AI Personal Research and Recommendations
Products, schools, contractors, services. Researched, compared, recommended.
Alice is your AI Personal Assistant on Sistava.
Choosing a school, picking a contractor, deciding which mattress to buy: each one is a research project nobody wants to run. Alice runs them. Reviews aggregated, prices tracked, trade-offs spelled out, recommendation included.
For local services she pulls neighborhood reviews and verified credentials. For products she compares specs and watches the price. For schools she pulls ratings and family-shared notes.
Two tools do the actual work, and both are on every employee from the day you hire them. Web search finds what is current rather than what she happens to know, and the scraper opens the page and pulls the real text out of it, stripped of ads and navigation, so a spec table or a review thread is read rather than half remembered.
What comes back is a brief, not a pile of links and not a promise to do it later. The answer first, then what it means, then the detail underneath for the parts you want to dig into. Every claim carries the source it came from, which is the difference between a recommendation you can act on and one you have to re-check from scratch.
The judgement stays yours on the things research cannot settle. Whether the school is right for your child, whether you trust the contractor after meeting him, whether the cheaper option is worth the compromise. She narrows twenty options to three and tells you why, and the last step is still a human one.
What you get
- Reviews aggregated across multiple sources.
- Price tracked over time so you know if "sale" is real.
- Trade-offs spelled out in plain language.
- Local options listed first when location matters.
How it works
- Frames what you need: Alice clarifies budget, must-haves, and timeline before researching.
- Compares options across reviews: Multiple sources cross-checked. Outlier reviews flagged. Verified credentials checked for services.
- Sends a clear recommendation: Top pick with reasoning. Two alternatives if the top pick is borderline.
- Cites everything: Each claim carries the page it came from, so checking her work is a click rather than a repeat of the research.
- Hands it over as a document when it is big: A short answer stays in chat. A comparison worth keeping becomes a real file in her Drive that you can download, share, and come back to.
FAQ
Can she help with school research?
Yes. Public ratings, demographics, programs, parent reviews, and prior visit notes from your network when shared.
What about big purchases?
Spec comparison, price history, return policy, warranty terms, and reviews. Includes alerts if the price drops.
Does she check reviews for fakes?
She cross-checks across sources and flags suspiciously skewed review patterns. Verified-purchase reviews weighted higher.
Is she reading the real pages or guessing?
Reading. Search finds the candidates and the scraper pulls the actual page content, so what she reports is what the page says today rather than what she remembers about it. Where she cannot verify something, the brief says so instead of filling the gap.
How specific do I need to be?
Give her the constraint that actually decides it: the budget, the deal breaker, the deadline. She confirms those before she starts rather than after, because a brief built on the wrong assumption is worse than no brief.
Can she keep watching after the answer?
Yes. Put it on a routine and the price check, the availability check, or the new-review check runs on the cycle you choose, with each run logged. That turns a one-off decision into something that tells you when it changes.