Transforming AI Startups through Technology
Most AI startups ship a wrapper around an API call and call it a product. Real AI engineering means RAG pipelines with retrieval quality you can measure, eval infrastructure that catches regressions automatically, latency optimization that makes generation times feel instant, and cost architecture that does not bankrupt you at scale. We build AI products that survive contact with real users.
Beyond the API Call: Building AI That Works in Production
Wrapping an LLM API in a chat interface is a weekend project. Shipping an AI product that works reliably for 10,000 users requires retrieval infrastructure, quality monitoring, cost management, and the engineering discipline to measure everything.
RAG pipeline quality depends on chunking strategy, embedding model choice, retrieval method (vector, keyword, or hybrid), and re-ranking. We tune each component to your specific content type and query patterns, then measure precision and recall continuously.
Hallucination is not a bug you fix once. It is a surface area you manage. We build source grounding, confidence scoring, citation generation, and output validation layers that prevent your AI from confidently stating nonsense.
Eval infrastructure is the difference between an AI demo and an AI product. We build golden datasets, automated quality scoring, regression detection, and the dashboards that tell you exactly when output quality changes.
Scaling AI Without Scaling Costs
Inference costs are the gross margin killer for AI startups. At $0.03 per query, 1M monthly queries costs $30,000. We build model routing (use GPT-4 only when needed, route simple queries to cheaper models), response caching, and prompt compression that cut costs 50-70%.
Latency optimization is UX optimization. Streaming token-by-token output, speculative pre-generation, and progressive rendering transform a 4-second wait into a perceived-instant response. Users stay engaged instead of bouncing.
Fine-tuning smaller models on your specific domain data often outperforms prompting larger models at 10x lower cost and 5x lower latency. We help you decide when fine-tuning earns its training investment and when prompting is enough.
AI products that scale need observability: token usage per feature, cost per user segment, latency percentiles, and quality scores by query type. We build the dashboards that let you make informed model and architecture decisions.
Technical Capability
Our AI Startups Stack
Production-grade AI products built by engineers who understand that the model call is 10% of the work.
Key Priorities
Standard Deliverables
The architecture artifacts you receive in every AI Startups engagement.
We understand your unique pain points
RAG pipelines that hallucinate less than 1%. Eval infrastructure that catches regressions before users do. AI products built for production, not demos.
Production-grade AI products built by engineers who understand that the model call is 10% of the work.
Who we help
We partner with forward-thinking organizations ranging from agile startups to established enterprises to deliver AI Startups solutions that drive true market leadership.
RAG-powered enterprise search products serving Fortune 500 companies
AI writing assistants processing 500K+ generations monthly
Document intelligence platforms extracting data from unstructured PDFs
AI coding tools with context-aware autocomplete and code review
How CiroStack Empowers AI Startups
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 do.
Explore ServiceAI Backend Infrastructure
Production AI APIs with streaming support, vector database architecture, context management, rate limiting, and the backend systems that keep inference reliable and latency predictable at scale.
Explore ServiceAI Cloud Strategy
GPU instance strategy, managed vs self-hosted inference trade-offs, vector database selection, and the cloud architecture that keeps per-query costs predictable as your user base scales.
Explore ServiceFrequently Asked Questions
Specific insights into our AI Startups engineering process.