Transforming AI-Powered Product through Technology
AI products require more than an OpenAI API key. We build RAG pipelines, streaming response infrastructure, eval pipelines that catch quality regressions, and the product UX that makes latency feel acceptable rather than broken.
RAG Is Not a Solved Problem. It Is an Engineering Problem.
Everyone has a RAG demo. Few have RAG in production serving real users. The difference: chunking strategy tuned to your content type, retrieval ranking calibrated to your query patterns, and eval pipelines that measure quality continuously.
Chunking strategy depends on your content: legal documents need section-aware splitting, technical docs need code-block preservation, conversational content needs turn boundaries. One-size-fits-all chunking produces mediocre retrieval.
Embedding model choice matters more than most teams realize. Domain-specific fine-tuned embeddings outperform general-purpose ones by 20-40% on retrieval relevance. We test multiple approaches against your actual query distribution.
Retrieval is not just vector similarity. We build hybrid search (dense + sparse), re-ranking layers, metadata filtering, and the contextual retrieval that brings back the right information, not just the mathematically closest text.
Making AI Feel Fast When It Is Not
LLM inference takes 2-5 seconds for a complete response. Streaming token-by-token makes this feel instant to users. But streaming introduces complexity: error handling mid-stream, output validation on incomplete text, and progressive UI rendering.
Caching strategies for AI are different than traditional APIs. Semantic caching (similar questions get cached answers) reduces costs 30-50% and improves latency. But cache invalidation requires knowing when your source data changes.
We build confidence scoring into every AI output. When the model is uncertain, the UI shows it. This builds user trust over time because the system is honest about its limitations rather than confidently wrong.
Eval pipelines are the difference between an AI feature that improves over time and one that silently degrades. We build automated quality scoring, regression detection, and A/B testing infrastructure that catches problems before users lose trust.
Technical Capability
Our AI-Powered Product Stack
AI-native products where the machine learning works reliably in production, not just in the demo.
Key Priorities
Standard Deliverables
The architecture artifacts you receive in every AI-Powered Product engagement.
We understand your unique pain points
LLMs hallucinate in production. RAG pipelines need real infrastructure. We build AI products that work when real users find the edges.
AI-native products where the machine learning works reliably in production, not just in the demo.
Who we help
We partner with forward-thinking organizations ranging from agile startups to established enterprises to deliver AI-Powered Product solutions that drive true market leadership.
Legal research tools with 98%+ citation accuracy
Customer support AI handling 60% of tickets without human escalation
Content generation platforms serving 50,000+ monthly users
Document analysis systems processing 10,000+ files daily
How CiroStack Empowers AI-Powered Product
We apply our proven engineering disciplines to solve your most complex sector challenges.
Generative AI Development
Vector databases, embedding pipelines, retrieval ranking, prompt management, and the orchestration layer that coordinates context and model calls into reliable, measurable outputs your users can trust.
Explore ServiceAI & ML Engineering
Custom model training, fine-tuning pipelines, golden dataset creation, automated quality scoring, and the regression detection that catches model degradation before your users experience it.
Explore ServiceAI Backend Infrastructure
Production AI APIs with streaming support, vector database architecture, context window management, rate limiting, and the backend systems that keep inference reliable and latency predictable at scale.
Explore ServiceHuman-AI Interaction Design
Designing where to show confidence scores, how to present sources, where to surface corrections, and how to make generation latency feel acceptable — the UX layer that determines whether users trust your AI.
Explore ServiceFrequently Asked Questions
Specific insights into our AI-Powered Product engineering process.