Most companies are guessing about AI. You don't have to.
We find the work that is quietly eating your team's week, and automate it with the AI that actually performs — measured, not marketed. No jargon, and no six-month project before you see anything working.
You don't need to understand AI. You need to know what it fixes.
Most AI advice is written for engineers. This part is not. Here is the honest version of what these tools are good at, what they are bad at, and what that means for a business with real deadlines and a real budget.
What it is genuinely good at
Reading enormous amounts of text and pulling out exactly what you need. Sorting, summarising, drafting, classifying, matching. Anything a capable person could do given the document and enough coffee — but at a thousand times the volume.
What it is genuinely bad at
Being certain. It will give you a confident answer whether or not it actually knows, so anything that must be right needs checking built into the process. It also cannot see your business — it only knows what you show it.
What that means for you
The wins are boring and enormous: the repetitive reading, typing and checking that fills your team's week. Not robots. Not replacing anyone. Just removing the part of the job nobody wanted in the first place.
Pick your world.
See the honest answer.
No form, no sales call, no email gate. Choose your sector and we will show you the three things we would look at first, and what each one typically returns.
Healthcare & clinical
Three places we would startWhat the manual
version is costing you.
Pick where you are and move the sliders to your own numbers. This is the arithmetic every AI project should pass before anyone writes a line of code — and most never do.
Five questions.
An honest verdict.
Not a lead-capture quiz — there is no form at the end. Answer five questions and the gauge tells you whether AI is worth your time yet, and what to do either way.
The gauge moves as you go. Nothing is sent anywhere — this runs entirely in your browser.
What we would do first
Six reasons this
goes differently.
Most AI projects fail the same way: someone picks a model because of a headline, builds for six months, and nobody can prove it works. We inverted that order.
We measure before we build
We benchmark the models publicly, every month. So when we tell you which one to use for your job, that is a measurement we can show you — not a preference.
You see something working in weeks
We start with one painful, well-defined job and get it running on your real data fast. If it does not work, you have lost weeks rather than a year — and you will know exactly why.
Built for regulated, messy reality
Our work has run on NHS medical records — scanned, inconsistent, full of personal data. If it survives that, your documents are not going to be the problem.
You get the engineer, not a sales team
You talk to the person building it. No account manager relaying your requirements to a team you never meet, and no junior learning on your budget.
We tell you when the answer is no
Sometimes AI is the wrong tool and a two-day process fix would save more. We would rather say that and keep the relationship than sell you something impressive that does nothing.
Everything is measurable afterwards
You get a dashboard showing what it processed, what it got right, what it flagged for a human, and what it saved. The value is a number, not a feeling.
Four weeks, start
to something real.
Scroll through the stages, or click any one. The panel shows what changes at each.
We find the expensive job
A week inside your process, following the paperwork. We come back with the jobs ranked by what they actually cost you in hours and salary — usually including one nobody had noticed.
We measure the options on your data
Your documents, your edge cases, your accuracy bar. Every serious model runs the same task set and we show you the scoreboard, including cost per document and what happens when the input is a bad scan.
We build the smallest thing that proves it
Not a platform. A pilot, on real data, that your team can put their hands on in a fortnight. Checking is designed in from the start, so you can see what it got right and what it sent to a human.
We hand you the numbers
Volume processed, accuracy against a held-out sample, hours returned, cost per item. If the numbers do not justify going further, we will be the ones to say so.
Not vibes.
Measurements.
This is the part that makes the rest credible. Every month we test the major AI models on identical real tasks and publish the results in full — including the ones that make popular models look bad.
Figures below are illustrative placeholders showing how the Index reads. The first live measurements publish at launch, with the full harness and raw outputs open-sourced alongside them.
| Rank | Model | Made by | Score |
|---|
The chart that decides architecture. Top-left is the value corner — strong results without the bill. Most teams overpay for a model they could not tell apart from one a quarter of the price.
Already running,
in production.
Eight years of shipped systems across healthcare, medico-legal, retail, cybersecurity and SaaS. These are delivered results, not projections.
Case bundles that read themselves
A team was reading scanned medical records by hand at ten pages an hour. We replaced it with a system that reads them, sorts them by type, regroups pages that belong together, and builds an accurate timeline of what happened to the patient and when.
Finding the right expert in seconds
Law firms picked medical experts from memory and a spreadsheet. Now the system reads the case, finds the best-matched experts from a curated database, explains why each one fits, and drafts the instruction letter.
Predicting what will sell, before it is made
A design and prediction platform built from scratch and adopted by more than fifty brands in six months. It spots which designs resemble past winners, and predicts which customers will buy what.
Proving the AI is actually right
Most teams cannot tell you whether their AI got better or worse last month. We built the scoring system that answers that automatically — and it is the same method behind the public Index.
Three divisions.
One standard of evidence.
Prametriq Labs
Public benchmarking. Every new model, run against real tasks, with the workings published beside the score.
- The Prametriq Index, updated monthly
- Plain-English teardowns of each release
- Free, open testing tools
- No sponsored verdicts. Ever.
Prametriq Solutions
The systems themselves, for organisations with real constraints — regulated data, legacy documents, auditability, and a board that wants numbers.
- Document and workflow automation
- Search that understands meaning, not keywords
- Accuracy monitoring built in from day one
- Data protection designed in, not added later
Prametriq Studio
Websites and social presence, with the same rigour applied to the front of the house and AI tooling built in rather than bolted on.
- Fast, accessible, measurable websites
- Content systems that run themselves
- Social management with real analytics
- Built on the tooling we benchmark
Built by someone who
ships this for a living.
Prametriq is a small, senior AI practice. The work is done by the person you speak to — an AI Solution Engineer Lead with eight years of production experience across healthcare, medico-legal, retail, cybersecurity and SaaS.
That experience includes building an enterprise AI capability from nothing — no team, no infrastructure, no standards — defining the architecture, testing practice and deployment governance from scratch, and working directly with NHS clinicians and medico-legal professionals to turn genuinely complicated workflows into systems that hold up under audit.
Prametriq exists because that work kept hitting the same wall: nobody could say, with evidence, which model was right for a given job. So we started measuring.
You will not be handed to an account manager, and no junior will learn on your budget. That is the whole model.
The things people
actually ask us.
Straight answers, including the ones that are not in our commercial interest.
We are a small company. Is AI worth it for us?
Often more than for a large one, because a small team feels wasted hours immediately. The question is not your headcount, it is whether you have a repetitive job with enough volume behind it. Ten people spending ten hours a week each on document work is a stronger case than a thousand-person company with no clear bottleneck.
Use the calculator above. If the annual number is smaller than the cost of a decent pilot, we will tell you to wait.
How much does this cost?
It depends entirely on the job, so any number quoted before we have looked at your process is guesswork. What we can say is the shape: a discovery week is a small fixed fee, a pilot is a defined project with a fixed scope, and ongoing support is a monthly arrangement.
We would rather scope it properly and give you a real figure than publish a price list that turns out to be wrong for you.
Will this replace our staff?
In the work we have delivered, no — it removed the part of the job people disliked and let them do more of the part that needed judgement. A five-fold throughput increase on medical records did not mean fewer reviewers; it meant the same reviewers handling far more cases without working later.
If your actual goal is headcount reduction, say so at the start. We will tell you honestly whether the technology gets you there, and often it does not.
Our data is confidential and regulated. Is that a problem?
It is the environment we come from. Our work has run on NHS medical records — scanned, inconsistent and full of personal data — with detection and consistent masking of personal details across whole case bundles, and an audit trail behind it.
Regulated data changes the architecture, not the answer. It is designed in at the start rather than added at the end, which is the mistake that makes these projects fail their first audit.
How do we know the AI is actually right?
Because we measure it and show you. Accuracy against a held-out sample, what it processed, what it got right, and what it flagged for a human instead of guessing. That measurement layer ships with the system rather than being promised later.
Anyone who cannot tell you their accuracy number does not have one.
What if it does not work?
Then you find out in weeks rather than a year, having spent a pilot budget rather than a programme budget. We agree what failure looks like before we start, so a negative result is a clear answer instead of an argument.
A cheap, fast no is a good outcome. An expensive, slow maybe is the thing to avoid.
Which AI model do you use?
Whichever one measures best for your job, and that changes. This is exactly why we run the Index — model rankings move every few months, and the right answer for reading scanned documents is frequently not the right answer for multi-step reasoning.
We build so the model can be swapped without rebuilding the system around it, because it will need swapping.
Do you only work with companies in the UK?
We are UK-based and most conversations start there, but the work is remote by nature and the calculator above covers several regions for a reason. What matters far more than geography is whether your data can be reached and whether someone inside your business owns the outcome.
Start with a conversation,
not a contract.
Tell us what your team spends too long on. We will tell you honestly whether AI helps, and what it would take — before anyone talks about money.
No sales sequence · A real reply from a real engineer
Thank you — that has reached us. Expect a reply, not an automation.