Challenges before users arrive..

As zero click results, AI summaries and other off site surfaces take on more of the work of explaining organisations, performance becomes more dependent on platforms and interfaces that teams do not control. Those Upstream gaps are becoming harder to ignore for machines and for people.

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This page is for teams who want to understand why sensible digital work is not moving the needle, and what is happening before users and AI ever reach their site.

Common upstream challenges we see.

Many teams are working hard on their websites and digital experiences, yet still feel like something is not quite joining up. Decisions are made, changes are shipped and tests are run, but clarity does not increase.

Results feel fragile. Confidence stays low.

What looks like a UX or tooling problem is often a clarity problem that starts before users arrive, in the signals and structures that shape how humans and AI understand you.

frequent issues

Decisions shaped by what is easy to see, not by how users and AI really encounter you
Upstream journeys and signals owned by no one, or split across teams with conflicting incentives
Conflicting signals between search, marketplaces, third party content and the website
Local fixes to pages or flows that do not touch the underlying problem
More tools and dashboards, less shared understanding of what is actually happening

The invisible decision.

Many important decisions now happen before someone reaches your website. People see AI summaries, marketplace listings, reviews and snippets in search results, then make up their minds. The website becomes a place to confirm a decision or look for reassurance, not where the decision is made.

What this looks like

Traffic and conversion look acceptable, but growth stalls without a clear reason
More people arrive mid journey, already fixed on a product, plan or competitor
Stakeholders assume the problem is in the funnel, not in the way demand is shaped upstream
Small changes in search or AI interfaces have outsize effects on demand and behaviour

Why typical fixes do not help

Teams focus on tweaking pages, flows and copy that only influence people who arrive
Zero click surfaces and AI summaries are treated as edge cases or an SEO concern, not as core touchpoints
Experiments target on site behaviour without a clear view of what users believed before they landed
No one is responsible for understanding how platform changes affect the way people and AI decide

What needs to change

Understanding where and how the real decisions are being made
Treating upstream surfaces as primary parts of the journey, not as a separate channel
Connecting changes in upstream signals to the way journeys, content and reassurance work on site
Reducing surprise when platforms or AI interfaces change, by knowing which signals they depend on

Split signals.

Different parts of the ecosystem tell different stories. Search results, review sites, help content and the website do not line up. Humans and AI systems pick up those inconsistencies quickly, often in ways teams do not see.

What this looks like

The website makes confident claims that are not backed up by reviews or third party content
Help content and product copy use different language for the same things
AI summaries present a version of the organisation that feels slightly off, overly generic or outdated
Internal documents, sales materials and public content describe different realities

Why typical fixes do not help

Teams rewrite interface copy without fixing the underlying contradictions
Brand, product, SEO and support work from different assumptions and vocabularies
No one is looking at how all of this appears together in search results or AI outputs
AI systems blend mixed quality signals into a single confident answer that is hard to correct piecemeal

What needs to change

A clearer, shared picture of what is actually true and what should be said
More consistent structures and signals across content that humans and AI use
Stronger links between what is promised, what is delivered and what third parties observe
Ways to correct and strengthen the signals that AI systems rely on when they describe you

Local fixes for global problems.

Teams often feel pressure to fix individual pages, flows or features quickly. The underlying issues usually sit in strategy, structure or upstream signals. The result is local optimisation that does not move the global picture.

What this looks like

Repeated redesigns of key pages with only marginal impact on performance
Experiments that improve local metrics while overall outcomes stay flat
Roadmaps that add more options and complexity without resolving basic confusion
Teams and vendors judged on narrow KPIs, even when the real issues cross boundaries

Why typical fixes do not help

Work is scoped around what a single team can change, not around where the problem truly lives
Tools and processes encourage small changes and tests rather than systemic improvements
Constraints and policies that shape behaviour are treated as fixed, even when they are the real issue
Incentives reward activity and short term wins, not long term clarity or stability

What needs to change

Starting from a system level view of how decisions are shaped and how value is delivered
Being honest about which problems are structural and cannot be solved with surface changes alone
Using experiments to test changes to structure, signals and strategy, not only visual or microcopy tweaks
Aligning incentives so teams are rewarded for addressing real problems, not just visible ones

Tool fog and partial evidence.

Organisations have more tools and data than ever, but often less shared understanding. Different teams see different slices of the truth. AI and zero click surfaces introduce another layer of behaviour that is not reflected cleanly in dashboards.

What this looks like

Teams get stuck in debates about which data to trust
Insights from research or analytics do not seem to align with what stakeholders see anecdotally
AI summaries and new search layouts change behaviour, but existing tools do not show that clearly
Each channel reports on its own metrics, with little connection to how people actually move or decide

Why typical fixes do not help

Adding more tools increases noise without improving clarity
Teams commission new research without making full use of what already exists
Data is reported in channel silos that do not reflect real journeys or decisions
AI mediated behaviour is treated as an interesting edge case rather than a core source of evidence

What needs to change

A single view of the most important signals before and during a visit
Agreement on which questions need answering and which evidence matters for each
Using AI and zero click surfaces as part of the evidence base, not as an afterthought
Turning tools and reports into shared understanding, not competing versions of reality

How Corpus helps before users arrive.

Corpus helps teams confront these upstream challenges directly, instead of treating them as background noise.

We:

Map how humans and AI encounter your organisation across search results, AI summaries, marketplaces and other off site surfaces
Connect those upstream patterns to what actually happens on your website and in your products
Help teams reframe problems so they reflect real behaviour, constraints and incentives, not just surface symptoms
Use existing data and research wherever possible, so new work is targeted and proportionate

What you gain by addressing upstream challenges.

When teams take upstream challenges seriously, they tend to see:

A clearer explanation for why previous work did or did not move the needle
Fewer debates driven by opinion, and more decisions grounded in a shared view of reality
On site work that is better aligned with how people and AI already understand the organisation
Less surprise when platforms, search layouts or AI interfaces change, because the dependencies are understood
Experiments and changes that are easier to explain, defend and iterate over time

Talk about the challenges you are seeing.

If the patterns on this page feel familiar but you are not sure where to start, a conversation can help. We will focus on your current situation, constraints and goals, and be honest if we do not think Corpus is the right fit.

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Typical first conversations last 45 to 60 minutes and focus on understanding your current situation and constraints.

Upstream optimisation for zero click and AI search.

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Typical first conversations last 45 to 60 minutes and focus on your current situation, constraints and goals

We Are Corpus is a consultancy created by Abi Hough and delivered through uu3 Ltd. Registered in the UK. Company 6272638