Adding AI without a rebuild
Most AI features are added as a layer, not a rewrite. Your app keeps working as it does; we add a service that calls a language model, feeds it the right context from your data, and returns results into your existing interface. Your users see a new button or panel, not a new product.
This works with most stacks — a web app, a SaaS backend, an internal tool, a mobile app. The AI logic lives in its own service, so it can be added, changed, or removed without disturbing the core product.
- AI runs as a separate service your app calls, not a change to your core code
- Common additions: draft generation, summarization, semantic search, classification, chat
- Works over your existing data — customers, tickets, documents, catalog
- Fits into your current UI so users don't have to learn a new tool
- Model choice (GPT, Claude, or others) picked for the task and swappable later
The parts that make it production-ready
A demo that calls a model is easy. A feature real users depend on needs the unglamorous parts: handling rate limits and outages gracefully, controlling cost, validating what goes in and checking what comes out, and logging enough to debug without storing sensitive data you shouldn't.
We build those in from the start, so when the model provider has a slow day or a user sends something weird, your feature degrades gracefully instead of breaking.
- Graceful handling of rate limits, timeouts, and provider outages with fallbacks
- Cost controls: caching, prompt sizing, and usage limits so bills stay predictable
- Input validation and output checks to catch bad or unsafe responses
- Streaming responses for a fast, responsive feel where it helps
- Logging and monitoring that respect privacy and help you debug
- The ability to switch models later without rewriting your app
More on ai development
Frequently asked questions
Will you need to rewrite our app?
Almost never. The AI is added as a separate service your existing app calls. Your current code, database, and interface stay in place — we build alongside them.
Which model should we use — GPT, Claude, or something else?
It depends on the task, your budget, and your privacy needs. We build so the model is swappable, so you're not locked in. We'll recommend one based on your actual use case rather than a favorite.
How do we keep the running costs under control?
By sizing prompts, caching repeated requests, and setting usage limits. We estimate the per-request cost with you before launch so you know what monthly usage will look like at your expected volume.