Runes AI Platform / Spend Analytics

Spend Analytics

Spend always reconciled to what the CFO sees in the general ledger, leakage against contracted price found at the invoice line, and a defensible view of what you should be paying and how to negotiate it.

What the application does

Spend reconciled, always

Invoices and purchase orders reconciled with what the CFO sees in the general ledger, so a cycle ends in a decision rather than an argument about whose number is right.

Maverick spend, discovered

Off-contract and off-process buying surfaced and matched to the categories you manage, rather than found by accident.

Leakage against contracted price

Invoice errors, over-invoicing, excessive debits and index prices applied incorrectly, found at the invoice line where they can be proven and recovered.

Contract centre

Contracts and amendments mined to flag what is expiring, what is rolling on evergreen terms, and what sits on unfavourable terms that should be revisited, so renewals happen on your timing. It also checks that the right contract type is in place for each category.

Cost reduction levers on who you buy from and what you buy

Supplier base consolidation, material consolidation, tail rationalization and overpayment against specification, each sized rather than asserted.

What price you should be paying

Price rationalized across vendors and plants using your own data, reconciled for differences in terms such as freight inclusion and payment terms, with rebate and volume tier effectiveness assessed.

Signs you need this

Procurement reports a savings number finance cannot find in the general ledger, and every cycle ends in a reconciliation argument rather than a decision.

You have a spend cube, a dashboard or a procurement platform that shows where the money went, but turning that into savings still takes a great deal of analysis.

You suspect you are paying above contract in places and cannot prove it at the invoice line. Overcharges, wrong escalators and missed rebates get found by accident, if at all.

Contracts renew on their own. Evergreen terms roll over and price escalations apply before anyone reviews them.

Category managers spend most of their time assembling data and very little of it negotiating.

What is AI about this

Contracts become terms, and the terms get checked.

Contracts as data

Machine-readable terms

Agreements and amendments converted into structured terms: price bands, index formulas, volume tiers, rebate triggers, notice periods and renewal dates. Once those exist, every invoice can be checked against the terms that actually apply to it.

Classification

Your taxonomy, not a generic one

Invoice line descriptions, part numbers and vendor names read and classified into the categories you actually manage, which is also what catches off-contract buying.

Agents on the tail

Discrepancies investigated end to end

A rule flags the variance. A bot continuously monitors the data, pulls the contract and its amendments and the right index publication for the month, checks whether freight or payment terms explain the gap, and returns a recoverable claim with an amount and citations. Today that takes a buyer a day or two per case, which is why most leakage is never pursued.

Should-cost

Models you can defend in the room

Vendor cost structures reconstructed from published input indices, energy and freight rates, regional labour data and disclosed financials, then calibrated against the prices you have actually paid across plants, vendors and volumes. Each cost line traces to a named public source with a date, and every negotiation outcome feeds back in.

See it on your own spend.

Send us an extract as it is. We will come back with what is reconciled, what is leaking, and where the price is worth revisiting.

Book a discovery call Working Capital