How we audit: the RXed AI Periodic Table v1.0
Every AI tool we audit is scored against the same 20 elements, organized like chemistry organizes the elements: 4 rows (from primitives to emerging capabilities) × 5 families. The concept builds on the AI Periodic Table by IBM Technology (Martin Keen) — credit where due. RXed extended it: we filled the three cells IBM left open and made one swap, so the table is complete and audit-ready.
The rows
- R1 — Primitives
- R2 — Compositions
- R3 — Deployment
- R4 — Emerging
What RXed changed vs IBM's original — and why
How scoring works — two honest axes, deterministic, always
Every audit shows the same two axes, side by side, everywhere:
How many of the 20 elements are in the tool's stated scope. Each element gets a whole-number score 0–10 with a written reason, or N/A with a written reason when the capability is outside what the tool claims to do. An N/A is honest, never a penalty — but "missing but expected" is a low score, not an N/A.
Audits from before 08/2026 scored in half-points; displays round half-up and the exact value is always in the tooltip.
The plain mean of the covered elements only, computed by the site generator, never set by hand, identical for every tool. Displayed as a whole number; the exact mean is always one hover away.
Why two axes instead of one score? A single number quietly punishes ambition: a platform attempting 17 elements risks a lower mean than a narrow tool polishing 7. Shown as Scope × Quality, a brilliant narrow tool isn't punished and a shallow platform can't hide — when a tool covers more and scores higher, you see it at a glance.
Tiers
- Frontier lab
- Enterprise platform
- SMB tool
- Specialist
- Open source
All 20 elements
Instruction design surface: system prompts, prompt tooling, templates, caching.
Scored when: Does the tool let users or developers shape instructions to the model?
Numerical representations of meaning powering semantic search and memory.
Scored when: Does the vendor ship or expose an embedding capability (API, semantic search feature)?
The context window and everything engineered into it: window size, file/knowledge attachment, context caching, context management. RXed addition v1.0.
Scored when: Does the tool manage what the model sees (files, history, knowledge, window)?
Logging of runs, decision paths and data lineage: run logs, trace trees, audit trails, cost attribution. RXed addition v1.0.
Scored when: Can an operator inspect what the AI did and why (logs, traces, history)?
The base language model(s): quality, choice, freshness. Third-party model access scores on quality-of-access, not ownership.
Scored when: Is an LLM at the core of the product?
The model invoking tools and APIs: breadth, reliability, schema strictness.
Scored when: Can the AI call external tools, actions or APIs?
Storage optimized for semantic search over embeddings.
Scored when: Does the vendor ship or host a vector/semantic store users can rely on?
Retrieval-augmented generation: grounding answers in user or org documents.
Scored when: Can the tool ground outputs in the user's own data?
Runtime safety: content filters, PII detection, policy enforcement, human-in-the-loop gates.
Scored when: Does the tool ship runtime safety or approval controls?
Native handling of images, audio, video alongside text.
Scored when: Does the product process or produce non-text modalities natively?
Think-act-observe loops working toward goals: autonomy, reliability, integration depth.
Scored when: Does the product ship agentic behavior (multi-step autonomous work)?
Adapting models on user/domain data: SFT, preference tuning, distillation.
Scored when: Does the vendor offer model adaptation to customers?
The scaffolding for building and running AI systems: SDKs, builders, orchestration logic, hooks. Renamed from IBM's Framework in v1.0 — Agent = Model + Harness.
Scored when: Does the vendor ship a builder, SDK or harness for composing AI systems?
Measuring quality: eval suites, benchmarks, regression testing, red-teaming (folded in from IBM's Rt in v1.0).
Scored when: Can users measure or test output quality systematically?
Efficient distilled/small model options: cost tiers, edge deployment.
Scored when: Does the vendor offer smaller/cheaper model tiers as a product choice?
Multiple specialized agents coordinating: handoffs, delegation, debate.
Scored when: Does the product support multiple agents working together?
AI-generated training data as a product capability.
Scored when: Does the vendor ship synthetic data generation for customers?
Open interoperability standards: MCP (agent-to-tool), A2A (agent-to-agent). RXed addition v1.0 — fills IBM's declared empty cell.
Scored when: Does the tool speak open agent protocols (MCP server/client, A2A)?
Understanding why the model does what it does: feature attribution, transparency reports exposed to users.
Scored when: Does the vendor expose interpretability surfaces to customers (not just research papers)?
Reasoning models with built-in chain-of-thought / test-time compute.
Scored when: Does the product offer reasoning-class models?
Table changelog
- v1.0 (2026-07-05): Initial frozen version. IBM's 17 elements + Cx Context (R1/G3), Tr Tracing (R1/G4), Pc Protocols (R4/G3). Swap: Rt Red-teaming folded into Ev Evaluations (R3/G4). Fw renamed Frameworks & Harnesses.
Audits are independent tool reviews, fully sourced on each passport, never legal or compliance advice. No affiliate links, ever.