Thanks for the feedback! From what we've seen it's actually the other way around - once it gets a sense of where this information lives the latter stages of data collection go quicker, especially since it's able to deploy search agents in parallel to get information and doesn't need to do the manual work as much anymore. Having said that, it does sometimes forget to do that, and although we've added the critic agent to remind it to do that it can be inconsistent but usually if you step in and ask it to deploy agents in parallel that fixes it.

We use Gemini 2.5 Flash which is already pretty cheap, so inference costs are actually not as high as they would seem given the number of steps. Our architecture allows for small models like that to operate well enough, and we think those kinds of models will only get cheaper.

Having said all that, we are working on improving latency and allowing for more parallelization wherever possible and hope to include that in future versions, especially for enrichment. We do think that one of the weaknesses of the product is for mass collection - it's better at finding medium sized datasets from siloed sources and less good at getting large comprehensive datasets, but we're also considering approaches that incorporate more traditional scraping tactics for finding these large datasets.