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trail · knowledge engine

AI that knows your business from the inside.

Trail is your company second brain — a living knowledge engine you can chat with and look things up in. It ingests EVERYTHING your company knows — brand, facts, tone, history — into one base that both your team and an AI can draw on. Always in your voice, with your facts. Never a generic guess.

Brand guides·Images and logos·Templates·Campaigns·Domain knowledge·Audio · Video
How it works

From material to living AI assistant.

  1. Onboarding — upload your knowledge

    Trail compiles it into Neurons — knowledge cells with provenance.

  2. AI responds and generates

    Always in your voice, with your facts — not a generic LLM guess.

  3. Curation

    Everything AI-suggested lands in a curated queue; a human approves.

  4. Deploy

    Trail is connected as a knowledge base on your site, or as an internal lookup brain.

Compare
DimensionTraditional RAGTrail
Working modelQuery-time retrievalIngest-time compilation
Voice and brandNo guaranteeYour voice always
Knowledge accumulationStatic chunksGrows organically over time
Quality assuranceNoneCurated approval queue
On your own site

Your own AI assistant — available 24/7 on your website.

Put Trail on your site as a chat your visitors can ask — or use it internally as your teams lookup brain. It answers in your professional language, with your companys own facts, 24/7, and gets smarter with every article you publish.

Further reading

Your own model is cheaper than you think.

The expensive part of a knowledge base is the entrance: every source has to be read before it becomes Neurons. The article explains how Trail Scout teaches a small, specialised model to do that work cheaply and close to your own data. Read the article

Articles

What we have written about en-trail

How do you know an AI isn't making things up?

The usual answer is a promise. Trail gives a different one: it measures it every night, against an answer key — with a control that proves the measurement can tell the difference.

Open source and digital sovereignty: Europe must be able to stand on its own feet

An email account in The Hague, a Danish ministry moving to LibreOffice, and the EU's first open source strategy. Why we build our own platforms — and offer the same to our clients.

When the chatbot has no answer, the question should not disappear

Four routes a question can take with Aidan and our other chatbots — and the one line we do not move: a quote is never sent without a person.

Your own model is cheaper than you think

Why we at broberg.ai train a small model for Trail, and what it means for the cost of knowledge.

From article to podcast without a microphone

Five links, one human — and five bugs we only found by running the whole chain. How our podcast pipeline is put together.

Knowledge you don't have to remember to save

Trail has gained two new ways of capturing knowledge: Ambient, which watches along while you work, and Web Clipper, which saves the page you are on — as content, not as a bookmark.

Chat With Your Site — Update Content, in Multiple Languages, in One Sentence

"Chat with your site" isn't a future vision — it's the screen that greets you at login. 60+ tools, translation on command, and the same engine available as a headless CMS via REST/MCP.

Three architectures of agent memory — and why Trail picked Compile

Karpathy, Tan, and Liu all start from the same diagnosis — your agent is a retriever, not a thinker. They reach three different architectures: retrieve (RAG), compile (LLM Wiki), and act (Fat Skills / GBrain). Trail picked Compile, deliberately.

Supervised Fine-Tuning: When Should You Train the Model — and When Should You Not?

Supervised Fine-Tuning has become the new buzzword — but it's one tool out of four. We walk the full staircase from prompt engineering through RAG to SFT, and add our own fourth path: Trail, a compile-at-ingest knowledge engine.

Hi — I'm Aidan