AI That Solves Real Problems, Not Just Demos
Production-grade AI integration for applications that need to work reliably at scale. OpenAI, Anthropic Claude, LangChain, RAG systems, and ML pipelines — built with the same quality standards as every other production feature.
What We Build
The gap between a working AI demo and a production AI feature is enormous. Production means proper error handling when APIs time out. It means cost management when thousands of users are making requests. It means latency optimization so the feature feels responsive. It means monitoring so you know when something breaks. Creatos DaaS approaches AI engineering with the same production-quality standards applied to all development work. The result: AI features that deliver measurable business value, not just impressive prototypes that break under real-world conditions.
LLM API Integration
Integrating OpenAI, Anthropic Claude, or other LLM providers into your existing products. Prompt engineering, streaming responses, function calling, and proper error handling for production-scale usage.
RAG System Development
Retrieval Augmented Generation systems that ground AI responses in your specific data. Knowledge base search, documentation assistants, and conversational AI that provides accurate, cited answers.
AI-Powered Chatbots
Intelligent conversational interfaces for customer support, sales assistance, internal knowledge management, and product documentation. Built to handle edge cases gracefully and escalate appropriately.
ML Pipeline Deployment
Taking trained machine learning models from notebooks to production. Feature engineering, model serving infrastructure, monitoring for drift, and automated retraining pipelines.
Intelligent Automation
Workflow automation powered by AI. Document processing, classification, extraction, summarization, and routing that reduces manual effort on repetitive tasks.
AI Feature Prototyping
Rapid proof-of-concept development to validate AI feature ideas before committing to full production implementation. De-risk your AI investment with a working prototype in 1-2 weeks.
Technologies We Use
Working with a technology not listed here? Our expertise spans a wide range of frameworks, languages, and platforms. Discuss your stack →
Our Approach
Use Case Validation
Before building anything, validate that AI is the right solution for the problem. Not every feature needs AI, and not every AI approach fits every use case. Honest assessment saves time and budget.
Prototype First
Build a working proof-of-concept in 1-2 weeks to validate feasibility, cost, and quality before committing to full production implementation.
Cost-Optimized Architecture
Design for cost efficiency from the start. Model selection, caching strategies, prompt optimization, and batch processing to keep API costs predictable and manageable.
Production Hardening
Error handling, rate limiting, fallback strategies, latency optimization, and monitoring. Production AI features need to handle the real world gracefully.
Measurement and Iteration
Define success metrics before launch and instrument the feature to track them. AI features improve with data, so build the feedback loop from day one.
What You Get
- Production-ready AI feature integrations in your application codebase
- RAG system implementations with vector database configuration
- Prompt engineering libraries with tested, optimized prompts
- AI feature documentation including architecture decisions and cost projections
- Monitoring dashboards for AI feature performance, cost, and quality metrics
- Cost modeling with projected per-user AI costs
- Prototype demonstrations for stakeholder validation
Timeline Expectations
AI feature prototype
1-2 weeks
LLM API integration
1-3 weeks
RAG system development
3-6 weeks
ML pipeline deployment
4-8 weeks
Full AI product feature
4-12 weeks
Ideal For
- Startups building AI-powered products that need production-quality engineering behind the models
- Companies adding AI features (search, recommendations, automation) to existing SaaS products
- Teams that have built AI prototypes and need help getting them production-ready
- Organizations exploring AI-driven automation for manual, repetitive processes
- Agencies with client AI requests that need specialist engineering capability
Frequently Asked Questions
Ready to Build AI Features That Work in Production?
Whether you need a prototype to validate an idea or production engineering for an AI feature, the discovery call is where we assess the feasibility, costs, and right approach for your use case.