Team
Applied AI Team
The Applied AI team at Datics AI designs, tests and ships the AI capabilities inside established software products: retrieval and search, assistants, agents, and how each one behaves for the people who use it.
The team decides what good looks like before a feature reaches users, builds the test sets from real cases, and keeps measuring once the feature is live.
Its articles come from that work, and each one is reviewed by Rana Umar Majeed, co-founder and CTO of Datics AI.
Applied AI Team
Datics AI
AI evaluation · LLM testing · Retrieval-augmented generation · AI agents · AI user experience
Articles by Applied AI Team
AI evaluation
AI model evaluation: how do you choose an LLM to build on?
AI model evaluation starts with a scorecard: define the task, metrics and test set first, then run every candidate model through the same rerunnable test.
Read the articleRAG and AI assistants
How do you make AI product search match how buyers shop?
AI product search works when it learns how buyers decide, such as a monthly payment budget, and turns that into precise filters over live inventory.
Read the articleAI evaluation
How do you run LLM testing with real user prompts?
LLM testing for an AI assistant should use real user prompts and customer-run tests, because demos hide lost context, repeated answers and wrong filters.
Read the articleAI evaluation
What does good AI agent testing in CI look like?
A tiered approach to AI agent testing in CI: scripted fake models on every commit, and real-model routing and quality evals on demand.
Read the articleBrownfield AI
What makes good AI user experience for non-technical users?
Good AI user experience for non-technical users means recommendations instead of automatic actions, AI work beside their own, and confirmation before saving.
Read the articleAI evaluation
AI model testing: test types, metrics and messy data
AI model testing for AI features in your product: the main test types, what each catches, and how to build a messy test set that becomes your regression suite.
Read the articleRAG and AI assistants
What chatbot features should an AI assistant in SaaS have?
The chatbot features an AI assistant inside a SaaS product needs: grounded answers, citations, user permissions, approved actions, handoff and measurement.
Read the articleYour customers already expect AI from your product.
Let us make it real inside the product you run today.