Adding AI to your app is easy. Adding it well is not.
A working prototype is one API call. A production AI feature needs streaming, retries, rate limits, cost controls, and a plan for when the model returns something unusable. We build the second one.
The gap between an AI demo and an AI product is wider than it looks.
Your AI feature works, but responses arrive all at once after ten seconds of blank screen
Your OpenAI or Anthropic key is in your frontend, which means anyone can spend your money
You have no spending cap, so one abusive user or one loop could produce a very memorable bill
The model sometimes returns malformed output and your app crashes rather than recovering
You want to answer questions over your own documents but RAG, chunking, and embeddings are a wall
Your prompts work most of the time and you have no way to tell when a change makes things worse
Sound familiar? You're not alone. This is exactly why we built our ai integration service.
We build the parts that only matter once real users arrive.
Streaming responses done right
Token-by-token streaming so users see output immediately. This single change does more for perceived quality than almost anything else you can do.
Keys server-side, always
Your API keys move behind your own backend, with per-user rate limiting, so your credentials are never shipped to a browser and your bill stays yours.
Cost controls and spend caps
Token budgets, sensible model selection per task, caching for repeated queries, and hard limits. AI costs should scale predictably, not surprise you.
RAG and vector search that actually retrieves
Chunking, embeddings, and retrieval tuned for your content. Most disappointing RAG systems fail at retrieval, not generation, and that is where the work belongs.
Structured output you can rely on
Schema-validated responses with retries and fallbacks, so a malformed generation becomes a handled case rather than a runtime exception.
Graceful failure and fallbacks
Providers have outages and rate limits. We make sure yours degrades into a clear message or a secondary model rather than a spinner that never resolves.
Free consultation. No commitment.
Why ai integration matters for your vibe-coded project.
The first version of an AI feature is deceptively easy. One API call returns something impressive, and it feels close to done. The distance between that moment and a feature real users can rely on is where most AI projects quietly stall.
Consider cost, which is the one that surprises people most. In a normal web app, a user who clicks a hundred times costs you nothing measurable. With an LLM, every interaction has a real price, and a user who discovers your unprotected endpoint can run up a genuinely alarming bill overnight. Cost control is not an optimization to revisit later. It is a required part of shipping.
Then there is nondeterminism. Traditional code either works or throws. A model returns something plausible every time, including when it is wrong. Your app needs to validate output rather than trusting it, and needs a defined behavior for the case where validation fails.
And with retrieval systems, the failure is almost always upstream of the model. When a RAG system gives poor answers, the usual cause is that the wrong chunks were retrieved. People respond by rewriting prompts for a week when the actual problem was chunking strategy the whole time.
The difference between a prototype and a product is in the details. AI Integration is one of those critical details that separates apps that fail from apps that scale.
We've handled ai integration for over 150 vibe-coded projects. We know exactly what you need and how to implement it correctly.
Every day without proper ai integration...
An API bill you did not see coming
An exposed key or an uncapped endpoint can produce four or five figures of usage in a day. Bots find unprotected AI endpoints quickly, because they are unusually worth finding.
Users who leave during the wait
Without streaming, an AI feature looks broken for the first several seconds. Most people do not wait to find out whether it is, they just form an opinion and go.
Confident answers that are simply wrong
Unvalidated output means the model occasionally returns something incorrect in a tone of complete certainty, and your product presents it to a user as fact.
A RAG system nobody trusts
Retrieval that surfaces the wrong context produces answers that are subtly off. Users stop relying on the feature well before they bother to complain about it.
You didn't start this project to let technical blockers stop you. You started it because you had a vision. Let us handle the ai integration so you can bring that vision to life.
Imagine your app with ai integration done right.
Your AI feature starts responding instantly, streaming as it goes, and feels comparable to the tools your users already know.
Your keys are server-side, every request is rate limited per user, and your monthly cost is a number you can predict.
When the model returns something malformed, your app validates, retries, and handles it, and the user never learns anything went wrong.
Your retrieval surfaces the right context, so the answers are grounded in your actual content rather than confidently improvised.
It Just Works
Your ai integration is solid. No more worrying.
This is what happens when you stop struggling and let experts handle the hard parts. Your vibe-coded project deserves to work flawlessly.
What you get with our ai integration service.
Streaming responses for immediate feedback
API keys secured server-side with rate limiting
Hard spend caps and token budgets
RAG with tuned retrieval, not naive chunking
Schema-validated structured output
Fallback handling for outages and rate limits
Prompt caching to cut repeat costs
Usage and cost monitoring from day one
Our ai integration process.
Describe the feature
Tell us what the AI should do for your users. We will be honest about whether an LLM is the right tool, because sometimes it genuinely is not.
We design for cost and failure first
Model choice, context strategy, caching, and spend limits get decided before implementation, because retrofitting cost control is much harder than designing for it.
Build and evaluate against real inputs
We test with your actual data and edge cases, including deliberately awkward inputs, rather than the three examples that happen to work nicely.
Ship with monitoring
Deployed with token usage, latency, and error visibility, so you can see what your AI feature costs and how it behaves without guessing.
Frequently asked questions.
Should I use Claude or OpenAI?
It depends on the task, and often the right answer is both, routed per use case. We benchmark against your actual workload rather than recommending a default. We also build the integration so switching providers later is a configuration change rather than a rewrite, because this landscape moves quickly and you should not be locked in.
How do I stop someone running up a huge API bill?
Several layers. Keys move server-side so they cannot be extracted from your frontend. Per-user rate limits cap how fast anyone can spend. Token budgets bound the cost of a single request. Caching removes repeat charges for identical queries. And hard spend caps at the provider act as a final backstop. Any one of these alone is insufficient.
What is RAG and do I need it?
Retrieval-augmented generation means finding relevant content from your own data and giving it to the model as context, so answers are grounded in your material rather than the model general training. You need it if you want AI to answer questions about your specific documents or product. You do not need it for general tasks like summarizing text a user has already pasted in.
My AI feature works but is slow. Can that be fixed?
Usually yes, and often substantially. The three common causes are no streaming, an oversized model for the task, and unnecessary sequential calls that could run in parallel. Streaming alone typically transforms the perceived experience even when total generation time is unchanged.
Can you add AI to an app that already exists?
Yes, that is most of this work. We add the AI layer to your existing app rather than restructuring what already works. Your current code stays as it is, with new endpoints and components handling the AI functionality.
How long does an AI feature take to build?
A well-built chat or generation feature with streaming, cost controls, and error handling is typically 3 to 5 days. RAG systems take longer, usually 1 to 2 weeks, because retrieval quality needs tuning against your real content and that is iterative by nature.
What if the AI gives users wrong answers?
You reduce it and you design for it. Grounding answers in retrieved content cuts fabrication substantially. Schema validation catches malformed output. And for anything consequential, the interface should show sources and make the AI role clear, so users can verify rather than simply trusting. We build with the assumption that the model will sometimes be wrong, because it will be.
Trusted by 150+ vibe coders.
150+
Projects Completed
3 days
Average Delivery
98%
Satisfaction Rate
4.9/5 average rating from our clients
Ready to get ai integration done right?
Stop struggling with ai integration on your own. Let our experts handle it while you focus on what matters—building your product and serving your users.
Free consultation. No commitment required.