Generative AI is any model that produces new content — text, images, audio, code — rather than just classifying or retrieving existing content. For most of our clients, that shows up as one of two things: text generation (drafting copy, summaries, replies, structured content) or image generation (visuals, variations, mockups). The interesting engineering work isn't calling the model — it's everything around that call.
A generation feature that looks simple in a demo gets complicated fast in production: you need to constrain output to your brand voice or visual style, handle the cases where the model produces something unusable, control cost when a feature gets used thousands of times a day, and decide where a human should review before something goes live. We build all of that as part of the feature, not as a follow-up.
The goal is always a feature that feels native to your product — a button your users reach for naturally — rather than an obvious "AI panel" stapled onto the side of your app.
Product descriptions, marketing copy, personalized emails, and summaries generated on-demand inside your product, matched to your brand voice and constraints.
On-brand visuals, product mockups, and design variations generated from a prompt or a reference — built into your existing tools instead of a separate app.
Generative features woven directly into your app's workflow — a 'draft this for me' button, an auto-fill, a variation generator — not a bolted-on chat sidebar.
The parts that separate a demo from a shipped feature.
Generation is expensive and slow if you call the biggest model for every request. We right-size the model per feature.
Output validation, style constraints, and human review points so generated content matches your standards before it ships.
The prompt is one piece. Structuring inputs, retries, and post-processing is what makes output consistent at scale.
Check LLM development for RAG and fine-tuning, or browse the full AI development services hub.
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