Over the last few weeks, I’ve shared a little about DBR Intelligence™, the capability being developed behind Altiora Research’s Digital Buyer Readiness™ assessments.
Digital Buyer Readiness™ is concerned with a relatively simple question: how effectively does an organisation’s digital presence support the way modern buyers research, understand, compare and shortlist potential suppliers?
That question is becoming more important as the buying process changes. Enterprise buyers already conduct much of their research before speaking to sales, and increasingly they are using AI systems and large language models to help them make sense of markets, compare suppliers and identify potential partners.
But the work behind Digital Buyer Readiness did not start a few weeks ago.
The foundations have been developing for around eight months, initially during evenings, weekends and whatever spare time I could find. What began as an exploration of changing buyer behaviour gradually became something much more structured — involving research, code, APIs, assessment logic, evidence models, testing and a lot of trial and error.
At the centre of it all has been one recurring question:
How do you turn a large amount of digital evidence into consistent, defensible and genuinely useful commercial insight?
That question eventually became the foundation for DBR Intelligence™.
Digital Buyer Readiness started with buyer behaviour
The starting point was not technology. It was buyer behaviour.
I have spent most of my career in enterprise IT sales, and one of the biggest changes I have seen is how much of the buying journey now takes place before the first meaningful conversation with a supplier.
Buyers increasingly arrive at that conversation having already formed an opinion. They have visited websites, reviewed capabilities, looked at customer stories, checked partnerships and certifications, considered security credentials and compared several potential providers.
In many cases, they have already started to determine which companies feel credible, relevant and worth speaking to.
AI is adding another layer to that process.
Large language models and AI-powered search tools can now help buyers research markets, summarise supplier capabilities, compare providers and answer questions that would previously have required hours of manual research.
For technology providers, this changes the question.
It is no longer simply:
Can buyers find us?
It is increasingly:
Can buyers and AI systems understand us well enough to trust us, compare us and include us on the shortlist?
That is the problem Digital Buyer Readiness™ is designed to examine.
Looking beyond traditional website optimisation
Traditional website optimisation has largely focused on helping search engines discover, index and rank content. That remains important, and Digital Buyer Readiness is not intended to replace SEO, content strategy or digital marketing.
It asks a broader question.
When somebody — or increasingly something — discovers your organisation, is there enough clear, consistent and credible information available to understand what your business actually does?
Can your areas of expertise be identified easily? Is your proposition consistent across the site? Are your vendor relationships and technical capabilities clear? Is there evidence to support the claims you make? Can someone understand who you work with, what problems you solve and why they should trust you?
These may sound like straightforward questions, but answering them consistently across a complex digital presence is not always straightforward.
This is particularly relevant for IT providers. Many resellers, integrators, MSPs and technology service providers represent similar vendors, hold similar accreditations and offer overlapping services. From a buyer’s perspective, distinguishing between them can be difficult.
The quality, structure and consistency of digital evidence therefore become part of the competitive picture.
More than a prompt, a score and a PDF
From the beginning, I wanted Digital Buyer Readiness™ to be more rigorous than asking an AI model to review a website, generate a score and produce a polished report.
Generative AI makes it remarkably easy to create something that looks like an assessment.
Creating an assessment that is repeatable, evidence-backed and capable of standing up to scrutiny is a different challenge altogether.
That is why DBR Intelligence™ is being developed as a controlled assessment environment, rather than simply a collection of prompts.
The platform is designed to bring together the different stages required to conduct an assessment properly: collecting and normalising digital evidence, organising that evidence into a consistent structure, evaluating it against the DBR Framework™, applying controlled scoring logic, validating the results and translating the findings into practical recommendations.
It also needs to distinguish between what is supported by evidence, what may be inferred and what simply cannot be established from the available information.
That distinction matters.
If an assessment is going to tell a business that an area of its digital presence is weak, unclear or potentially affecting buyer confidence, there needs to be something more substantial behind that conclusion than an impressive-sounding AI response.
Building control around AI
One of the most interesting lessons from developing DBR Intelligence™ has been that using AI effectively is often as much about control as capability.
AI models can analyse information extremely quickly. They can identify patterns, compare content, summarise evidence and surface relationships that would otherwise take considerably longer to find.
But speed alone does not create a reliable assessment.
The methodology still needs defined criteria. Evidence needs to be traceable. Scoring needs to be applied consistently. Contradictions need to be identified. Missing evidence should remain missing rather than quietly turning into an assumption.
And, ideally, the same evidence assessed against the same methodology should produce materially consistent results.
Building those controls has become a significant part of the work behind DBR Intelligence™.
As a result, what started as a relatively simple concept is gradually becoming a specialised research and assessment platform designed specifically around Digital Buyer Readiness.
From a score to something actionable
Scores are useful. They provide an immediate indication of overall position and make complex findings easier to communicate.
But few business leaders really want to be told they have scored 6.8 rather than 7.4 and then be left there.
The questions that follow are much more important.
Why did we receive that score? Where are the weaknesses? Which issues genuinely matter? What can we improve, and what should we address first?
That is why the assessment is designed to go beyond scoring.
The aim is to translate digital evidence into a clear readiness position, detailed findings and prioritised recommendations that can be understood by marketing, sales and leadership teams.
For one organisation, that might expose weaknesses in proposition clarity or customer evidence. For another, important technical expertise may exist within the business but be almost invisible online.
Elsewhere, the organisation may have strong security credentials, vendor relationships or specialist capabilities that are simply not represented clearly enough for a buyer — or an AI system — to identify confidently.
Those are very different problems, and they require very different actions.
Why the initial focus is on IT providers
My initial focus has naturally been on IT resellers, systems integrators, MSPs and technology service providers.
Partly that reflects my own background, but it is also a market particularly well suited to Digital Buyer Readiness.
Technology providers often operate in crowded markets where apparent differentiation is limited. Several companies may sell the same technologies, hold similar partner status and describe broadly similar capabilities.
Yet buyers still have to decide who makes the shortlist.
That decision can be influenced by many individual signals: clarity of positioning, customer evidence, technical depth, security credentials, vendor relationships, thought leadership, digital authority and the overall consistency of the organisation’s digital presence.
None of those elements operates in isolation.
Together, they create a representation of the business that buyers use to form an opinion.
Increasingly, AI systems are interpreting many of the same signals.
That makes it important not only to be visible online, but to be understood accurately once you are found.
An unexpected return to coding
There has also been a more personal side to developing DBR Intelligence™.
It has taken me back to something I first became interested in a very long time ago: coding.
Like many people of my generation, my first experience of programming involved a ZX Spectrum. That usually meant typing in code, discovering it did not work, trying to understand why, changing something and running it again.
Quite often, it still did not work.
There was no AI assistant explaining the error message. In many cases, there was not much of an error message at all.
I went on to spend most of my career in enterprise technology sales rather than software development, so I certainly did not expect those early experiments to become particularly relevant again several decades later.
Apparently, all those hours spent in front of a Spectrum were not entirely wasted.
The tools available today are almost unrecognisable by comparison. Modern development environments, APIs, cloud platforms and AI-assisted coding allow a small organisation to build capabilities that would once have required a considerably larger development team.
One thing, however, has not changed very much.
You can still spend an unreasonable amount of time wondering why something worked perfectly yesterday and refuses to work today.
AI-native, but not AI-only
Altiora Research is being built deliberately as an AI-native research business.
That means using AI across research, analysis, testing and production where it can improve speed, consistency or analytical depth.
But AI-native does not mean AI-only.
Human judgement remains an important part of the process.
AI can organise evidence, identify patterns, compare large amounts of information and accelerate analysis. What it cannot remove is the need to understand commercial context, challenge questionable conclusions and decide whether a particular finding genuinely matters to a buyer.
For me, that combination is where much of the opportunity lies.
Machine capability where machines are strong. Human judgement where judgement matters.
For a small research business, that creates the possibility of doing work that would previously have required significantly more people, while still maintaining control around how the research is conducted.
What sits behind a Digital Buyer Readiness assessment
The screenshot accompanying this article comes from the actual DBR Intelligence™ working environment, with client and proprietary information deliberately removed.
I wanted to show something of what sits behind the finished Digital Buyer Readiness™ report.
The report itself is only the visible output.
Behind it is the less visible work of evidence collection, normalisation, assessment, scoring, validation, testing and interpretation.
That part is not particularly glamorous, but it may be the most important part of the process.
Ultimately, the value of an assessment is not determined by how polished the PDF looks.
It is determined by whether the evidence and methodology behind it are strong enough to support the conclusions being made — and whether those conclusions help an organisation decide what to do next.
Still building
DBR Intelligence™ continues to evolve. There is more functionality to develop, more testing to complete and undoubtedly more things that will break before they work properly.
That is part of building something new.
What began as an idea explored during evenings and weekends is gradually becoming something much more substantial: a controlled research environment designed around how organisations are represented, understood and evaluated in an increasingly AI-assisted buying landscape.
And, somewhat unexpectedly, building it has also taken me back to something I enjoyed doing many years ago.
Writing code, testing it, breaking it, working out why — and trying again.
Some things, apparently, have not changed quite as much as the technology around them.
Small company. Serious methodology. Increasingly serious technology.
