Writing about AI agents, search APIs, and developer tools at Cloudsway.
Technical writing collaborations, developer content, and conversations about AI search and agent workflows.
Absolutely—brand monitoring and AI discovery are really two sides of the same entity-understanding problem. When business information is inconsistent across sources, AI systems may misidentify a company, merge it with another entity, or lack the confidence to surface it at all. What you’re building at Oglas AI sounds highly relevant to where this space is heading. I’d be interested to hear how you’re approaching entity verification and information consistency.
Great question. I’d use a two-stage approach: canonicalize the URL first—following redirects, removing tracking parameters, and checking rel="canonical"—then fall back to a normalized content hash when different URLs still serve the same page. I’d also keep the original URLs as aliases and preserve retrievedAt for each fetch, so deduplication doesn’t remove provenance or freshness information. The canonical URL is the cheaper first check, while the content hash catches mirrors and unreliable canonical tags.
Good question. Our current offline relevance evaluation is human-led—so it’s actually the more expensive option 😄 We manually review query–result pairs against the original search intent and use those judgments as the quality benchmark for improving the Cloudsway Search API, rather than using an LLM judge as the final scorer. And agreed on the retry cap—a grader alone doesn’t make the retrieval loop reliable. If you’d like, I’d be happy to give you access to test the API against your own evaluation setup. I’d especially value the failure cases you find.
