Building an AI-powered pricing database for a global aviation operator

QuantSpark turned a global aviation operator's pricing history, previously scattered across individual contract documents and disconnected systems, into a single, searchable database. For the first time, the operator's commercial team could trace any price back to the contract that set it, compare pricing consistently across customers and stations, and walk into a negotiation with visibility rather than guesswork. That is a real, delivered change in capability. It is not, on the evidence in this case study, attached to a percentage, a pound figure or a time saving: the projected return on investment from smart pricing recommendations is described as a forward roadmap item, not a result banked to date, and this account keeps that distinction intact.

The problem

The problem QuantSpark was solving is a familiar one for large operators with many customers and many stations: the pricing data that should have driven better commercial decisions was locked away in the wrong format. Terms sat buried inside individual contract documents, rather than in a system that could be queried, so commercial staff had no fast way to retrieve, compare or trend service pricing over time.

Without a consolidated view, the team struggled to see where their own pricing sat across the business, spot the patterns that a good negotiation depends on, or judge with confidence whether a quote was competitive. That made pricing harder to optimise, revenue opportunities harder to spot, and a competitive position harder to hold.

The approach

QuantSpark's answer combined document-extraction AI with a deliberately staged build. The first move was to apply the AI to unstructured contract text, pull out the pricing terms as structured, queryable records, and consolidate that data from multiple systems into one central database. On top of that base, the first phase of delivery layered three capabilities.

Visibility came first: interactive dashboards that trace every price straight back to its source contract, so a number on screen is never divorced from the agreement that produced it. Analysis came next: examining how pricing is distributed across customers and stations, and enriching the dataset with metadata such as aircraft categories and exchange values so that comparisons hold up even where contract structures differ. Intelligence followed: mapping services and aircraft descriptions onto a single, consolidated taxonomy, so that like is genuinely compared with like across contracts that might otherwise describe the same service in different words.

A roadmap then set out the next stage of work: improving extraction accuracy, deepening data enrichment, benchmarking prices, and building toward market-aware pricing recommendations. That is scope still to be delivered, not scope already banked, and this account treats it that way throughout.

The workflow

Read as a workflow, the build runs cleanly from ingest to insight. Contract documents are pulled together from scattered systems. Document-extraction AI structures the pricing terms buried inside them. That structured data is centralised into one queryable database. Dashboards, metadata and a consolidated taxonomy then turn the result into something commercial teams can actually compare and act on.

The systems involved are best understood categorically rather than by name: a document-extraction AI layer, a centralised pricing database, an interactive dashboard or BI interface, and a data-enrichment and taxonomy layer sitting on top of the database. Nothing in the source names a specific vendor or product, so none is claimed here.

The value

The value delivered sits squarely in that capability shift. Commercial teams gained one searchable view of pricing across customers and stations, in place of pricing scattered across contract documents. Decision-making was streamlined by the ability to trace any price back to its source contract. Competitive positioning improved because pricing could, for the first time, be compared consistently across the operator's own book of business. And pricing negotiations were better informed, backed by a visibility that simply had not existed before.

On return on investment, the honest position is that the case study does not evidence one. It names the ROI from smart pricing recommendations as a forward-looking roadmap ambition, not a measured result. Where projects of this kind do eventually quantify a return, the generic method is to compare outcomes with and without pricing visibility, for example fewer under-priced renewals or faster negotiation cycles, benchmarked over a defined period. No such calculation exists in this source, so none is asserted as fact here: the delivered outcome is the pricing database and the visibility it created, and that is the story this case study can responsibly tell.