Documents decay, structured knowledge compounds.
Every organization sits on years of accumulated knowledge - reports, research, internal documents - buried in folders and PDFs. Regnor Knowledge turns that archive into a structured, interlinked knowledge base your team can actually use. Atomic fragments. Cross-linked, source-tiered pages. A living wiki that compounds with every document added - fact-checked and continuously verified, so it stays trustworthy as it grows. Your team runs it. Your data stays yours.
One system, built for you, run by you, owned entirely by you.
Every organization accumulates knowledge - in reports, research, internal documents. Most of it stays buried. We don’t process your documents for you. We design a structured extraction system - templates, prompts, wiki architecture, and an integrity layer that keeps it auditable and self-checking - calibrated to your domain, then hand it over. Your team operates it internally. No data shared. No vendor lock-in. No black box.
- Custom extraction templates
- Tested prompts with merging logic
- Wiki architecture & folder structure
- Naming conventions & taxonomy
- An integrity layer: source-tiering, a skeptic pass & regression checks
- Git version control — full history, rollback, off-machine backup
- A runbook your team follows
to living knowledge, we
architect the full
stack of structured
intelligence.
Teams that accumulate faster than they retrieve.
If your organization produces or consumes large volumes of documents - and your analysts spend more time searching than synthesizing - this system is built for you. For regulated domains, claims cite a primary authority and anecdotal sources are quarantined, never stated as fact.
Research & Intelligence Teams
Biotech, pharma, materials science, energy. You read 50+ papers and reports a year and need to track entities, products, and markets across all of them.
Strategy & Consulting Firms
You produce and consume massive amounts of research per engagement. Knowledge from past projects rarely compounds into future ones.
Investment & Due Diligence Teams
VC, PE, family offices. You review deal flow, market maps, and competitive landscapes. Every memo should feed a living knowledge base.
Policy & Regulatory Teams
Government, think tanks, compliance-heavy industries. You track evolving regulations, entities, and precedents across hundreds of documents.
Templates, pipelines, and architecture for compounding knowledge.
We design structured extraction systems calibrated to your domain - so every document your team processes becomes a permanent, cross-linked, source-tiered fragment in a knowledge base that verifies itself and grows smarter over time.
Structure,
not summaries
Custom templates for entities, markets, concepts, products, and sources. Each one defines exactly what to extract - consistent fields, consistent output, every time.
Extraction
as a workflow
Tested prompts with deduplication and merging logic. Feed a document in, get structured wiki pages out. 450 pages compiled across four domains, every internal link resolving.
A wiki,
not a database
Plain-text markdown files. Cross-linked pages. Folder structure and naming conventions designed for browsability. Works in Obsidian, Notion, or any text editor, with any LLM - Claude, GPT, Gemini, or local models. No vendor lock-in.
Auditable,
not just searchable
Every claim carries its source and a confidence tier. A skeptic pass flags single-source hype before it's filed; a regression test catches silent changes. The base stays trustworthy as it grows.
Working knowledge bases, built from scratch, verified, open, browsable.
Knowledge that compounds, not retrieves.
Regnor Knowledge is built on the LLM Wiki pattern - a framework published by Andrej Karpathy (founding member of OpenAI, former Sr. Director of AI at Tesla) for building persistent, structured knowledge bases maintained by LLMs.
The problem with RAG
Most AI + document systems use retrieval-augmented generation: upload files, retrieve chunks at query time, generate an answer. The LLM rediscovers knowledge from scratch on every question. Nothing accumulates. Nothing compounds.
The wiki alternative
Instead of retrieving raw chunks, the LLM reads each source once and integrates it into a persistent, interlinked wiki - updating entity pages, revising summaries, flagging contradictions, strengthening cross-references. The knowledge is compiled once, then kept current.
Why it works
The tedious part of a knowledge base isn't the reading - it's the bookkeeping. Updating cross-references, keeping summaries current, maintaining consistency across pages. LLMs handle that at near-zero cost. The wiki stays maintained because maintenance is automated.
What we add
Karpathy published the pattern. We deliver the implementation - calibrated to your domain, your document types, your taxonomy. Custom templates, tested prompts, wiki architecture, a runbook, and an integrity layer - source-tiering, a skeptic pass, and regression tests - so your team can operate it independently and trust what it produces.
From documents to compounding knowledge.
Every stage is iterative, structured, and domain-driven - composable templates, not opaque prompts.
Extract →
We study your document archive and identify the knowledge structures buried inside - entities, markets, concepts, products, forecasts.
Structure →
Custom templates, extraction prompts, and wiki architecture - calibrated to your taxonomy. Tested against real output, refined until clean.
Connect →
Cross-link atomic fragments into an interlinked knowledge graph. A company mentioned in three reports gets one page with three sources.
Compound
Your team runs the pipeline from here. Every document compounds the system - and deterministic checks keep it current, automatically.
Before you ask.
Practical answers about scope, process, and what you actually get.
How does a deployment actually work?
The delivery motion today is forward-deployed: we embed with your domain, calibrate the engine against your real corpus, and hand it over. That calibration is deliberate - a knowledge engine that hasn’t been tuned against a real corpus is a toy. Timelines depend on your document types and the complexity of your taxonomy; in practice it’s weeks, not quarters, and we confirm scope in the proposal.
What does the deliverable look like?
You receive a complete extraction system: custom templates, tested prompts, wiki architecture (folder structure + naming conventions), and a runbook your team follows to process new documents. Everything is plain-text markdown - no proprietary formats.
What happens after handover?
Your team runs the system independently. The runbook and the deterministic tooling are designed so a team is self-sufficient without us, and we stay close afterwards for questions and refinements. Extended support is available if you want it.
What size of document archive is this suited for?
The system works whether you have 20 documents or 2,000. What matters is that you're regularly adding new material and need the knowledge to compound. We calibrate the pipeline to your volume.
What document formats do you support?
PDFs, Word docs, web articles, slide decks - anything with extractable text. If your archive includes scanned documents, OCR is a prerequisite step we can advise on.
What if our documents are in a non-English language?
The system works with any language your chosen LLM supports well. The four knowledge bases built to date are English-language. For other languages, we test extraction quality during the calibration phase before committing to scope.
Do we need technical staff to run the system?
No. The pipeline is designed for analysts, not engineers. If your team can follow a checklist and paste text into a prompt, they can run it. We provide the runbook.
Do you see or store our documents?
No. We design the system using sample structures and anonymized examples. Your actual documents never leave your infrastructure. Zero data shared.
Can we modify the system after handover?
Yes. Everything is plain markdown and documented prompts. You can add templates, adjust extraction fields, or extend the wiki structure without us. It's yours entirely.
How do I know I can trust it - doesn’t AI hallucinate?
Every claim carries its source and a confidence tier - peer-reviewed, official, or practitioner. A skeptic pass attacks new claims before they’re saved: single-source hype and contradictions get flagged, not filed. And a regression test runs on every update, so if a fact silently changes, a red check tells you. It’s auditable and self-checking by design - not “never wrong,” but never quietly wrong.
Doesn’t the knowledge base get noisier as it grows?
A search-based archive does - more documents, more junk in every query. A linked, verified knowledge base gets stronger: every new entry connects into the graph and is checked against what’s already there. Deterministic integrity checks run continuously, so growth compounds reliability, not risk.
Is it a maintenance burden to keep current?
No - it maintains itself. Scheduled checks repair links, flag stale pages by topic, and regenerate the index automatically, at zero token cost. An optional weekly research pass pulls new literature and filings, fact-checks them, and files what survives. You get a living asset, not a growing backlog.
Let’s structure what you know.
A 30-minute discovery call. No documents shared. We learn your domains, your report formats, what your analysts actually need - then send a clear proposal with scope and timeline. We calibrate the engine against your real corpus, then hand it over: weeks, not quarters.
Or write directly - romil@regnor.systems
