Methodology & Glossary
A programmatic infrastructure log for marketing AI. Every claim is sourced. Unknowns are marked Unknown rather than guessed.
#Methodology
aistacktrack is a structured registry of vendors selling AI to marketing teams. For each vendor we capture a fixed schema — architecture, data posture, agentic level, pricing, integrations, channels, and company facts — so that any two vendors can be compared on equal footing.
Research is a hybrid pipeline. An automated scraper pulls each vendor's site (pricing, security, about, docs). An LLM extracts structured fields against a strict schema with a no-hallucination instruction: missing evidence must yield null, not a guess. Every extracted field is paired with a source URL and an excerpt in our vendor_attributions store.
Editors review enriched profiles before they go live, and we re-scan on a rolling cadence so pricing changes, new compliance certifications, and product repositioning surface within days.
#Sources & attribution
Every field on a vendor profile can be traced to a source. We prioritize, in order:
- Vendor site, docs, and trust/security pages (primary)
- Vendor SOC 2 / ISO / privacy policy artifacts
- Independent analyst reports (Gartner, Forrester)
- Review platforms (G2, TrustRadius, Capterra)
- Reputable press / news
- Community signal (Reddit, HN) — clearly flagged
Each citation carries a confidence rating (high, medium, low) so you can weigh self-reported claims against third-party validation.
#Proprietary scores
Four ownable scores summarize each vendor. All are deterministic functions of the structured fields you can already see — nothing is crowd-sourced, paid-for, or hidden. Recomputed daily. Each score on a vendor profile links back here.
Transparency Index (0–100)
How much of our schema the vendor has actually disclosed, weighted by source quality. Vendor-primary sources (vendor site, docs, privacy policy / DPA) count full credit; third-party sources count 60%; disclosed but unsourced fields count 50%. Highest weights go to pricing transparency, compliance, data governance, integrations, and notable customers.
Reads: 75+ = well-documented; 50–74 = average; under 50 = thin disclosure. This is a disclosure score, not a quality score — a stealth vendor with a great product can score low until they publish.
Agentic Autonomy (0–100)
How real the "agent" claim is. Combines stated autonomy level (copilot / semi-autonomous / fully agentic), breadth of channels acted on, depth of integrations, and whether the vendor charges on outcomes — a signal of confidence in autonomous performance.
Buyer Risk (green / amber / red)
Procurement's first question, scored from compliance certifications, funding stage, company age, team size, and data-handling posture. Green = enterprise-ready signal; amber = viable with diligence; red = early-stage or thin disclosure — proceed with eyes open.
Change Velocity (trailing 90 days)
Count of changelog entries detected by our nightly scanner in the last 90 days — pricing changes, new compliance, repositioning, new integrations. High velocity means the product is moving; zero may mean stable, dormant, or unindexed.
None of these scores are reviews. They measure observable signals about how a vendor presents itself and what we can detect — not whether you should buy.
#How we treat unknowns
Where a vendor does not publish a fact and no third party corroborates it, we render Unknown instead of a plausible-sounding guess. This is deliberate: a marketing director comparing four CDPs should be able to trust that a populated cell is sourced and that a blank cell is genuinely unknown — not a scraping failure dressed up as data.
#Corrections & claims
Vendors can claim their profile to suggest edits — every change still requires a source. Anyone can submit a correction with a link; we re-verify before publishing. Claim a profile →
Glossary
Every term we use across the matrix, vendor pages, and comparisons. Deep-link any definition.
#Architecture terms
- §Pure Wrapper
- Thin layer over a third-party foundation model. No proprietary weights.
- §Fine-Tuned
- Customized/fine-tuned models on top of base providers.
- §Sovereign AI
- Owns the full model stack and inference infrastructure.
- §Copilot
- Suggests; human approves every action.
- §Semi-Autonomous
- Acts within guardrails; human reviews outputs.
- §Fully Agentic
- Plans and executes multi-step workflows end-to-end.
- §Model stack
- The chain of models a vendor uses — foundation model(s), any fine-tuned layers, retrieval, and orchestration. Disclosed when the vendor publishes it.
- §Inference infrastructure
- Where model calls actually execute (vendor-hosted GPUs, a hyperscaler, or your own VPC). Matters for latency, residency, and cost pass-through.
#Data & governance
- §Synthetic Only
- Trains and operates on synthetic data; no PII ingress.
- §Zero-Copy Warehouse
- Runs inside your warehouse. No data leaves your perimeter.
- §External Processing
- Data is sent to vendor infrastructure for processing.
- §PII (Personally Identifiable Information)
- Any data that can identify an individual — emails, names, device IDs, IPs. Drives compliance scope.
- §Zero retention
- Vendor commits in writing that prompts and outputs are not stored or used for training. Verify in the DPA, not the marketing page.
- §Subprocessor
- A third party (often a foundation-model provider) that processes your data on the vendor's behalf. Listed in the subprocessor log.
- §Compliance certifications
- Independent attestations — SOC 2 Type II, ISO 27001, HIPAA, GDPR readiness. We record only certifications with a public artifact.
#Commercial terms
- §Usage-based
- Pay per token, message, generation, or compute unit. Scales with success but harder to forecast.
- §Seat-based
- Pay per active user/seat. Predictable; can penalize broad enablement.
- §Outcome-based
- Pay per resolved ticket, qualified lead, conversion. Aligns incentives; rare and complex to audit.
- §Enterprise-only
- No self-serve tier; pricing is bespoke and contract-driven.
- §Hybrid
- Mix of seats + usage or platform fee + consumption.
- §Pricing transparency: Public
- Numeric pricing published on the website.
- §Pricing transparency: Gated (signup required)
- Pricing visible only after signup or form fill.
- §Pricing transparency: Contact sales
- No public numbers — sales conversation required.
- §Time to value: Self-serve
- Sign up and produce value the same day.
- §Time to value: Weeks to deploy
- Light implementation — integrations, data mapping, a pilot.
- §Time to value: Months (implementation)
- Real implementation project: data work, change management, SOWs.
- §Funding stage
- Most recent disclosed round (Seed, Series A–E, PE-backed, public, bootstrapped). Signal for runway and roadmap risk.
- §Team size band
- Approximate headcount band from LinkedIn or vendor disclosure. Bands avoid false precision.
#Source types
- §Vendor site
- Vendor's marketing site. Primary for product claims; treat positioning critically.
- §Privacy policy
- Vendor's published privacy policy or DPA. Authoritative for data handling.
- §Vendor docs
- Vendor's developer documentation. Authoritative for APIs, models, and limits.
- §G2
- G2 reviews and category pages. User-reported feature presence and sentiment.
- §Capterra
- Capterra reviews. Similar to G2; weight against vendor self-claims.
- §TrustRadius
- TrustRadius — typically deeper, enterprise-skewed reviews.
- §News / press
- Press articles, funding announcements, product launches.
- Reddit threads — useful signal but unverified; flagged as community.
- §Analyst (Gartner/Forrester)
- Gartner, Forrester, IDC reports and Magic Quadrant placement.
- §Community
- Other community sources — HN, Slack, Discord, blogs.
- §Other
- Anything that doesn't fit the above; URL always provided.
- §Confidence (high / medium / low)
high: primary source, unambiguous.medium: secondary or partial source.low: inferred or community signal — treat as a lead, not a fact.
#Marketing fit
- §Channels
- Where the tool operates — email, SMS, paid social, paid search, web, in-product, CRM, sales outbound, organic content.
- §Use cases
- The marketing jobs the tool is built for: lifecycle, lead scoring, content generation, ad creative, attribution, SEO, RevOps, etc.
- §Integrations
- Native connections to the surrounding stack (warehouses, CDPs, CRMs, ad platforms, analytics). Native > Zapier > CSV.
- §Output types
- What the tool produces — copy, images, video, audio, structured data, decisions/actions, or analyses.
- §Notable customers
- Publicly disclosed logos (case studies or vendor site). We do not list customers from sales decks or rumor.
- §Headquarters
- Primary legal/operational HQ. Useful as a first-pass proxy for data residency posture; always confirm in the DPA.
Definitions and vendor facts evolve. Send a correction with a source and we'll update.