Most production RAG fails the same way: it generates plausible answers and the team trusts them, until somebody asks for the source and the agent points to nothing. Trust collapses, the project dies. We build with citation discipline by default. The data agents we ship for clients run on the same RAG architecture behind 300+ live content sites and our own browser-based language model. Every substantive answer traces to a source. The agent says "I don't know" when it cannot find one.
Citation by default. No free generation. No vendor-hosted training on your prompts. The agent answers because it found the source; if it cannot, it doesn't answer. That standard is non-negotiable.
Reads PDFs, scanned images, tables, forms, contracts. Extracts structured data: line items, dates, parties, totals, signatures. Pushes to your database, ERP, or data warehouse. Handles 10,000-document pilots and 10-million-document backfills with the same architecture.
RAG over your Notion, Confluence, Google Drive, SharePoint, Slack history, support knowledge base. The agent answers internal questions in natural language with cited source pages. Replaces the "ask Sarah from Ops" pattern with something every new starter can use from day one.
The plumbing under every serious AI product: chunking strategy, embedding model selection, vector store, reranker, retrieval evaluation, observability. We build it once, properly, against your data. Other AI features stand on top of it. Half the AI products that fail in production fail because the RAG was an afterthought.
One natural-language query, indexed across your tools: support tickets, internal docs, ERP, CRM, code repository, data warehouse. The user asks; the agent searches everywhere; the answer cites the system, the document, the row. The replacement for "I'll look in five places."
Three production proof points, before we get to your build.
300+ automated content sites. A pipeline that handles content generation, deployment, and performance monitoring across hundreds of live sites. Every site is researched, drafted, deployed, indexed, and monitored without manual intervention. The retrieval and generation layer is the same RAG architecture we ship for clients.
Signet LLM, live at llm.digitalsignet.com. A language model transformer built from scratch in TypeScript, running entirely in the browser. A demonstration that we understand the language-model layer at the level of source code, not vendor abstractions. When the RAG breaks in production, knowing why matters.
The AI Job Impact Calculator. A reference site with full source citation methodology, showing how primary sources (OECD, ILO, Brookings, BLS, WEF) translate into a buyer-facing calculator. The same citation discipline we apply to every internal knowledge agent we build.
We do not just talk about RAG. We have shipped it in production, at scale, with citation discipline, and we can show you the source code.
Vendor-agnostic where it makes sense. Opinionated where it matters. We have stress-tested every component of this stack in production.
Most production data AI fails the same way: it generates something plausible-looking and the user trusts it. Three months in, somebody asks for the source and the agent points to nothing. Trust collapses, the project dies.
We build with citation discipline by default. Every substantive answer traces back to a source document, page, paragraph, or row. The user can click through to the source, verify the agent got it right, and learn what is in the document they did not know about. The agent earns trust because the trust is verifiable.
Where the agent cannot find a confident source, the answer is "I do not know" with the searches it ran. This is unusual; most consumer AI is built to never say "I do not know". For internal knowledge agents this is the only correct behaviour. The same standard applies to our AI for Legal work where citation is a regulatory and professional requirement.
Your company has 5+ years of accumulated docs in Notion, Confluence, or Drive and finding anything is now harder than asking a person.
You receive thousands of supplier invoices, contracts, or claims a month and human review is the bottleneck.
You tried a vendor RAG product and it hallucinated answers your team spotted but customers might not.
You are building an AI product and the retrieval layer is becoming the constraint.
Your support team answers the same questions a hundred times a week and the answer is documented somewhere nobody reads.
Your data sensitivity rules out vendor-hosted AI. You need a knowledge agent that runs in your tenant.
We map your sources, your data sensitivity, the question you want answered. Pick the architecture (RAG flavour, vector store, embedding model). You leave with a written specification, an effort estimate, and a recommendation on what to build versus what to buy.
Document extraction pilot, internal knowledge agent, or RAG infrastructure built end to end. Goes live with citation discipline, retrieval evaluation, and an honest accuracy number on day one. We hand over runbooks and ownership.
We monitor accuracy, push fixes when retrieval drifts, retrain as your documents grow. Quarterly accuracy benchmark, red-team review against hallucination patterns. Model and infrastructure costs at cost.
Voice agents are RAG agents in disguise. Every voice customer service or training agent we build sits on top of the same data AI stack.
Document extraction at finance scale: AP automation, contract terms extraction, invoice OCR. Same architecture, finance-specific tuning.
Citation discipline matters most where regulation requires it. Legal AI is the most disciplined data AI we build.
We build document extraction, internal knowledge agents, and RAG infrastructure for mid-market companies across the UK, US, and Australia. Signet LLM and 300+ live content sites prove the stack.
