Independent · Evidence-based · Built for AI

Choose software on evidence, not reviews.

Tell us what you are trying to choose and we order the field by exactly that, with the source and retrieval date on every fact. No star ratings, no solicited reviews, and no vendor can pay to rank.

Say what the job is and what you weigh most. The more specific the ask, the more useful the ordering.

agent session grounded in vioscale

user ▸ best framework for a new React SaaS, and why?

assistant ▸ Next.js is the strongest choice. vioscaleAI scores it 79.7/100 on independent signals, led by package downloads of 32.4M weekly installs, at high confidence.[1]

[1] cited source
www.vioscale.ai/software/nextjs
retrieved 2026-09-14 · confidence high · no reviews, no vendor money
How the evaluation works

You bring the criteria. The evidence does the ranking.

An evaluation is not a search. Describe what you are choosing and the field is ordered against your requirements, not against a generic best-of list.

  1. 01

    Describe the job

    Say what you are choosing, the constraints you cannot bend, and what you weigh most: team size, budget, hosting, compliance.

  2. 02

    The model sets the weights

    It reads your requirements and turns them into weights over the evidence. It never invents a fact and never picks the winner.

  3. 03

    The evidence does the ranking

    Every product is ordered on the same dated, sourced facts, weighted your way, and you can see why each one sits where it does.

How much does it cost to evaluate software on vioscaleAI?

Starting an evaluation is free and needs no account. You describe what you are trying to choose, and the field is ordered against your criteria with a source and a retrieval date on every fact it uses.

Does the AI decide which software is best?

No. The language model reads your requirements and turns them into weights; the ranking itself comes from the same independent, provenanced facts the rest of the site is built on. The model never invents a fact and never picks a winner.

Can a vendor pay to rank higher in an evaluation?

No. Nothing in an evaluation or a ranking can be bought. A vendor can correct a fact about their own product with evidence, and that is the extent of their influence.

Explore the core

Find the tool. Read the evidence.

Search the whole knowledge core, or browse by group to see how a category ranks its tools on independent signals.

Browse by group
Why now

Software discovery is being rewritten by machines.

Buyers no longer scroll a review site; they ask an assistant. But assistants are being handed the same data humans were: solicited star-ratings on sites funded by the vendors they rank. That model was gameable before AI. Now it's a liability.

4.5M

fake reviews Trustpilot removed in 2024

source · corporate.trustpilot.com
Oct 2024

FTC ban on fake & AI-generated reviews took effect

source · ftc.gov
200M+

buyers on the newly-merged G2 + Capterra network

source · company.g2.com
The approach

You can't out-review the incumbents. You can out-trust them.

01

Every fact carries provenance

Source, retrieval date, and a confidence band travel with every value, so an agent can cite the evidence, not just the claim.

02

No vendor money touches the data

Vendors can claim and correct their profile, but they can never buy rank. Independence is the one thing the incumbents can't copy.

03

Structured before prose

The canonical form of a comparison is data. Prose, JSON-LD, and markdown are all projections of it, never the reverse.

04

Revealed preference, not reviews

We score real adoption and developer activity: downloads, release cadence, security posture. Vanity metrics like stars count for little.

The evidence

See the working, not a five-star average.

Every score decomposes into weighted, independently-sourced signals with an explicit confidence band. Here is a real profile from the knowledge core, rendered exactly as an agent would read it.

Next.jsWeb Frameworks

A full-stack React framework for building web applications with integrated routing, rendering, and caching.

80
vioscaleAI score
79.7/100
high · 77%

A confidence-weighted blend of independent signals: real adoption and developer activity, not stars or solicited reviews. Every input is dated and sourced.

See the full profile →
Signal breakdown · weighted contribution
package_downloads97.6 · w0.173
dev_activity63.1 · w0.12
community_qa_activity92.3 · w0.08
dependent_projects91.4 · w0.08
capabilities81.2 · w0.083
release_cadence95 · w0.067
repo_stars97.2 · w0.033
security_score61 · w0.053
integrations8.7 · w0.053
security_posture0 · w0.087 · no data
Every fact carries its evidence
adoption.package_downloads_weekly
32413652
source · 2026-09-10 · 85%
adoption.github_stars
142221
source · 2026-09-10 · 90%
adoption.stackoverflow_questions
148000
source · 2026-07-30 · 75%
Purpose-built for AIs

One knowledge base. Every machine format.

The same canonical entity, dereferenceable however your agent speaks: JSON, GraphQL, clean markdown, or a single MCP tool call. No scraping, no HTML parsing, and a citation in every response.

request
curl -H "Authorization: Bearer $VIOSCALE_KEY" \
  https://www.vioscale.ai/api/v1/software/nextjs
response
{
  "data": {
    "name": "Next.js",
    "score": { "composite": 76, "confidenceBand": "medium" },
    "facts": {
      "adoption": [{
        "attribute": "adoption.package_downloads_weekly",
        "value": 8100000,
        "provenance": {
          "source": "https://nextjs.com",
          "retrievedAt": "2026-07-06",
          "confidence": 0.8
        }
      }]
    }
  },
  "meta": { "license": "CC-BY-4.0", "source": "https://www.vioscale.ai" }
}
For agent builders

One call instead of a scavenger hunt.

Researching software with an agent today means a loop of fetches over raw HTML: pricing pages, docs, changelogs, trust centres. Most of it is marketing noise, some of it a dead homepage that returns nothing. It burns tokens, breaks the moment a vendor changes their UI, and still hands back an answer you cannot cite. vioscaleAI answers the same question in a single call, as structured facts with a source on every value.

Do it yourself
Web + LLM scrape
  • · 15 to 20 tool calls per comparison
  • · ~1M tokens of raw pages summarised
  • · Breaks on every vendor UI change
  • · No provenance, nothing to cite
~$1.50 to $4 / comparison, still incomplete
With vioscaleAI
One MCP tool call
  • A single structured, cited payload
  • Source, date, and confidence on every fact
  • Stable schema, nothing to maintain
  • Re-rank by intent, which no web search gives you
Free / call, buyers and agents alike

We do not charge for this. Buyers and agents read the index for free, and we intend to keep it that way: the open layer is what earns the citations. Bulk export, full history and a redistribution licence are the separate commercial arrangement, for products built on top of the corpus.

Live knowledge core

Ranked on signals, not stars.

The vision

An independent trust layer for the agentic web.

As software discovery moves from clicks to answers, the winner isn't the biggest pile of reviews; it's the source AI reaches for and trusts. We're building that source, one verifiable fact at a time.

Are you the vendor? Claim your profile and control how you're represented.

We already index, score, and generate your profile. Claim it in the portal to correct, refine, and keep it current. Control of your own representation is what's on offer, never a change to your rank.