CherryPick Get a diagnosis
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cherry-pick (v.) To choose, from everything available, what is genuinely worth it.

AI can build almost anything. My job is picking what's worth building.

I'm Miguel Martín Lacoma. I help leadership teams at mid-sized and large companies decide where AI genuinely changes operations, I build it, and I answer for it working at nine on Monday morning.

Get a free diagnosis

Miguel Martín Lacoma on stage The talk that best explains how I think La era de la escasez · 22 min

How I work

There are twenty AI initiatives on the table. I keep three and explain in writing why I stop the rest.

The diagnosis is literally this: a list of everything your company could do with AI, what's already under way and what nobody has proposed, with what isn't worth it crossed out and explained. What remains has a cost, a timeline and someone who answers for it: me.

Everyone now has the same access to AI. What changes the outcome is deciding where to apply it, what it does alone, what it asks you, and what you always decide.

  1. 01 Copilot for all 1,200 employees Almost nobody knows what to ask it. Context first.
  2. 02 Read supplier invoices across all six entities 40,000 a year checked by hand. This first.
  3. 03 Replace the ERP
  4. 04 Decision memory for the leadership team
  5. 05 Customer service chatbot on the website
  6. 06 Carrier claims drafted by an agent, with approval

What a Monday looks like

AI handles the volume. You decide what matters.

Monday, 9:00. 214 invoices have come in. Until now, someone in accounts spent the morning checking each one against its order and delivery note. Sound familiar?

214 invoices AI reads & matcheswith order and delivery note 196booked automatically new rule Laura18 for review Week 1 · 4 · 8 214 invoices AI reads & matcheswith order and delivery note 196booked automatically new rule Laura18 for review Week 1 · 4 · 8

Example · supplier invoices

  1. All 214 invoices go through Laura, one by one. All morning.
  2. AI reads them and matches each to its order and delivery note.
  3. 196 match and are booked automatically.
  4. Only 18 reach Laura: the ones that don't match.
  5. Laura approves 15 and corrects 3. Her corrections become rules.
  6. Only 9 reach Laura now.
  7. 4 reach her. Her time goes where judgement is really needed.

If your company still does this by hand, it should already be solved. It's a good place to start. Get a free diagnosis →

  1. Free diagnosis

    A session with leadership and the heads of the area you choose. Within two weeks I hand you the crossed-out list, in writing. It's yours even if we stop there.

    Free

  2. Project

    I build what we chose with AI, in short deliveries, with your IT team and within your security policies.

    Fixed price, never by the hour

  3. Into production

    On your real data, permissions and systems. Not another pilot.

    Included in the project

  4. Continuity

    I keep it running and improve it, or hand it over to your team fully documented. Data and code are yours.

    Optional monthly fee

I take on 4 diagnoses a month, because I do them myself.

Where I usually come in

Typical situations, depending on who you are

These are examples, not client cases. Each one ends with what you decide.

Executive leadership

«I have the data, but I can't tell how things are going»

Example

The leadership team's memory

Now
Something is decided in the leadership meeting and by March nobody remembers why or who owned it.
I do
Every meeting yields decisions and owned tasks, in a memory that doesn't depend on who attended.
You
You approve what becomes company policy.
Example

The leadership report

Now
Each department builds its report by hand, with figures that don't match each other.
I do
One up-to-date view, pulled straight from the systems, with nobody preparing it.
You
The leadership team debates decisions, not numbers.

Finance and operations

«Month-end close still runs on overtime»

Example

Supplier invoices at scale

Now
Thousands of invoices a month, from hundreds of suppliers across several entities, checked by hand.
I do
They are read, matched against order and delivery note, and only the mismatches reach a person.
You
Your team handles the exceptions.
Example

Multi-entity reconciliation

Now
Every close, matching bank and intercompany entries across several entities by hand.
I do
The reconciliation proposal arrives ready, with every difference explained.
You
A person reviews and confirms.

Technology and transformation

«We have twenty AI pilots and none in production»

Example

Pilots that never reach production

Now
Twenty AI pilots open, none in production and nobody deciding which to close.
I do
I review each one: value, data, risk and running cost. I recommend closing most and taking two or three to production.
You
The leadership team decides with the reasoning in writing.
Example

Copilots nobody uses

Now
AI assistant licences for the whole workforce, and almost nobody knows what to ask it.
I do
I connect the assistant to each role's context: its documents, its systems and its repeated tasks.
You
You measure real use, not licences.

See all examples →

Clients I've worked with

  • Levante UD

The talk

When intelligence stops being scarce, what matters is deciding where to apply it.

Wealth depends on two things: energy and intelligence. For 300,000 years at least one of them was scarce. Energy stopped being scarce with the industrial revolution; intelligence is ceasing to be scarce now, ten to a hundred times faster.

“Today's wealth is leveraged, one way or another, on the scarcity of intelligence.”

That's why CherryPick is called CherryPick. If anyone can build almost anything, the work that matters is choosing what to build. Not protecting what you have: converting it.

Miguel Martín Lacoma on stage during the talk "La era de la escasez"

Who answers for it

Senior judgement, no layers.

At a large consultancy, the person who sells is rarely the one who thinks or builds. Here it's the same person: I do the diagnosis, design the solution, build it with AI and answer for it. Before this I spent thirteen years as CTO and COO at Redradix, with clients like Telefónica, GMV, BME and Hugo Boss. Every month I write in El Economista about what AI is changing in companies, and I bring that same judgement to yours.

More about me →
  • 1
    Fixed price

    I never bill by the hour. You know the cost before we start.

  • 2
    Your data, your code, your systems

    Everything is built on your infrastructure and documented. If I'm not around tomorrow, it keeps working.

  • 3
    I answer for it

    What I put into production, I stand behind, and I work with your IT team, not behind its back.

Let's start

Tell me what's holding you back.

That's what I prepare the diagnosis session with. If I see I can't help, I'll say so in my first reply.

Email
info@cherrypick.tech
Phone
+34 610 70 30 44

Talk to my digital twin

An AI that knows how I work, my services, what I write and my talk. It answers instantly, at any hour.

Tell me in your own words: what gets done by hand, what gets lost, what you'd like to know and don't.

Controller
Miguel Martín Lacoma (CherryPick)
Purpose
to answer your request and, if you ask, prepare a proposal
Legal basis
your consent
Recipients
no data is shared unless legally required; Railway (hosting), Resend (sending) and Google Workspace (email) process it on my behalf
Rights
access, rectification, erasure and others, by writing to info@cherrypick.tech

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