No. An LMS or LXP is a platform people log into. Filtered Intelligence is infrastructure ;  it makes the content and skills data inside your existing stack readable and usable, including by AI. Many of our customers use Filtered specifically so they do not have to buy or renew an LXP: they get the outcome they wanted from an LXP without adding another portal to manage.

Filtered does four connected things. Ingest Content pulls learning content from any source and processes it in a secure environment. Map Skills connects that content and your workforce data to skills frameworks. Signal Quality scores every asset for relevance, quality and duplication. Connect AI exposes the whole layer to AI agents via MCP. Four components, one infrastructure layer.

Most learning content sits inside sealed SCORM packages ;  readable only as a title in a catalogue. Filtered opens those packages and reads the content at module level: the actual learning objectives, structure and substance. That is what makes content searchable, mappable to skills, and usable by an AI agent, rather than an opaque file nobody can see inside.

No. Filtered makes the content you already own ;  and the content you license from third parties ;  structured, scored and discoverable. It is content-source agnostic: LMS, LXP, content libraries, internal knowledge bases and SCORM packages all feed into the same layer.

Rather than sending your content to a third-party AI service, Filtered runs its processing ;  transcription, tagging, scoring ;  within a secure, self-hosted setup. This is what allows enterprises in regulated industries to make their content AI-readable without it ever leaving their controlled environment.

Filtered is certified to ISO 27001 and holds Cyber Essentials certification, alongside FuturePlus impact certification. Enterprise security reviews are a standard part of our onboarding, and we work with customer infosec teams early in any deployment.

Access is controlled by you. Filtered operates on a strict multi-tenant model with tenant isolation by design, and your data is exposed only to the systems and AI agents you explicitly authorise. The infrastructure is built so that switching it on extends your stack ;  it never opens your data to anyone you have not approved.

Filtered is built on MCP, so it works with any MCP-compatible AI assistant ;  including Microsoft Copilot, Teams AI, Claude and ChatGPT Enterprise ;  as well as custom enterprise agents. Because the connection is through an open standard, new agents can reach Filtered without bespoke engineering each time.

No. Filtered makes the AI assistants you already have better. It is the layer underneath them ;  the structured skills and content data they need to give grounded, accurate answers. Your Copilot or agent stays exactly where it is; Filtered gives it something useful to reach for.

A general AI model can summarise a document, but it cannot tell good content from outdated content, see what is duplicated, or know how an asset maps to your skills framework. Filtered provides that structure ;  quality scores, skills mapping, a unified view of the estate ;  so the AI’s answers are grounded in what your organisation actually owns and trusts, not in a generic response.

Traditional LXPs typically cost £500K to £1.5M+ annually. Filtered delivers the outcome enterprises actually wanted ;  content that is structured, skills-mapped and usable ;  at roughly one tenth of that cost, and without adding a portal for employees to adopt.

Enterprises consistently uncover significant duplicated and unused content, then redirect spend to what is genuinely valuable. AstraZeneca gained a single view of its content estate and reduced content spend considerably. GSK saved an estimated £250K versus manual skills mapping. ECITB has reported over £1m in annual savings. Content search and curation is typically around 80% faster once the estate is structured.

Weeks, not months. Because Filtered works through APIs and an open protocol rather than a platform rollout, there is no migration project and no organisation-wide adoption curve. Building equivalent capability in-house typically takes 12–18 months and requires dedicated engineering; Filtered is operational and connected far faster.

The best first step is a demo built around your own stack ;  your systems, your content sources, your AI tools ;  so you can see exactly how Filtered Intelligence would connect them. From there, most enterprises begin with a defined scope (a content estate or a business unit) and expand once the value is proven.