Repository Health Check

The AITS Content Tracking System (CTS) gives an executive one number: a composite Repository Health score. At 96.8%, you know your repository is sound, and you can say so with confidence in any room. At 64%, there is work to do, and the breakdown behind the score shows exactly what it is.

BI charts and reports show you the data. CTS uses AI and machine learning to tell you what it means: how healthy the repository is, what is driving the score, and what to do to improve it. That evidence is what gives you confidence in what every AI initiative rests on.

You choose what healthy means. The score is built from the KPIs that you and your managers select and weight. AITS recommends a proven starting set, and you add to it, drop from it or change the weights as your priorities change.

Behind the score is AI trained on your own document repository. It begins with a health check that establishes where the repository stands today, then keeps the score current around the clock, for every document and data record as it is added.

For example: a contract held in three places with no record of which is the signed one, or a sensitive document stored where too many people can see it. CTS flags it, scores its effect on repository health, and recommends action, before the issue becomes a wrong answer or a complaint.

What you receive: the score, the breakdown behind it, and a prioritized list of what to fix first. It is evidence you can show the board or an auditor.

It improves over time. CTS learns from every finding your staff confirm or correct, so its alerts get sharper, and it can take on the routine fixes you approve. Your official repository is never touched.

Click the Demo link below the image to see an illustrative demo.
In the demo, click on the health score to see the composite makeup.

Most organizations cannot say. That is the problem: not that the content is known to be poor, but that its condition is not known at all.

Most organizations hold years, often decades, of valuable content across document management systems, shared drives, cloud storage and email. Much of it is hard to find, inconsistently structured, duplicated or out of date. Problems are usually fixed only once they surface: a failed search, an audit finding, a privacy complaint, an AI pilot that gives wrong answers. Until then, nobody could have known.

AI does not repair a repository. It inherits it. As you know, three things follow from that:

• It inherits what it finds: duplicates, superseded versions and missing metadata all become part of its answers.
• It answers with confidence either way: AI does not know which of three copies is authoritative, or that a policy has been replaced.
• It follows the permissions it is given: if access controls are wrong, AI will surface a sensitive document to someone who should never see it.

Gartner expects 60% of AI projects that are not supported by AI-ready data to be abandoned through 2026. The models are not the problem. The content is.

The gains come from finding issues early: each one corrected improves the next result, and the improvements compound.

Modernization can begin with the records and processes that already exist. Paper records can be digitized, and content from legacy and acquired systems brought together, starting with what the health check shows to be most at risk. This work is difficult to do well at scale, and its quality is hard to see until a document is needed. CTS measures it: every record is scored as it enters the repository, so you know the work is complete, legible and correctly filed before paper is destroyed or an old system is retired.

AITS provides the health check and the ongoing monitoring, and that measurement is independent of whoever performs the digitization or migration. That work can be carried out by your own provider or arranged as part of an AITS engagement.

The result is a progressive path from scattered content to searchable information, from searchable information to connected, governed data, and ultimately toward an ongoing healthy document repository that AI can safely work from.

CTS is not a fixed set of rules applied to every repository. Its AI is trained on your own records, so it learns what a sound record looks like here. The measuring is automated. The standard being measured against is yours.

It learns how you solve problems. When staff confirm, correct or dismiss a finding, that decision trains the model. CTS learns not only to recognize an issue but how your people resolve it, so the alerts get sharper and the number needing review falls.

You decide how much it does. Every type of issue starts with CTS recommending and a person acting. When you are satisfied it handles a type correctly, you can let it prepare the fix for approval, and later give it standing permission to resolve that type itself. Staff check a sample, every action is logged and reversible, and you can withdraw the permission at any time.

The decisions stay with your people. CTS corrects repository records: filing, links, duplicates, classification. It never alters the content of a document.

People stay in charge. CTS learns from your records and business staff, and their judgement is what it cannot replace. It takes the repetitive work off their desks and leaves them the review, the exceptions and the decisions.

The repository improves with it. Every issue corrected raises the score, and the trend shows whether the repository is getting healthier or slipping. Better records make the AI more accurate, and a more accurate AI finds the next issue sooner. The two improvements compound.

CTS does not run against the official repository. Your repository environment is replicated in a secure cloud environment hosted in-country, and the AI is trained and run on that replica. It works alongside the systems you already have, with no platform replacement.

Every item is assessed, from the first to the last. The replica starts empty. Your content is loaded into it in bulk, the same way a digitization project loads records into a repository, and CTS assesses each item as it arrives. Existing content, newly digitized records and new documents all go through the same check, so the score covers the whole repository and not a sample.

The official repository remains untouched. Nothing CTS does is destructive, and any result or correction can be compared against the original at any time.

Corrections are made and checked in the replica first. Applying them to the official repository remains the organization's decision, through its own change controls.

Health is not one property. CTS scores the repository across eight dimensions, each built from KPIs you define and weights. These eight are the starting set AITS recommends; each rolls up into the composite score, and each can be opened to see what is pulling the score down. Together they show whether the record has the four characteristics of an authoritative record under ISO 15489, the international records management standard: authenticity, reliability, integrity and usability.

Organizations rarely have all of their information in one place. Enterprise repositories, departmental systems, network drives, cloud storage and application-specific document stores may all contain information that employees depend upon.

AITS does not require a particular document management platform. The Repository Health Check can be applied to a wide range of enterprise, specialized and file-based repositories.

Examples include:

Repository modernization follows eight steps. The health score is not a ninth. It runs alongside all eight and shows whether each one left the repository better than it found it.