Question 1

What AI systems do you build?

Accepted Answer

We build production AI systems including LLM applications, AI agents, RAG systems, and AI-enhanced products. From conversational AI to document processing to intelligent automation—we handle the complete development lifecycle from architecture to secure production deployment.

Question 2

How quickly can you start our AI project?

Accepted Answer

Most AI engagements begin within 2-5 days. We match senior engineers based on your AI requirements and tech stack, then integrate them into your development workflow immediately. No months of discovery—we start building fast.

Question 3

How much does AI development cost?

Accepted Answer

AI development costs vary by engagement model. AI Sprints range from $15K-$50K for 2-4 week rapid prototypes. Forward-deployed engineering teams start at $50K/month for embedded senior engineers. Enterprise engagements are scoped based on compliance requirements and project complexity. Contact us for a detailed estimate based on your specific needs.

Question 4

How long does it take to build an AI product?

Accepted Answer

Timeline depends on scope and approach. AI Sprints deliver working prototypes in 2-4 weeks. Full product builds with embedded teams typically reach MVP in 8-12 weeks. Enterprise AI systems requiring compliance and security review take 17-20 weeks from architecture to production deployment.

Question 5

Do you only work with large enterprises?

Accepted Answer

No. We work with high-growth startups, scale-ups, and enterprises. What matters is the complexity of the AI challenge and your commitment to shipping production systems. If you need serious AI engineering—not consulting theater—we can help.

Question 6

What makes Procedure different from AI consultancies?

Accepted Answer

We're engineers who write production code, not consultants who deliver slide decks. Our team embeds with yours, ships working systems, and leaves your team stronger. No months of discovery, no handoff nightmares, no POCs that never reach production.

Question 7

How do you handle AI security and compliance?

Accepted Answer

Security is built into every AI system we develop—not bolted on later. We implement secure LLM architectures, prevent prompt injection, ensure data privacy, and meet compliance requirements (SOC 2, HIPAA, GDPR). Our AI security expertise comes from building production systems that enterprises trust.

Engineering AI Systems
That Actually Ship

All Articles

The Complete Guide to Notion MCP: Connect Your Notion Workspace to AI Agents

Breaking Docker’s LLM in 30 Minutes And What It Means for AI Security

Beginner’s Guide to Model Context Protocol (MCP) for Smarter AI Systems

Observability 101: Logs, Metrics, Traces & Monitoring Modern Systems

Scalable Frontend Architecture: A Step-by-Step Guide for Modern Web Apps

Stay ahead of AI trends

User Experience Observability: Metrics, Errors, and Tools You Need to Know

How to Prevent JavaScript Lag Using Web Workers

Mastering Expo EAS: Submit, OTA Updates, and Workflow Automation

Automate Mobile App Builds with Expo EAS (No CI Server Required)

Optimizing MongoDB Array Updates: Strategies, Benchmarks & Best Practices

Engineering Zero Downtime Database Migrations with AWS Aurora

The Streaming Backbone of LLMs: Why Server-Sent Events (SSE) Still Wins in 2026

Ready to Build Production
AI Systems?

Engineering AI SystemsThat Actually Ship