Trust Score Methodology

How We Calculate Trust Scores

Every AI tool on Intelloro receives a Trust Score from 0 to 100 based on four weighted pillars. This score is designed to give you an objective, data-driven assessment of each tool's reliability, transparency, and user satisfaction.

Our methodology combines market-adoption signals (named notable customers), company and funding signals, operational-reliability signals (measured uptime, status page, data residency, incident record), independent verification, and self-reported compliance signals to create a comprehensive trust profile. Third-party review-platform star ratings (Capterra, GetApp, and others) are never fed into the Trust Score and are not publicly displayed anywhere; only npm and PyPI package-download counts are shown.

The 4 Trust Pillars

Each pillar contributes a specific weight to the total score of 100 points. An item is scored only on the signals that apply to it — a missing signal is never counted against it. Items with very little verifiable public data are marked "Insufficient data" (shown as "—") rather than given a misleadingly low score.

Trust Tiers

Based on the total score, each tool is assigned a Trust Tier that gives you a quick visual indicator of overall trustworthiness.

Beyond Trust Scores, every tool and agent receives Dimension Scores and Task Scores, each rated 1–10. Tools are scored on 9 dimensions (6 universal + 3 AI-specific). Agents are scored on 12 dimensions (the same 9 plus 3 agent-specific dimensions). Task Scores are drawn from per-category rubrics described below.

The Scoring Dimensions (9 for Tools, 12 for Agents)

Grouped into three tiers: Universal SaaS (6 dims, all products), AI-Specific (3 dims, all AI products), and Agent-Only(3 dims, agents only).

Task Scores rate how well a tool or agent performs at specific tasks within its category. Intelloro maintains a per-category set of scoring rubrics — one per vertical category. Each rubric defines 10 task capabilities that are the prime evaluation axes for that vertical (e.g., a Search & Discovery rubric scores search accuracy, semantic understanding, real-time indexing, filter refinement, source attribution,and 5 more). Each task is scored 1–10 against a 4-band anchor (1–3 / 4–6 / 7–8 / 9–10) — modeled after per-category feature-rating patterns used across the software-review industry.

Rubric selection is deterministic: an item's category_id maps one-to-one to its scoring rubric (e.g., data-analytics → the Data Analysis & BI rubric), so every item in a category is graded on the same 10 canonical tasks for fair, apples-to-apples comparison. Rubric definitions are locked in code and protected against drift by automated audit checks.

Every tool and agent is scored by an AI-assisted pipeline that reads the vendor's own website (pricing, features, documentation, security pages), cross-references the data with the independent verification sources described above, and grades each dimension and task against the canonical 4-band rubric anchor. Scores are not vendor-supplied — they are independently generated from public evidence by our own model, reviewed by a human, and they update whenever the underlying data changes.

The rubrics are the fixed comparison targets, but the free-text task labels our extraction pipeline produces (and admins enter) vary in wording. We normalize them through a controlled alias map — a curated set of aliases — so synonyms like “content writing” and “copywriting” resolve to one canonical rubric, keeping every item comparable. Aliases are grounded in recognized, independent standards rather than vendor marketing: W3C WCAG 2.2 for accessibility tasks, the NIST AI Risk Management Framework for governance, OpenTelemetry semantic conventions for observability, and the DAIR.AI taxonomy for prompt engineering — supported by public reference encyclopedias for general software categories. Every alias must map to an existing rubric: a continuous-integration drift gate rejects any that points to a non-existent category, keeping the map and the rubric set in lockstep, and new aliases require a cited source before they are merged.

The 60-rubric architecture aligns with the per-software-category feature-rating patterns used across the software-review industry. Forrester Wave and Gartner Magic Quadrant use analyst-determined criteria per evaluation, which is more nuanced but does not scale beyond ~50 vendors per cycle. Intelloro's LLM-judged fixed-rubric approach is optimized for breadth — every item in every category is graded on the same canonical task set, keeping comparisons consistent across thousands of items.

Every data point on Intelloro is tagged with its source so you know where the information comes from.

Confidence Levels

Each score and data point is assigned a confidence level indicating how reliably it has been verified.

Intelloro recognizes exceptional AI tools and agents through editorial awards. These awards highlight products that stand out in quality, data completeness, and user value.

Every listing carries a verification-level badge (Fully Verified, Largely Verified, Partially Verified, Limited Data, or Unverified) reflecting how many of its data points we have completed via our extraction + independent verification pass. The badge describes the state of our data about a tool, not a rating of the tool itself.

For vendors: if your listing carries a lower verification level, you can request faster verification via the claim flow. The badge updates automatically after each extraction + verification pass. Industry pattern reference: LinkedIn Profile Strength (5 levels), Crunchbase Profile Strength, Wikipedia Content Assessment letter grades.

Verified listings on Intelloro are cross-referenced against a dozen-plus independent external sources. Not every listing has completed this process yet — each listing shows a verification-level badge (see above) indicating how much of its data has been cross-checked. This cross-referencing brings accuracy beyond what vendor websites alone can provide.

Every classification on Intelloro — task categories, supported countries, supported languages, data residency regions — runs through a controlled vocabulary and a multi-layer normalizer before reaching a tool or agent listing. The same rules apply to every entry on the platform: claimed, unclaimed, free, paid, sponsored or organic. Vendors cannot purchase a different vocabulary, override a normalizer rule, or skip the drift-detection cycle. The pipeline is summarized below.

The same controlled vocabulary, normalizers, and audit gates apply to every listing — there is no paid pathway to bypass them.

AI Origin Classification — How We Label AI-Native vs AI-Layer

For some tools and agents, Intelloro publishes an AI Origin label. This is Intelloro's editorial opinion — a transparent, subjective classification based on publicly available information, not a statement of fact and not a claim that any vendor misrepresents its technology. We apply one simple, disclosed test.

This label reflects Intelloro's opinion from public information and may be updated as products evolve. Vendors who believe a classification is inaccurate can request a review via our contact page.

Frequently Asked Questions