Connect your business systems. Surface every place they disagree. Let a human pick the right answer once — and give every AI agent, chatbot, and report a clean, compact context they can actually trust.
Why Linqura
Cursor became essential because it understood your entire codebase — not just individual files, but the relationships between them. Your AI agents need the same thing for your business data: not just access to a warehouse, but an understanding of which number is canonical, which source has been approved, and which definitions conflict across the systems you actually use.
Warehouse governance tools work well — inside their platform. But when churn lives in HubSpot, active customers are counted differently in Stripe and Segment, and no layer knows which one to trust, your AI picks whichever number it finds first and calls it "high confidence."
Before your agent ever runs, a human on your team looked at the conflicting sources, picked the right one, and approved it. Now every agent, every report, and every chatbot gets that same answer automatically — with the source, the owner, and a freshness check attached. Across every system you use.
The Problem
Most companies have business data spread across 3–5 systems that tell slightly different stories. That's manageable — until someone needs to act on it. A business user asks a question, or an AI agent runs a workflow. When there's no agreed source of truth, there's no reliable answer — for a human or an agent. That's not acceptable when decisions depend on it.
How It Works
Linqura sits between your data sources and anything that needs to use them — your AI agents, your chatbots, your reports, your team.
Plug in your warehouse, CRM, billing tool, support system, and docs. Linqura maps every metric and entity across all of them — finding every place two systems tell a different story.
Everywhere two systems disagree, Linqura surfaces the conflict and asks someone on your team to pick the right answer. That decision is saved into your Trust Registry — the single place where your company's agreed-upon definitions live. Every future question, from a human or an agent, uses that approved answer automatically.
Your agents make one call to Linqura instead of fanning out across 3–5 MCP tools. They get back a compact context package — the right number, the source, who owns it, when it was last checked. Every correction feeds back into the graph, so future queries get faster and cheaper automatically.
Who Uses It
Linqura is the layer that makes your business data ready for AI — so every answer, from a human or an agent, comes from a source your team has verified.
Plug in your warehouse, HubSpot, Stripe, Notion. Takes an hour.
Linqura surfaces every conflict. You pick the right answer — once.
Approved sources, metric definitions, entity mappings — all locked in.
Maintained automatically as data changes. New conflicts surface in the queue.
What was enterprise churn last quarter? No SQL. No dashboard hunting.
The answer shows the number, the source, who approved it, and when it was last checked.
One click to correct. Updates the graph so the same mistake never happens again.
linqura.get_context("churn_rate") → compact context package (~200 tokens)
Approved answer, source, owner, freshness. No MCP fan-out needed.
User corrections flow back into the graph. Future queries route faster and cost less.
The Differentiator
Linqura isn't asking you to trust a confidence score. It uses machine learning to scan your systems, detect where definitions drift, and surface the conflicts worth resolving. But the actual decision — which source is right, which definition your company uses — is made by a person. That human judgment is what makes an answer trustworthy in a way no model confidence can replicate.
And every decision your team makes teaches Linqura more about how your business thinks about its data. The graph improves. Future conflicts surface with more context. New data sources get resolved faster. The product gets better the more your team uses it — because the judgment that improves it is yours.
Linqura automatically finds every place two systems define the same concept differently — metrics, entities, definitions that drift over time or across tools.
Your team looks at the options and picks the right answer. Not a model score — a real business decision, made by the person who knows what your company actually means by "active customer."
Every approved decision is stored. Future conflicts surface faster. Future agent queries route directly to the resolved answer — one call, no MCP fan-out, less context consumed. The more your team engages, the smarter and cheaper every agent query becomes.
Good — Linqura works best on top of one. Your pipeline defines how metrics are calculated. Linqura adds what a pipeline can't: a named human who approved that definition, a correction workflow when the answer is wrong, a compact agent-ready context API that wraps the output, and resolution for the data that never made it into the warehouse — HubSpot properties, Notion docs, the spreadsheet RevOps still uses for the board deck.
If you don't have a mature pipeline yet, Linqura helps you define what it should say before you build it.
Interactive Demo
For AI Builders
After the conflicts are resolved, your AI agents don't need to figure anything out. They call Linqura's context API and get back exactly what they need — the approved number, which system it came from, who signed off on it, and when it was last checked. No MCP fan-out across five systems. No dumping your entire warehouse into context. And if a question touches multiple sources — a report referencing a doc referencing a metric — Linqura figures out which ones to call and in what order. You don't wire that logic into every agent separately. One call. Done.
The resolution layer is already built. Your agents skip straight to using trusted data — you don't spend months building the infrastructure that makes it trustworthy.
Connect a new system, and Linqura automatically finds every place it conflicts with what you already have. Resolve the conflicts, and the new source is trusted everywhere instantly.
If an agent or a human gets a wrong answer, they flag it. That correction updates the approved source — so the same mistake never happens twice, and future queries route directly without any re-resolution work.
The first time your agent asks about a metric, Linqura may route through resolution. The hundredth time, it returns the answer in a single graph lookup — no extra MCP calls, no extra tokens. Resolution happens once. Savings compound indefinitely.
How It Compares
The same query. Two approaches. Watch what each one does under the hood.
For Your Team
Business users ask questions in plain English and get answers that show exactly where the number came from, who approved it, and when it was last updated. The right answer is already verified before anyone asks.
Build vs. Buy
Multi-source resolution with human approval workflows, data path tracking, provenance tagging, and a context API is not a weekend project. Every team that has tried to build this has shipped something brittle or run out of runway.
Who It's For
If you check 4 of 5, we should talk.
The Pilot
Fixed scope. Success criteria agreed upfront. Click each phase to see what gets built.
We're accepting a small number of pilot partners. Tell us about your setup and we'll follow up within 48 hours.
Share where you are. We'll read it and follow up within 48 hours if it sounds like a fit.