Problems we solve

The agentic era broke the mid-market data stack before most companies had one.

Data stacks were built for humans reading reports. AI agents need data that carries its meaning, its rules and its permissions with it. For a company with a small team, that gap shows up as eight familiar problems.

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Data silos

The problem

Customers in the CRM, money in finance, activity in the operational system, the rest in spreadsheets and supplier portals. Nothing joins up, so someone exports, pastes and reconciles by hand.

Cost: hours every week, and answers that are only as good as the last export.

How Kenerix resolves it

Connect each source once. Kenerix queries data where it lives or hosts it when that is faster, then links the systems on a single data map, including keys that only match after cleaning, with a note of how.

The report queue

The problem

Every new question becomes a ticket for IT or the one BI analyst. By the time the report arrives, the decision has been made without it.

Cost: slow decisions and a key-person risk at the centre of the business.

How Kenerix resolves it

Anyone describes the report in plain English. An agent builds it from certified definitions, checks the queries, previews the data and saves it privately until it is ready to share.

Arguing about numbers

The problem

Revenue, active customer, backlog, cancellation: each department counts differently. Meetings start with reconciling rather than deciding.

Cost: lost trust in data, and decisions taken on instinct.

How Kenerix resolves it

Each measure has one documented meaning in the semantic model and knowledge base: what counts, on which date and what is excluded. Every person and every agent uses the same rule.

Knowledge in one head

The problem

Why a column is called what it is, which status really means closed, which old system still feeds the totals. It lives with one long-serving person.

Cost: fragile reporting, slow onboarding and real risk when that person leaves.

How Kenerix resolves it

A living knowledge base of concepts, measures, reports and guides, owned and certified by your team, versioned, and marked by whether a person or AI wrote it. Agents read it before they answer.

Shadow AI

The problem

Staff already paste exports into public chatbots to get work done. Nobody can say what left the building, or stop it happening again.

Cost: data leakage, data protection exposure and AI answers built on stale copies.

How Kenerix resolves it

Give people a sanctioned route. The AI tool they prefer connects through the Kenerix gateway and answers from live, governed data, so there is no reason to copy and paste.

AI without guardrails

The problem

Every AI connection is a live pathway into your systems. Agents granted broad access can read far more than the question needs, and one misconfigured tool widens the blast radius.

Cost: a risk your security lead cannot sign off, so AI projects stall at the first review.

How Kenerix resolves it

Governance runs at the moment of the request. Read-only access by default, per-client grants, role-based rows and columns, and changes only ever proposed for a person to review. Each person and agent sees only their data.

Finding out too late

The problem

Monitors are static, built once by someone else, and checked at month end. When a measure moves, the right person hears about it last.

Cost: weeks of lost reaction time on revenue, service and compliance.

How Kenerix resolves it

Create an alert in a sentence: “tell me when weekly revenue falls 10%”. It runs on the same certified measures as your reports and goes to the person who owns the fix.

Silent data drift

The problem

A supplier renames a field, a system update changes a status, a feed stops overnight. Reports keep running and quietly show the wrong number.

Cost: decisions on bad data, discovered only when someone notices it looks odd.

How Kenerix resolves it

Rules attach to fields and datasets in the semantic layer: null spikes, schema changes, distribution shifts. Monitoring agents flag the issue and, as the platform matures, re-run or rebuild what is affected.

A quick self-assessment

Five questions to ask your team this week.

If more than two answers make you wince, a readiness conversation will pay for itself.

Discuss your answers with us

How long does it take to answer a new question that spans two systems?
If our main data person left tomorrow, which reports would we lose?
Does everyone in the leadership meeting use the same definition of revenue?
Which AI tools are staff already using with company data, and what did they share?
How many days pass between a measure moving and the owner finding out?