Every second client conversation at Zartek in 2026 starts the same way.
"Can we add AI to this?"
Sometimes it's a great idea. Sometimes it really isn't. But "add AI" is usually where the thinking stops, and that's where a lot of products start going sideways. Over the last couple of years, the Zartek team has shipped AI features into food delivery apps, OTT platforms, e-commerce products, and healthcare tools. Here's the simple version of what actually holds up in production.
Start With the Problem, Not the Technology
The first mistake Zartek sees teams make isn't technical. It's strategic.
Founders decide they need AI because competitors have it. They pick a feature, wire up an LLM, and ship it. A few weeks later, nobody uses it.
Start with a real user problem, then ask whether AI is the right tool. If a search bar with filters solves it, you don't need a chatbot. AI shines when inputs are open-ended, when users can't express what they want in keywords, or when personalization beats categorization. Everything else is usually solved better with classical software.
Don't Call the AI From the Client
This is the most common mistake Zartek sees in codebases we inherit.
Teams wire the app directly to OpenAI or Anthropic. It works in development. It breaks in production. Your API key gets exposed. One bad user can drain your credits overnight. You can't switch providers without shipping an app update. You can't improve prompts without a code release.
The rule Zartek follows on every project: the AI lives on the backend, behind your own API. The client calls your API. Your API calls the AI provider. You get control over cost, security, and iteration speed, without touching the client ever again.
Make It Feel Fast:
AI responses are slow. A typical reply takes two to ten seconds. Users hate waiting.
The fix is streaming. Instead of a loading spinner, the answer appears word by word as it's generated. Same total time, dramatically better experience. Every chatbot that feels responsive — ChatGPT, Claude, Gemini — works this way. Zartek builds streaming in from day one because retrofitting it later is painful.
Always Have a Plan B:
AI providers go down. Your self-hosted model will crash. Networks fail. It's not if, only when.
The worst user experience is staring at an error because the AI broke. The second worst is an infinite loading spinner.
For every AI feature Zartek builds, we plan a fallback. If smart search fails, the app falls back to keyword search. If the AI chatbot is down, the user sees a friendly message pointing to the FAQ. The fallback doesn't need to be as good as the AI version. It just needs to be better than a broken screen.
Caching Saves Real Money:
A lot of AI queries are repeats. Users ask the same questions. Similar product searches come up across thousands of customers. Without caching, you pay for the same answer over and over.
The simple version stores exact repeats. The smarter version recognizes that "cheap sneakers under 2000 rupees" and "affordable sneakers under 2k" mean the same thing, and serves the same cached response. In Zartek's projects, this kind of caching regularly cuts AI costs by 40 to 60 percent where queries naturally repeat.
Log Everything:
AI is unpredictable in a way normal software isn't. Prompts that worked last month can quietly produce worse results after a provider updates their model. The only way to stay ahead is observability.
For every AI call, Zartek logs the prompt, the response, how long it took, and whether the user found it useful. Someone reviews a sample every week. This single habit has caught more silent regressions than any testing framework.
What AI Features Actually Work:
A few that Zartek ships regularly and that deliver real value.
Smart search that understands meaning instead of keywords. A user typing "something light for dinner" matches salad options with zero keyword overlap.
Customer support chatbots done right resolve 60 to 80 percent of routine queries before a human gets involved. Done wrong, they frustrate users into leaving. The difference is how well the bot is connected to real context about the user and business.
Recommendations for cold-start users where you don't have months of data. LLMs suggest relevant products after a single interaction.
Content generation — product descriptions, email copy, captions — is the fastest win for most businesses. Low risk, high time savings, humans still review before publishing.
What Usually Goes Wrong:
A few things Zartek has learned the hard way.
Prompt injection is real. If your app passes user input to an AI, someone will try to manipulate it — to make the chatbot say offensive things or reveal confidential data. It happens to every public AI feature eventually.
Users expect consistency. They don't realize the same question can produce different answers on different days. When two users compare notes and get different replies, trust erodes fast.
And AI often feels impressive in demos but underwhelming in daily use. Zartek tests features over weeks of real usage before deciding whether to expand them.
Wrapping Up:
AI features are genuinely valuable when they solve a real problem. They're expensive tech debt when they're bolted on because every startup is doing it.
The principles above — start with the problem, keep AI on the backend, stream responses, plan for failures, cache aggressively, log everything — hold up across the dozens of AI features Zartek has shipped into production. None of it is revolutionary. It's just the version that actually works when real users hit the system.
If you're planning an AI integration, the AI development services team at Zartek has this conversation with founders every week. Happy to dig in.
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