Methodology
Authority built for AI answers.
Recala is not a faster agency or a generic writing tool. It is a per-brand authority engine: software that turns private knowledge, live research, grounded writing, verification, and scoring into a compounding public corpus.
A model can only repeat what is already on the web. We start somewhere else: with the data your business already owns. We turn it into original, dated, sourced findings an answer engine can verify and quote, and that build a real advantage for your brand. The findings are public and citable; the data behind them stays yours.
From rankings to citations
Search is moving into AI answers, and a growing share of readers never click through. Being found now depends on whether an answer engine can verify what you published and quote it directly. That is what we build for.
The World of Knowledge
Every brand has a private universe of proof: product data, customer stories, internal research, founder opinions, case data, category vocabulary, and documents. Recala structures that material into a source-tagged graph that every article can stand on.
The Recala Loop
- 01
Ingest brand sites, product data, CMS history, PDFs, docs, voice, and author identity.
- 02
Structure that material into a queryable World of Knowledge with chunks, embeddings, entities, and source tags.
- 03
Read the live search battlefield before writing: SERPs, competitor sections, semantic gaps, and source quality.
- 04
Write something new from the brand's own evidence, in the brand's voice.
- 05
Verify claims, links, citations, schema, originality, and publishing state before public release.
- 06
Score authority across GEO, AEO, and SEO, then feed every published article back into the graph.
Research before writing
Before a draft begins, we check the competing pages, the questions readers are actually asking, the quality of the sources in play, and the brand's own knowledge. The research decides what the article needs to cover.
New information, not a rewrite
Before writing, we map what competing pages already say and where they stop. The article is built to add what they're missing, drawn from what the brand can show with its own evidence.
It sounds like your brand
We write in your voice, with your opinions, your audience, and your evidence — published under a named author on your side, not ours.
Everything is checked before release
Figures, facts, citations, internal links, schema, and originality are all verified before a piece goes live. A confident claim with nothing behind it does not publish.
What we believe
How the engine works
Findings come from your own data
We start with the numbers your business already has, and compute an original finding from them: a figure for a specific segment, measured against a public benchmark, with the date it was true. That finding is the kind of original statistic answer engines reward, and it gives your brand an edge a rewritten blog post never will.
We compute the number, then write the sentence
The figures are calculated in a separate step, before any writing happens. The language model only phrases what was already computed; it never invents a number. That single rule removes the most common way AI content goes wrong.
Every number is checked against its source
Before an article publishes, each figure in it is matched back to the data that supports it. A figure that doesn't check out is corrected to what the evidence shows, or the draft is held until it does. We would rather hold a piece than publish a number we can't stand behind.
Your findings are easy to verify
We publish the method, the segment, the sample size, and the date alongside each finding, so a reader — or an answer engine — can see exactly how it was reached and trust it enough to cite it. What stays private is the raw data itself.
Your data is read through one narrow door
When we work from your data, we read it through a single, read-only connection that returns only summaries — never individual records, and only when a group is large enough that no one customer can be identified. Your raw records stay with you.
We take a position, and we keep it current
We don't write "it depends." Each piece says who an option is right for, on evidence, and where it falls short. Every finding carries the date it was measured, and we update it as the numbers change, so the article stays current instead of going stale.
How we hold ourselves to this
We measure our own work the same way we'd measure anyone's: does an article say something new, does every number check out, and would a person believe a human wrote it. We track those scores on every piece, hold the ones that fall short, and only publish what clears the bar. The standard is the product, and we'd rather show you the standard than a number we can't back.
Each article makes the next one better
What we publish becomes part of what the next piece is built on, so your library gets stronger as it grows. That's the difference between churning out volume and building authority that lasts.