation. Taking a process your team runs by hand through a chat window and turning it into a reviewed, repeatable feature with an audit trail.

Case Study: MediaTrAiner, an AI Media Interview Platform

Fight or Flight, an international PR agency, needed a way for executives to practice media interviews and get expert-level feedback without a trainer in the room. We built MediaTrAiner, a subscription SaaS platform delivered as a custom WordPress plugin, powered by the ChatGPT API.

MediaTrAiner analyzes interview transcripts and generates realistic news articles in the style of specific reporters and publications, then evaluates the spokesperson’s performance against Fight or Flight’s media training framework. Analysis that once took hours now takes minutes, and the platform was shortlisted for Best AI Platform at the PRWeek US Awards 2026.

Read the full MediaTrAiner case study.

Case Study: AI Report Writing for Commercial Property Inspections

One of our clients produces narrative sections of commercial property inspection reports. Their team was doing it by hand in the consumer ChatGPT app: paste in report data, paste in a prompt, copy the result back out, then fix everything the model added that nobody asked for.

We built that workflow directly into their inspection reporting platform. One feature auto-populates market data using web-search-grounded AI, with comparable properties, market rent and vacancy pulled from live sources and checked against real search evidence before anything is written. The other generates the report’s narrative write-up from the report’s own data, with fixed required sentences hardcoded, output locked to a JSON schema, and character limits enforced in code because a model cannot be trusted to count. Both features are in production now.

That project is a good picture of the difference between using ChatGPT and shipping AI: the manual version worked until it did not, and the production version is constrained so it cannot go rogue.

“The recent integration has noticeably improved productivity, eliminating those redundant manual tasks of summarizing data that was already keyed somewhere else in the report.”

The client

Read the full case study for how the build holds the model to a fixed format and stops it claiming a check it never ran.

Why Production AI Beats a ChatGPT Window

Copy-and-paste AI breaks down at volume. Output drifts, formats wander, character limits get blown, and the model will sometimes claim it checked something it never checked. The fix is not a better prompt. It is engineering:

• Structured output. The model returns a strict JSON schema, so the right fields come back in the right order every time.
• Code-side enforcement. Length limits, required language and formatting rules are validated in code, not requested politely in the prompt.
• Verified grounding. When a feature depends on live data, we require real search evidence before the model is allowed to state a result.
• Human review where it matters. AI output that feeds a client deliverable stays a draft until a person approves it.
• Cost and latency control. Expensive AI calls run when the user asks for them, not on every save.

How AI Projects Work With Us

Can you add AI to our existing application?

Yes, that is most of the AI work we do. We start with the workflow your team already runs, usually something they are doing manually in ChatGPT today, and build it into your application behind a button, with your data flowing in automatically instead of being pasted.

Which AI models do you work with?

We work primarily with OpenAI’s APIs, including the ChatGPT models our delivered projects run on. Model choice is part of the engagement: some features need a search-enabled model for live data, others need a fast, inexpensive model with tightly structured output.

How do you keep AI output accurate and consistent?

Structure and verification. Output is locked to a schema, limits and required language are enforced in code, features that need live data are gated on real search evidence, and anything customer-facing keeps a human review step. This is the difference between a demo and a feature your team stops thinking about.

How do AI projects start?

With a short discovery and, when the risk warrants it, a small paid spike: a focused build that proves the hard part works on your real data before you commit to the full scope. You get working code and an honest read on reliability, then we scope the rest.

Talk to Us About Your AI Project

Tell us what your team is doing by hand that software should be doing. We will tell you honestly whether AI is the right tool for it.