How we're building Momor, what we're learning, and what we believe.
When organizations realize their internal data is fragmented, they usually look for an "Enterprise AI Search" tool. **Glean** is currently the most recognized name in this space, offering a powerful "internal Google" experience.
When an enterprise engineering team is tasked with bringing AI into their internal workflows, the instinct is often to build it themselves. They spin up **LangChain**, **LlamaIndex**, or **CrewAI**, connect a few APIs, and build a proof-of-concept agent in a weekend.
For organizations running on Microsoft 365, **Microsoft Copilot Studio** is the default path of least resistance for AI automation. It promises deep integration with Teams, Outlook, and SharePoint, allowing you to build custom agents that live where your employees work.
Consumer AI search engines like **Perplexity** and **You.com** have changed how we interact with the web. By synthesizing search results into coherent answers, they eliminated the need to click through ten blue links.
When deploying AI into legal, financial, or healthcare workflows, the standard rules of SaaS no longer apply. You cannot ingest highly confidential data into a black-box model and hope the provider respects their data processing agreement.
For the last decade, the enterprise data problem was treated as a search problem. Companies bought massive indexing platforms to scan SharePoint, Slack, and Salesforce, hoping that if an employee could just *find* the right document, the workflow would become efficient.
The enterprise AI conversation has rapidly shifted from "chatbots" to "agents." The promise of agentic automation is alluring: an autonomous system that takes a goal, breaks it down, and executes it without human intervention.
**2026 is the year search stopped pretending AI was optional**. Google added Gemini 3 to AI Overviews. OpenAI launched ChatGPT Search to 800 million users. Perplexity hit a $20 billion valuation while getting sued by half the publishing industry. And a federal court ruled Google is a monopolist — then let them keep Chrome anyway.
Searching for academic papers usually means fighting with paywalls, clunky university logins, or "free" sites that demand your email.
Perplexity AI changed how we search. It proved that people want direct answers, not a list of blue links. But as it has grown, so have the concerns. Perplexity is a venture-backed company that needs to monetize, and increasingly, that means ads and data tracking.
ChatGPT changed how millions of people search for information. You can ask questions in plain English, get intelligent answers, and follow up naturally. It's powerful, it's impressive, and it's genuinely useful.
**Looking for a Perplexity alternative?** I built Momor Search because I was using Perplexity and kept hitting the same wall. It was June 25th when I realized what was wrong. Every query started fresh. Every answer required me to piece together what was relevant right now, to me, in my situation. That is what this comparison is about.
If you have searched for a recipe online in the last five years, you know exactly what I am talking about.
Apple announced today that they are partnering with Google in a multi-year, billion-dollar deal to power something called World Knowledge Answers. The idea is to turn Siri into an actual answer engine - not just opening apps or setting timers, but understanding what you are asking and giving you a real response.
Google's ten links are dead, and they have been dying for years. The results load in half a second, but finding your actual answer takes ten minutes. You click, scan, hit back, try another link, scan again, and piece it together yourself. Google did not make search fast. Google made their part fast and outsourced the rest to you.
Search engines have trained everyone to lower the bar.
Answer engines were supposed to fix search. Instead of ten links and ten minutes of clicking, you ask a question and get an answer. Apple is building something called World Knowledge Answers. Perplexity has been at it for a while. ChatGPT taught millions of people to ask questions in full sentences. Google added AI Overviews above their link lists.
I built Momor because search was broken. You'd type something specific and get everything except what you asked for. So I built something that just works - you search, you get what you searched for.
**The search engine landscape has never been more competitive**. While Google still dominates with 89.6% of global market share, 2025 has brought significant innovations in AI integration, privacy features, and specialized search capabilities. Here's your comprehensive guide to the best search engines available today.
Remember when asking your phone for restaurant recommendations felt revolutionary? That was quaint. Today's AI-powered search engines can write sonnets and explain quantum physics, yet they still can't figure out whether you should water your plants this afternoon. We've built machines that can simulate human conversation but somehow missed the most human thing about asking questions: we never ask them in a vacuum.
You know that moment when you're staring at search results that are technically correct but completely useless? Like when you search "should I go running today" and get articles about the benefits of exercise instead of whether it's actually a good day to run where you live. That's the moment we built Momor to fix.
The search giants spent billions building elaborate tracking systems to guess what you want. We realized something simpler: just understand what people are actually trying to do.