AI consulting for executives. Right-size your automation.
AI is very good at some work and wasteful at the rest. We help leaders
tell the two apart: AI for the reading, judging and deciding, traditional
software for the counting, calculating and record-keeping, with the AI
directing it.
We work with the systems you already run, in stages small enough to learn
from. A number you can take to the board.
A two-minute check, all clicks. No email needed to see where to start.
The wrong engine for the job. Right-sizing means AI for the judgment,
traditional tools for the heavy lifting, and the power matched to the work.
Plain English, on purpose
This field runs on jargon, and we have spent ten thousand hours in it. We
know the vocabulary because we have lived in it. You should not have to.
We treat you as a smart professional who wants to understand how the thing
works without learning a dozen acronyms to do it. Ask us anything and you
will get an answer in ordinary words.
There are a lot of misconceptions about what AI is and how it works, and
some wild claims about where it ends up. That is a little like predicting
the end stage of electricity in 1926. The useful work then was wiring the
building well, and it is the useful work now.
It has to start at the top
Your people want to work with AI, and many already are. It shows up in
four ways, and all four are reasonable.
Remarkable gains
Some people have rebuilt how they work and are far more productive than a year ago. Most of what they know lives in their own habits, and nobody else gets the benefit.
Impressive toys
Clever things built in an afternoon that nobody but the person who wrote the prompt will ever use. Good practice for them. Not yet a change to the business.
More than a text generator can give
Expecting judgment, exact arithmetic or perfect recall from something that generates plausible text. The disappointment is predictable, and so is the fix.
A better search box
Prompting reluctantly, and using AI as a search engine or an answer engine. It is very good at both. But is that what the business needs from its people using AI?
Individual enthusiasm does not add up to organizational change on its own.
Somebody has to decide what the business needs from AI, own the result, and
use it in front of everyone else.
That is a top-down job, and we work with leaders who have personal skin in the game.
Start with the business problem
If a person cannot do the process end to end, even slowly, a machine will
not do it faster.
Business schools have taught it for a century: make the process work for
people first, then speed it up. Clear steps, clear ownership, a clear
definition of done. Only then is it worth handing to a machine, and by then
you know exactly what you are asking it to do.
So we start with the business problem, not the technology. What is the job,
who does it today, and where does it actually get stuck?
Right-size the engine to the job
A language model produces text one small piece at a time, and that is
remarkably powerful. It is also only one of several proven ways to automate
business work. Decades of older tools came before it: systems that search a
company’s documents, organize what a business knows, and apply fixed
rules. They give the same answer every time, you can check their work, and
they are cheap to run. The skill is knowing which to use where.
Step 1
Let AI do the language work
A language model produces text, and for reading, drafting, summarizing, sorting and deciding what to do next, it is exactly the right tool.
Step 2
Let proven tools do the computing
Adding a column of numbers is arithmetic, not prose. A spreadsheet or database does the math exactly, every time. A model can return a plausible-looking answer without doing the sum. We have the AI direct the real tool.
Step 3
Retrieve, then read
Search systems, and the ways businesses organize what they know, have spent decades getting the right record in front of the right reader. Use them to find it, and use AI to read it.
Step 4
Start small and adjust
Baseline the process, automate a slice, and check the cost and the errors against it. Keep what works. Mistakes stay small, early and cheap.
Some work needs an AI. Some needs a spreadsheet. Most needs both, with
something sensible in charge.
What makes it hold up
The demo is the easy part. Anything that runs your business has to
survive contact with your data, your people, and your auditor.
It knows your business
Generic AI gives generic answers. We ground it in your own records so what comes back is about your company, not about companies in general.
It shows its work
Every answer traces back to the record it came from. Your people check it in seconds instead of taking it on faith, which is what earns trust.
It stays inside the lines
Who can see what, what it is allowed to do, and a log of everything it did. The controls your auditor will ask about, built in from the start.
It gets tested
Before it runs and after, because the models change underneath you. Last quarter’s result is not a warranty on next quarter’s.
How we work
Start small, prove it works, then decide how far to take it. The approach
is the same for a bank, an agency, a drugmaker or a factory.
📐
The Blueprint
We read how your organization works and where AI would take real load off it. You get the highest-leverage place to start, costed and sequenced, with plain advice on what not to build and what to wait for.
1–2 weeks
🔧
The Build
We put it into your highest-leverage process, on the systems you already run, with the cost tracked from day one. Access controls, logging, and an audit trail so you can show it holds.
5–10 weeks
🔄
The Standing Program
This field changes every few months. We keep what you built working, keep the numbers checked against the baseline, and keep your leadership current.
Monthly
We ship what we advise
Applied Relevance also makes a product. Epinomy WMS runs a working warehouse:
it drives Universal Robots arms, talks to PLCs over MODBUS TCP, and deploys
air-gapped for sites that will never put their operations on someone else’s cloud.
We mention it because it keeps us practical. Software that has to run a
warehouse teaches you quickly which jobs need AI and which need a plain,
reliable program.
Pepsi, Coca-Cola, Quaker, Kodak, The Home Depot, Lowe’s
Industry and manufacturing
Siemens, Alcoa, Mercedes-Benz
Professional services
Deloitte, KPMG, PricewaterhouseCoopers, Ernst & Young, McKinsey & Company, Booz Allen Hamilton
Built by practitioners
We have spent thirty years in the technologies that came before: enterprise
search, organizing what businesses know, and managing and retrieving
information at global scale, across public sector, financial services,
pharmaceuticals, telecommunications and more. The tried-and-true techniques
still work, and they pair well with what AI does best.
We work in whatever you already run. We get things wrong too, so we build
in stages small enough that mistakes show up early and cost little. Part of
our value is knowing what not to build yet, or what to wait six months for
because it is about to arrive inside software you already pay for.
We take on a small number of clients at a time.
The first step is a Napkin Sketch: a video call where we sketch where AI
could help you, what to leave alone, and whether a Blueprint is worth your time.
No slides, no pitch.
