Think about your own work or business in this area: The Web Search API for AI. Used by McKinsey, SNCF, Cohere, Legora, Polymarket, and Artisan AI. #1 on OpenAI's SimpleQA. What is the single biggest problem you have there right now? Be specific about what it costs you (time, money, customers) and what you have already tried.

Randal Friesennegative

The biggest problem we have right now with our web research layer is its fragility and the sheer amount of manual effort involved. We're stitching together Google searches, custom Python scripts that break often, and then manually reviewing spreadsheets for citation checks. It's incredibly time-consuming, easily costing us 10-15 hours per week of engineering time that could be spent on core product development. We've tried optimizing the scripts, but the fundamental issue is the reliance on unstructured web data and the constant need for manual verification. I'm also concerned about the limitations of the basic search API we currently pay for – it's not designed for the kind of deep, context-aware research our AI needs.

Simulated interest: 70/100

Hazel Bruennegative

The biggest problem? Verification. I spend hours manually cross-referencing AI outputs against primary legal sources and official databases. This 'citation audit' is critical for accuracy, but it consumes days of my team's time each week. We're comparing against the Law Society database and legislation directly. While the speed of AI is appealing, the cost of human verification is enormous, both in terms of my time and my team's capacity. I'm skeptical about any API claiming to solve this without demonstrating granular citation accuracy.

Simulated interest: 30/100

Giuliano Thöneneutral

The biggest issue with our current search process is the time I lose cross-checking manual sources against inconsistent free tools. Last quarter, I spent 15 hours reconciling competitor pricing data—errors left unchecked could cost clients millions in missed opportunities. We tried free APIs, but they either hit rate limits or returned unreliable PDF scraps. Now we’re testing a paid API at €500/month, but I still compare every result to my manual work, which doubles my workload. The real cost isn’t just the tool’s price—it’s the hours I could spend improving methodology instead of firefighting bad data. I’d need proof this API actually reduces errors before trusting it.

Simulated interest: 35/100

Sarala Iyernegative

The biggest problem is exactly what you're touching on: how we gather information. Right now, it's a manual process. I spend hours each week piecing together data from various free search results, bookmarks, and Slack threads for account research and customer support. It's time-consuming and prone to errors because we don't have a standardized tool. We've tried organizing our bookmarks better and creating shared search folders, but it's still inefficient. The cost is primarily my time, which could be better spent on process improvement, but I also worry about the accuracy impacting our customer interactions.

Simulated interest: 70/100

Marcos Howenegative

The biggest problem for us is managing the escalating costs of the external search API we currently use. As our data volume grows, so does our bill, and it's becoming a significant line item we can't easily absorb. We've tried optimizing our queries and implementing caching layers, but the core issue remains. The sheer volume of data we need to index and retrieve for our news monitoring product makes this a constant concern. It's hard to predict and control.

Simulated interest: 70/100

Léa Leroynegative

The biggest problem for us right now is ensuring the accuracy and efficiency of our fact-checking process, especially with increasing client demands and complex product lines. We rely heavily on manual review and free tools, which is time-consuming and prone to human error. While I see the names of large companies using your API, I'm not sure how a web search API for AI would integrate with our current workflow or if it would be cost-effective for our small agency's tight budget. We need to be certain it would save us significant time and resources rather than add complexity or expense.

Simulated interest: 20/100

Jeffrey Bergenegative

Right then, a Web Search API for AI, eh? Sounds fancy, but honestly, the biggest problem I have right now is the sheer time I spend manually compiling data for reports. Clients want super specific breakdowns – competitor ranking changes, keyword performance across different platforms, that sort of thing. I spend hours pulling data from various sources, often just screenshots and spreadsheets, then trying to stitch it all together. It's painstaking. If I had to put a cost on it, I'd say it eats up at least 10-15 hours a week that I could be spending on actual strategy or finding new clients. I've looked at some dashboard tools, but they're either too generic or too expensive for what I need.

Simulated interest: 10/100

Davina Willemsneutral

The 'Web Search API for AI' sounds interesting, especially given your client list. For us, the biggest problem is ensuring consistent data quality across our various internal tools and the external search service we already use. We're spending too much time reconciling discrepancies and manually validating data, which probably amounts to several hours per person per week – valuable time my team could use for more strategic improvements. We've tried refining our existing scripts, but it's becoming a maintenance headache. I'm curious how your API handles data normalization and what kind of integration effort would be involved.

Simulated interest: 50/100

Gerald Beiernegative

Honestly, the biggest problem is the sheer manual effort. My team spends hours each week digging through LinkedIn, news articles, and SEC filings to update our employer profiles and find hiring signals. We're a seven-person startup, and our single engineer can only do so much with the data pipeline. This manual process costs us significant time – easily 10-15 hours per person per week – which directly impacts our outbound campaign speed and accuracy. We've tried building some internal scripts, but they're limited and break often. It's hard to keep up with the pace of change when so much is done by hand.

Simulated interest: 80/100

Atreyee Bharadwajnegative

The biggest problem for me is the sheer time spent manually cross-checking ambiguous outputs. We use a paid API, but its reliability isn't 100%, so I have to verify flagged items with free searches and screenshots. This hybrid approach, while necessary for accuracy, consumes hours that could go towards higher-level analysis. I've tried refining the API's parameters, but it doesn't eliminate the need for human oversight on tricky cases. I'm concerned about the cost of that wasted time and the potential risk if an anomaly slips through.

Simulated interest: 40/100