Agency-first, by design.
NarraLoom exists because AI search visibility became sellable before it became deliverable.
Agencies were being asked "do we show up in AI answers?" — and behind the pitch there was no fulfillment system: tools that diagnose, and people-heavy processes that don't scale.
We built the missing layer: white-label fulfillment that turns buyer-question gaps into work an agency can deliver — branded audits, researched articles with metadata, supported publishing and technical follow-through, Search Console inspection, and client-ready reporting.
Two commitments shape everything.
Your agency owns the client relationship and retail pricing. NarraLoom powers the fulfillment layer behind the service, while wholesale terms stay private.
We report what AI systems actually did, and we never guarantee indexing, rankings, AI citations, traffic, leads, or revenue — no vendor controls those.
Founder: Andrew Oldfield
Andrew Oldfield founded NarraLoom. His background includes running Google Ads for home remodeling companies, where he saw how gaps in website content and SEO made lead generation harder.
That experience informs NarraLoom’s focus on practical content fulfillment for agencies: researched answers, reviewable drafts and reporting tied to captured evidence.
Our editorial standard
NarraLoom is the credited organization behind this article library. These standards guide content preparation, review and revision.
1. Ground the topic
Start from a real buyer question. Retain its search-demand source when a question is presented as demand-backed, and define the audience, engine and scope.
2. Check claims and sources
Support material factual claims with primary sources where available. Label observations, examples and unknowns. AI assistance is not evidence.
3. Review before release
Check accuracy, originality, brand voice and useful links before publishing. Preserve the original publication date and record substantive revisions separately.
4. Credit and measure honestly
Credit the organization or person responsible. Name a reviewer only when that review occurred. Record page delivery, brand mentions, linked citations and traffic as separate measures.
Our guide to what AI search engines look for applies these distinctions.
· NarraLoom-owned case
A documented editorial review
Our own article library gave us a concrete place to apply this standard. Some older articles made overly broad statements about AI search, and nine other articles needed useful contextual links in their prose.
6 articles revised
AI-search explanations were corrected to distinguish documented guidance from observations and unknowns, with primary-source links added.
18 links across 9 articles
Useful contextual links were added while preserving the existing wording, article URLs, original publication dates and Related Resources.
Article metadata and sitemap revision dates were checked against the recorded changes. The linked evidence record lists the pages, dates, sources and added links so the work can be inspected.
Read two examples: How AI Search Finds and Cites Business Content and How Agencies Can Evaluate Content Performance Without Overclaiming Search Outcomes.
Inspect the dated editorial evidence record (JSON)
This case documents completed editorial work. AI-engine responses, index coverage, rankings, traffic, leads and revenue were not measured in this review, so the record does not attribute changes in those outcomes to the edits.
Keep your own question-evidence record
Record the real question and its demand source, the exact page URL, the engine or product, the UTC observation time and the captured response. Track a brand mention separately from a linked citation. An answer on a website does not establish that an AI system retrieved or cited it; mark missing observations as unknown or not measured.
Download the blank question-evidence worksheet (CSV)
The worksheet is a blank template. It contains no measured buyer-question or AI-test data.
NarraLoom Inc. is a Delaware corporation.
Talk to us: hello@narraloom.com.