/ FEEDS · EVIDENCE FOR USEFUL WORK
Make the
next move
less vague.
Field guides, comparisons, operating frameworks, and tools for teams deciding where AI belongs, how discovery works, and what should happen next.
Only distinct, evidence-ready pages move from the roadmap to the live feed.
/ THREE RESEARCH DESKS
Different questions.
One standard of proof.
Browse by the decision you are trying to make. Every desk uses the same answer-first structure, visible sources, limitations, and practical next step.
Market & Buyer Intelligence
Fair comparisons, industry playbooks, and decision frameworks for choosing where—and how—to move.
Enter the deskSearch & Discovery
Evidence-led guidance for being found, understood, measured, and cited across search and AI-assisted discovery.
Enter the deskApplied Systems & Tools
Workflow patterns, readiness methods, practical tools, and operating guidance for useful applied AI.
Enter the desk/ PUBLISHED LIBRARY
Start with the
useful question.
39 field notes use the same standard: a direct answer, visible evidence, a decision surface, stated limitations, and one relevant route into dotSuper.
Gushwork and dotSuper: Two Operating Models for AI-Search Inbound
A neutral comparison of Gushwork's broad AI growth system and dotSuper's evidence-controlled Inbound Engine, including fit, limitations, and buying questions.
Read the field noteManufacturing AI: How to Choose the First Use Case Without Buying a Demo
A practical method for selecting a first manufacturing AI workflow using operational value, data fitness, human oversight, integration, and a real stop rule.
Read the field noteBuild, Buy, Configure, or Partner? A 12-Factor AI Decision Scorecard
A vendor-neutral scorecard for choosing among packaged AI software, configurable platforms, custom builds, and delivery partners using evidence and whole-life trade-offs.
Read the field noteThe Programmatic SEO Risk Gate: When a Page Deserves to Exist
A practical gate for deciding which scalable pages should be published, indexed, merged, noindexed, redirected, or rejected before a template turns into search spam.
Read the field noteDesign Pages That AI Systems Can Cite Accurately
A practical guide to making expert pages easier for people, search engines, and AI answer systems to interpret, verify, retrieve, and cite without relying on hidden prompts or citation hacks.
Read the field noteThe GA4 Measurement Plan for a B2B Lead-Generation Website
A practical GA4 blueprint for measuring the path from content and service interest through form activity, lead generation, qualification, and closed outcomes without treating every click as a conversion.
Read the field noteAutomation, Copilot, Workflow, or Agent? Choose the Simplest System That Fits
A practical decision guide for choosing rules automation, an AI copilot, a defined AI workflow, or an agent based on uncertainty, consequence, and control.
Read the field noteAI Readiness Is a Workflow Property, Not a Company Personality Test
Assess AI readiness around a named workflow, decision, and outcome across problem, process, data, technology, people, governance, and measurement.
Read the field noteThe dotSuper 0→1 Method: From a Real Constraint to the First Measurable System
dotSuper’s evidence-led method for moving from a real workflow constraint to the smallest useful intervention, a measurable result, and a defensible next decision.
Read the field noteAI Consultancy, Product Studio, or Systems Integrator? Choose by the Work That Must Change
A practical comparison of three common AI partner models, the work each is built to do, and the evidence a buyer should request before selecting one.
Read the field noteThe Industrial AI Vendor Evaluation Checklist: 18 Questions Before a Pilot
A buyer-side checklist for evaluating industrial AI vendors across workflow fit, data, reliability, integration, human oversight, security, and transfer.
Read the field noteAI Proof of Concept vs Production Pilot: Know Which Evidence You Are Buying
A clear distinction between technical feasibility and operational proof, with gates for deciding what to fund and what each stage must produce.
Read the field noteAI Readiness Assessment vs Strategy Workshop: What Should Leave the Room?
A buyer’s guide to distinguishing a useful readiness assessment from an inspiration session, transformation roadmap, or generic AI workshop.
Read the field noteDigital Twin, AI Assistant, or Workflow Automation? Start With the Decision Loop
A fit guide for three different industrial system patterns and the operational problems each is equipped to solve.
Read the field noteHow to Evaluate a RAG Vendor: The SME Buyer Scorecard
A practical scorecard for retrieval quality, source control, permissions, evaluation, integration, and ongoing ownership in an AI knowledge system.
Read the field noteNIST AI RMF vs ISO/IEC 42001: A Practical Guide for Operational Teams
A plain-language comparison of two influential AI governance frameworks and how a smaller organisation can use them without turning governance into paperwork.
Read the field noteEU AI Act Checklist for Manufacturers Buying or Deploying AI
A non-legal operational checklist for inventorying AI uses, clarifying roles, risk classification, transparency, documentation, and supplier evidence.
Read the field noteThe Hidden Costs of Industrial AI: A Total-Cost Model Beyond the Licence
A practical model for estimating data preparation, integration, evaluation, change, monitoring, exception handling, security, and ownership costs.
Read the field noteHuman-Machine Collaboration in Operations: Define Authority Before Automation
A practical operating model for deciding what AI prepares, what people judge, how exceptions move, and who remains accountable.
Read the field noteGEO vs SEO: What Is Actually Different—and What Still Matters
A grounded comparison of generative-engine optimisation and established SEO, separating useful changes in discovery behaviour from new labels and myths.
Read the field noteChatGPT Search Visibility Checklist for B2B Websites
A practical checklist covering OAI-SearchBot access, indexable evidence, answer-first pages, entity consistency, analytics, and honest measurement.
Read the field noteOAI-SearchBot vs GPTBot: Separate Search Discovery From Training Controls
A plain-language guide to OpenAI crawler controls, noindex behaviour, and the decisions publishers should record before changing robots.txt.
Read the field noteHow to Track AI Referrals in GA4 Without Inventing an “AI Visibility” Metric
A measurement design for ChatGPT and other AI referral traffic, landing-page quality, qualified actions, attribution limits, and decision-ready reporting.
Read the field noteThe Entity Consistency Audit: Make Company Facts Easier to Verify
A practical audit for aligning organisation, product, service, location, leadership, and proof across public pages and structured data.
Read the field noteHow to Build a Comparison Page Worth Citing
A fair-comparison framework built around source parity, decision criteria, fit, limitations, current evidence, and an explicit method.
Read the field noteThe B2B Product Page Blueprint for Search and AI-Assisted Discovery
An answer-first product-page architecture covering audience, problem, system, evidence, exclusions, implementation, commercial next step, and machine-readable facts.
Read the field noteStructured Data for AI Visibility: What It Can Do—and What It Cannot
A practical explanation of JSON-LD, visible-content parity, rich-result eligibility, and the myth of a special schema for AI answers.
Read the field noteIndexNow, XML Sitemaps, and Freshness: A Publishing Workflow for AI-Era Search
A clear division of labour between canonical sitemaps, change notifications, crawl access, update signals, and real editorial review.
Read the field noteThe Evidence Refresh Workflow: Stop B2B Content From Quietly Becoming Wrong
A maintenance system for reviewing claims, sources, dates, product facts, regulations, links, and decision guidance based on risk and change triggers.
Read the field noteThe RAG Readiness Checklist: Fix the Knowledge Operation Before the Chatbot
A pre-build assessment for source ownership, permissions, document quality, update paths, question coverage, evaluation, and accountable use.
Read the field noteHuman in the Loop Is Not a Control Until the Human Can Actually Intervene
A practical design guide for review authority, evidence, time, competence, escalation, override, and learning in human-AI workflows.
Read the field noteAI Agent vs Deterministic Automation: Use Agency Only Where It Earns Its Risk
A decision framework for choosing fixed rules, model-assisted steps, or bounded agents based on ambiguity, action space, reversibility, and consequence.
Read the field noteThe LLM Evaluation Scorecard: Test the Business Workflow, Not the Demo Prompt
A practical evaluation design for representative cases, groundedness, task success, safety, latency, cost, review burden, and release decisions.
Read the field notePrompt Injection Controls for RAG and AI Agents: Design for Compromise
A practical security playbook for separating instructions from data, constraining tools, validating outputs, protecting secrets, and containing failures.
Read the field noteAI Knowledge Base Architecture: Preserve the Source of Truth
A practical architecture for source systems, ingestion, provenance, permissions, retrieval, citations, updates, deletion, and human ownership.
Read the field noteHow to Pilot AI in a Document Workflow Without Automating the Wrong Decision
A bounded pilot blueprint for extraction, classification, comparison, drafting, review, exception handling, and measurable operational value.
Read the field noteThe AI Production Handoff Checklist: Leave Capability, Not Dependency
A practical handoff checklist for ownership, runbooks, evaluation, monitoring, access, suppliers, cost, change, incidents, training, and retirement.
Read the field noteThe AI Incident Response Runbook: Detect, Contain, Decide, Learn
A practical runbook for harmful outputs, data exposure, tool misuse, drift, cost spikes, service failure, and unreliable knowledge.
Read the field noteAI for Manufacturing Quality: Choose the First Use Case by Evidence and Consequence
A selection playbook for inspection, document review, non-conformance triage, root-cause support, and knowledge retrieval in quality operations.
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