History & Projects
History & Projects. We Answer How Should Data Be Stored If The People Who Need It Don’t Know In Advance What They’ll Need & Their Primary Working Tool Is A Spreadsheet?
History & Projects – The Story Of Azquo
Azquo’s development began over 35 years ago. Bill Cawley was given the task of defining the basis of an expert system for marketing — this was the mid-1980s, when expert systems were considered the future of computing. Two things became clear early in the research. First, marketers rarely know at the outset of a project exactly what data they will require, and that data is likely to change as the work develops. Second, the spreadsheet was already the dominant analytical tool for business users, and any solution that ignored it would be ignored in return.
Bill concluded that the first requirement, before any expert system could be built, was a database fundamentally more accessible to business users than the relational databases of the time. The criteria were straightforward: a database that would accept any data at any time without needing to call in the IT department, and a database that could transfer data to and from a spreadsheet seamlessly.
The Core Idea
The key insight came from observing how people actually read data from a spreadsheet. We look at the row headings, the column headings, and any surrounding context to understand what a number in a cell means. This became the basis for how Azquo stores data: individual values, each with a list of names attached. Rather than forcing data into predetermined tables with fixed columns, Azquo labels each value with the names that describe it — a product, a date, a channel, a measure. This allows flexible dimensionality without restricting what data can be stored.
To make reporting more powerful, the concept of set hierarchies was introduced. A report cell labelled “London” automatically shows the sum of all data belonging to names within the set “London” — whether that’s individual postcodes, branches, or boroughs. Hierarchies can be as deep or as shallow as the data requires, and new ones can be added at any time.
Bill also recognised early that the flexibility of this approach would require rigorous traceability. If any data can arrive at any time from any source, every individual value needs a provenance record — who created it, when, and from which file. This was built into the system from the start, not added later as an afterthought.
Ark: The First Incarnation Of Our History
Our History & Projects starts with our first version, built before Windows was universal, was a DOS-based spreadsheet that interfaced directly with the database. It was known as Ark, and it was deployed across a number of major organisations:
De Beers:
Azquo was used to support the allocation of diamonds for De Beers. The allocation process involves matching complex criteria across multiple dimensions.
HSBC:
Azquo was used for central financial consolidation, bringing together data from multiple sources into a single queryable structure.
Wimpey:
Azquo supported project planning for the construction group, managing data across timelines, cost categories, and project phases.
Toto: The Windows Generation
A subsequent Windows-based version, named Toto, extended the platform’s reach. Its most significant deployment was in UK education: Toto was used to report on examination results across half of the local authority schools in England, handling the varied and evolving structures of educational data across hundreds of institutions. Further projects included work with Bally and William Grant.
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A Detour and a Return: Our History & Projects
At that stage, with development being done by one person and minimal marketing effort, the business took an unexpected detour into the sale of trampolines — bet you didn’t expect that from our History & Projects! This was a venture that proved highly successful until the market became saturated. Bill then founded Feefo, the product reviews and customer feedback platform, which continues to thrive as an independent business.
With the trampoline and Feefo chapters behind him, Bill returned to the database with his son Edd, an accomplished analyst and developer. Together they undertook a fundamental rebuild.
Azqou: The Current Platform
Edd reconsidered the technology from first principles. The new version introduced an all-in-memory database architecture, where names, hierarchies, and values are held in RAM with direct pointers between them, persisting to disk only for backup and recovery. He also removed the last vestiges of fixed dimensionality that had existed in Toto, achieving a fully flexible peer structure where any value can be attached to any combination of names.
The result is the current Azquo platform — faster, more flexible, and more scalable than its predecessors, while preserving the core principles established 35 years ago: accept any data at any time, work through spreadsheets, and trace every value back to its source.
Our Recent Projects
Joe Browns:
Azquo was deployed for budget planning and financial consolidation at Joe Browns, the multi-channel fashion retailer. This was a particularly good fit for Azquo’s architecture, because Joe Browns’ channel mix evolved year on year as new channels opened up — high street shops, Amazon, and other marketplaces — all of which could be absorbed into the existing data model without restructuring. Users’ existing spreadsheets were applied to cycle through budget instances, creating data that passed upward through the database to higher-level consolidation spreadsheets
Lloyd’s of London Managing Agent:
Azquo was used for bordereau processing for a Lloyd’s of London managing agent. Bordereaux are the detailed statements of insurance premiums and claims submitted by brokers and coverholders to managing agents. The processing of these documents had become significantly more costly than the agent could sustain, compounded by multiple changes to contract conditions and the varying participations of different underwriting syndicates. Azquo’s ability to absorb evolving data structures without schema changes, combined with full provenance tracking, made it possible to handle this complexity at manageable cost.
VetStream:
Azquo is being used to analyse website visit data for VetStream, a provider of clinical decision-support resources for veterinary professionals. The analysis draws together visit patterns, content usage, and engagement metrics into a single queryable structure.
In each of these cases, the common thread was the ability to interface easily with spreadsheets, absorb data of varying shapes without IT intervention, and adapt to changing structures over time. Azquo was able to take over from existing methods while preserving the interface that was familiar to users.
SheetRead
SheetRead is a version of Azquo specifically targeted at the Lloyd’s of London insurance market. The Lloyd’s market has its own vocabulary — bordereaux, contracts, managing agents, syndicates, coverholders — and its own data challenges. SheetRead packages Azquo’s core capabilities with terminology, workflows, and reporting structures tailored to this market, making it immediately usable by Lloyd’s managing agents and their teams without requiring them to learn a generic data platform.
What We’re Building Now For Our Projects
We are currently developing an AI layer that will automate the two tasks that currently require Azquo-specific knowledge: importing data and creating reports.
On the import side, the AI will handle the mapping of spreadsheet columns and external data sources into Azquo’s hierarchical structure — the process of defining which columns are names, which are values, how names relate to each other, and where they sit in the hierarchy. Today this requires understanding Azquo’s import syntax. With the AI layer, a user will be able to point Azquo at a spreadsheet or database export and have the structure interpreted automatically.
On the reporting side, the AI will generate report templates from natural-language requests. Rather than learning how to define row headings, column headings, and context filters in an Azquo report workbook, a user will be able to describe what they want to see — “show me monthly revenue by channel for the current financial year” — and receive a working report.
The goal is that the benefits of Azquo’s flexible, auditable, spreadsheet-native data model become accessible to organisations without any learning curve at all. Users load their data, ask questions of it in plain language, and get live Excel reports connected to a fully provenanced database — without needing to understand anything about the underlying data architecture.
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The Team
Azquo remains a small, focused team: Bill Cawley and his son Edd, together with two colleagues, Ed, who liaises with customers, and Nick, who specifically understands insurance, based in Ludlow, Shropshire.
The team works directly with clients, building and adapting Azquo deployments to fit each organisation’s specific data challenges. We are actively looking for businesses that can exploit this unique database — organisations where conventional data tools are too rigid, too slow to adapt, or too dependent on IT specialists.