Generative AI at IRPR.io means building production features powered by LLMs — not another wrapper, not another demo. Our team has shipped 30+ AI products that serve millions of real users across fintech, healthcare, SaaS, and e-commerce.
Built around a real RAG pipeline.
Generative AI · how we actually work.
The work is mostly engineering. Model selection, prompt caching, safety guardrails, evaluation harnesses, cost observability, latency tuning, and the integration layer that ties all of it to your existing product.
We build AI features that hold up at scale: cost-controlled, latency-tuned, hallucination-guarded, and instrumented so you can see exactly what's happening in production.
AI products that actually ship — not just demos.
Generative AI as a product feature.
Not demos. Not pilots. Real features in real products.
Most AI projects die before production. Ours don't — because we design for the parts Twitter doesn't talk about: cost, latency, safety, observability.
Ships with every AI feature.
The production-readiness bar. Non-negotiable.
- Golden-set evaluation suite
- LLM-as-judge scoring
- Prompt injection input validation
- Output policy filtering
- Citation enforcement for facts
- Per-user spend limits
- Cost + latency dashboards
- Regression alerts in Slack
Code that ships.
This is what production AI feature integration looks like.
1// Classify user intent, route to appropriate model2const intent = await classify(userInput)3const model = router.pick(intent.complexity)45// RAG-ground the response6const ctx = await retrieval.search(userInput, { k: 6 })7const ranked = await rerank(userInput, ctx)89// Stream with observability + cost tracking10const stream = await llm.complete({11 model,12 system: cached(prompts.assistant),13 messages: [...history, { role: 'user', content: userInput }],14 context: ranked,15})1617metrics.emit(stream.usage)
What we ship in this practice.
AI-assisted workflow inside your product
Draft-and-review patterns, smart suggestions, natural-language commands, AI summaries of complex data. The LLM-as-feature pattern, done right.
Custom copilot / agent
Domain-specific AI assistant grounded in your data. Tool-using, RAG-backed, evaluation-driven. Not a chatbot — a product feature.
Content generation pipelines
Marketing copy, product descriptions, SEO content generated at scale with brand voice + human review queues.
Intelligent classification / extraction
Pull structured data from documents, classify support tickets, route inbound leads. Measured accuracy, cost-controlled.
Idea → Roadmap → Product → Release.
Every engagement runs through the same four-stage pipeline. Predictable by design.
Tools we trust.
Generative AI for your context.
Tailored entry points by industry vertical or US metro - each page is hand-tuned with the right keywords, compliance, and case studies.