Technology
Data and AI
Turning data into models, predictions and products.
Every prompt in this sub-category arrives with a test attached. A head of ML can run your drift check against a model that is live right now and watch whether it fires. A quality manager can hold your defect list against the batch that escaped last week. They ask an engine first and verify second, in that order, which is why a small firm here can be named ahead of a platform vendor. It is also why one wrong threshold ends the reading.
Where the answer is being lost
The generic answer already belongs to the tool vendors.
A head of ML who has watched a production model quietly lose accuracy asks 'How do you monitor for model drift in production'. What comes back is a tour of three observability products. It does not mention that the drift test you can use depends entirely on whether the label arrives in an hour or a quarter. So the engineer buys a dashboard, the dashboard reports green, and the model goes on degrading underneath every decision it touches.
How we win this
The programme for data and ai
The condition nobody states
Every question in this file turns on a dependency the generic answer leaves out. Whether the label arrives in an hour or a quarter decides which drift test is even available to you. Whether the corpus is ten thousand chunks or ten million decides whether a retrieval score means anything. Whether the defect is a surface scratch or a dimensional tolerance decides whether a camera can catch it at line speed. We write the conditional version, because the condition is the part that changes what actually gets built.
Your buyer builds with the same machinery
Nowhere else does the buyer understand the mechanism from the inside. A head of ML or an AI lead already knows why one passage gets selected over another. They can tell within a paragraph that a page was assembled rather than written, and some of them will put your firm's name to a model themselves, to see what it says about you before they write. That cuts both ways. Nothing padded survives contact with this audience, and being cited stops being a marketing number, because the buyer is reading it as evidence you understand your own subject.
Not one audience
Two of these four are read by practitioners and two by operators, and the split decides the channels rather than the tone. A platform engineer or an AI lead reads under a named person's byline, watches conference talks, and forwards whatever settled an argument, so the work goes into references, full transcripts and threads signed by your engineers. A CFO costing a rebuild or a plant head raising a capex request reads cost material and trade titles, and will never open a developer forum. One plan run across all four is how firms end up publishing to the wrong room.
Sending the wrong enquiry away
The failure mode here is not silence, it is the enquiry that should never have arrived. A plant head who has read a headline accuracy figure and expects it to hold on their own defect mix will start a pilot that cannot succeed, and that costs both sides more than never speaking. So the writing states what does not work, on which defects and at which line speeds. Analytics gets the same treatment for a different reason: the vendors have owned the generic question for a decade, so we scope it to narrow sector ground and say plainly that it buys a hearing before it buys a pipeline.
The mix that carries it
Content
Answer and comparison pages
Cost, process, eligibility and comparison pages built for direct extraction, not for a reader who scrolls.
Content
GEO blogs and authority content
The definitive written answer to the questions your buyers put to an engine, structured so it can be lifted and attributed.
Foundation
Entity and schema engineering
Structured data and entity definition so engines know exactly what you are, where you operate, and what you are credible in.
Measurement
AI Presence tracking
Standing measurement of inclusion, share of answer and competitor movement as models update.
Authority
Original data and benchmarks
Proprietary numbers, surveys and benchmarks — the most-cited asset class there is, because nobody else has them.
Foundation
Technical fixes
Crawlability, render, speed and the machine-readability faults that keep an engine from reading you at all.
The constraint we work inside
Most of what would make the strongest proof belongs to your clients. Accuracy figures, drift rates, close times: they sit inside the engagement and stay there, so the publishable proof is methodology and whatever benchmark data is genuinely yours to give away. The second thing to know is that this is not a body of work that finishes. A page written against this year's stack can be wrong a quarter later, when a model release changes the recommendation or a library deprecates the function the answer rests on. What goes up in the first quarter gets re-checked in the third, and that re-checking sits inside the scope rather than arriving later as a separate invoice.
Specialisations
4 total
The pitch is different for each one, because the buyer, the trigger and the rules on what may be published are different for each one. Open the one that is yours.
A CFO sits through two teams presenting two different revenue numbers, then asks an engine 'How do you build a single source of truth for revenue reporting'. No consultancy appears in the answer.
The question deciding this today
“How do you build a single source of truth for revenue reporting”
- Who they sell to
- Teams reporting and analysing what happened in the business
- Who signs
- Head of data, CFO, operations lead
- What starts it
- Reporting burden, a decision made blind, new data source
- Cost of staying invisible
- Meetings spent arguing about whose number is right
The answer belongs to the warehouse and BI vendors. They have published on this for a decade and they will keep winning the generic version, because the generic version is a product description. What nobody has written is the version with a sector in it: revenue recognition for a subscription business with usage billing, or a distributor consolidating three ERPs after an acquisition. That is the ground where a services firm is still selectable, and it is narrow on purpose.
What we would run
- 01AI Visibility Diagnostic
We put the reporting questions your buyers actually ask to the live models and record what comes back. Most of it is product pages. The useful part is finding the questions that return a firm rather than a tool, because those are the only ones worth writing for.
This is the most contested ground in the file and we would rather establish that in week one than after a quarter of writing. It also settles which sector cut we take, which is the whole decision here.
- 02Answer and comparison pages
A page per reporting problem you genuinely solve: what a single source of truth costs to build for a mid-size distributor, how long a semantic layer takes, what has to be true in the source systems before any of it starts.
The CFO is costing a rebuild, not shortlisting a tool. Those are the questions asked before anyone contacts a firm at all.
- 03GEO blogs and authority content
The worked account of one reconciliation: two systems, the definition of revenue each of them used, the rules written to settle it. Named fields, named edge cases, the sequence you followed.
A head of data checks this against their own mess. It is also the one thing a tool vendor cannot write, because it is about the data model rather than the product.
- 04Entity and schema engineering
Entity definition that states which sectors you have rebuilt reporting for, which stacks you work in, and the size of business you serve, instead of leaving you described as an analytics consultancy.
An engine has to put you on one side of a line: a product, or a firm that builds with products. Left to infer it from a capability page, it sends the reporting question to the vendors, because from the outside that is what an analytics company looks like. The sectors and the stacks are what put you on the other side of the line.
What we would not recommend
- Quora. Written for people learning a BI tool. A head of data designing a semantic layer is years past that, and the threads are old enough that half the products in them have since been renamed.
- Reviews and testimonials. Having needed help to settle whose revenue number was right is not a thing a finance team confirms in public. The testimonials we could actually gather would come from the smallest engagements, and a head of data reads that difference at a glance.
- Video and YouTube. A recorded walkthrough of a reporting build is a product demo with your logo on it, and the vendors make better ones with more money. What has to be citable here is a definition and the rule that resolves it, and both read faster than they watch.
What a lead looks like
A head of data at a distributor, three weeks into an acquisition, who has read your page on consolidating two chart-of-accounts structures and wants to know whether the same approach survives a third ERP. They list their current stack in the first email. They are not evaluating tools, they are scoping the modelling.
What we measure
- Whether any services firm is named at all beside the tool vendors
- Inclusion on the sector-qualified reporting questions we chose to take
- Metric definitions published in full rather than summarised
- First emails that describe the reconciliation before we ask for it
What changes
The enquiry comes from a head of ML who ran your drift test against their own model, did not like the result, and wants to know what a proper monitoring layer costs to build. Or from a quality manager who can name the defect their line keeps missing. They write with the problem already specified and in your vocabulary, so the first call is scoping rather than qualifying. Volume drops and the conversation starts further in.
Start here
See who gets named in data and ai today
We put your buyers' real questions to the live models and come back with the businesses they name, the sources behind those answers, and the gap between that list and yours.