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Artificial Intelligence
Production AI systems — not demos, not wrappers.
Build AI-powered applications with real production infrastructure — LLM integrations, RAG pipelines, computer vision, and intelligent automation systems that solve actual business problems.
Choose Your Tier
All tiers ship a fully production-ready product. Choose based on your current stage, compliance needs, and growth ambitions.
Focused AI feature or assistant integrated into a working product.
4 – 6 weeks
Full AI-powered application with pipelines, monitoring, and fine-tuning.
8 – 14 weeks
Enterprise AI infrastructure with compliance, scale, and custom models.
Custom timeline
Overview
The AI application space is full of demos that cannot survive production load and wrappers around ChatGPT that stop working when the API changes. ZIRA builds AI applications that are production-grade from day one — with proper data pipelines, retrieval-augmented generation (RAG) systems, vector database infrastructure, and monitoring that keeps AI behavior predictable and auditable.
We have built AI assistants with domain-specific knowledge, automated document processing pipelines, intelligent recommendation engines, and real-time AI decision systems. Our engineers understand the difference between a fine-tuned model and a well-designed RAG system, and we choose the right approach for your use case and budget.
Our AI MVP packages are priced to reflect the higher engineering complexity of production AI systems. What you receive is a working, monitored, explainable AI application — not a prototype that breaks when the data gets messy.
Tech Stack
Use Cases
Every Package Includes
How We Work
We map the exact problem you are solving with AI, evaluate whether LLM, RAG, fine-tuning, or a traditional ML approach is correct, and design the data pipeline before writing any code.
Source documents, structured data, or knowledge bases are cleaned, chunked, embedded, and indexed in the vector database. Data quality at this stage determines AI quality at every stage after.
RAG retrieval, LLM integration, prompt chains, function calling, and output parsing built and evaluated against a test dataset. Iterative prompt optimization until quality benchmarks are met.
The user-facing product — chat interface, search UI, automation dashboard, or workflow tool — built on top of the AI pipeline with streaming responses and latency optimization.
Automated evaluation suite, red-teaming for adversarial inputs, hallucination detection, and output safety review before any real user traffic.
Deployment with LLM cost monitoring, latency alerting, quality dashboards, and a human-review workflow for flagged outputs. AI systems require ongoing monitoring — we set this up from day one.
Why ZIRA
Production-grade AI that works on real, messy data — not just clean demos
RAG architecture that keeps your AI knowledge up to date without retraining
Cost-controlled LLM usage with monitoring and budget alerts
Explainable AI with audit trails for regulated industries
Model-agnostic architecture so you can switch LLMs as the market evolves
Human-in-the-loop workflows for high-stakes AI decisions
Evaluation frameworks that measure AI accuracy objectively
Full ownership of your AI pipelines, data, and infrastructure
Questions
We work with OpenAI (GPT-4o, o1), Anthropic (Claude 3.5 Sonnet), Google (Gemini), Meta (Llama), and self-hosted open-source models. Model selection depends on your cost, latency, data privacy, and quality requirements — we advise based on your specific use case.
Retrieval-Augmented Generation (RAG) connects an LLM to your specific data sources without retraining the model. Instead of fine-tuning, the LLM retrieves relevant documents from your knowledge base and uses them to answer questions. This keeps your AI current, accurate, and auditable — and is significantly cheaper than training custom models.
We design prompts with explicit grounding instructions, implement retrieval confidence scoring to avoid generating answers from insufficient context, add output validation layers, and build human-review workflows for high-stakes outputs. No AI system is hallucination-free, but ours are architecturally designed to minimize and surface it.
Yes. For data-sensitive applications, we deploy open-source models (Llama 3, Mistral, etc.) on your own AWS VPC or use Azure OpenAI with data processing agreements. Your data stays on your infrastructure. This is standard for our Enterprise tier.
LLM API costs depend on usage volume, model, and context length. As a rough benchmark, GPT-4o processes approximately 1 million tokens per $5. We implement cost monitoring, caching strategies, and model routing to minimize spend. We provide monthly cost projections as part of the architecture review.
Ready to Build?
Book a free discovery call. We will scope your product, confirm the right tier, and send a written proposal within 48 hours.