Decision intelligence for teams without a data team
Every morning, a brief with the decisions worth making today: which accounts to save, which spend to cut, which renewals to push. Grounded in definitions your own team agreed on. You approve every one.
Seven quick questions. About two minutes.
Tuesday · 3 decisions
Contact these 4 accounts today.
Activity down 40% over 14 days: meets your definition of at-risk.
Pause Campaign B.
Acquisition cost 2.1× your agreed ceiling, third day running.
Acme renews in 21 days on last year's terms. Propose the new ones now.
Its volume is up 35% since it signed. The last 3 accounts in this position accepted new terms within a month.
The problem
Your team already uses AI. It writes the summaries, answers the questions, drafts the reports. And it does it with total confidence, using whatever it guesses "active customer" or "revenue" means.
That's the part nobody checked. Operations counts an active customer one way, sales another, finance a third. Your main operational system, the finance sheet and the board deck show three different revenue figures. An agent built on top of that doesn't fix the disagreement. It automates it, faster, and hands you an answer that sounds certain.
So the meeting still ends the same way: nobody's sure which number is right, and the decision waits another week. When a board, an investor or an auditor asks, someone rebuilds the answer by hand. And every AI tool you add from here reads the same three numbers and picks one without telling you.
What you get
A short list of what's worth acting on today, with the numbers behind each recommendation, the reasoning, and the exact definition it used. You say yes or no. Nothing happens on its own.
How it works
We interview the people who use your numbers and write down what they actually mean here: revenue, an active customer, a new customer, a healthy account. Where teams disagree, we surface it and you settle it. The result is a semantic layer: your company's definitions, written by your people, in a form a machine can use.
We wire your data to those definitions: your main systems, billing, CRM, the spreadsheets. When several companies or partners report the same thing differently, each one maps to the same definition. Every figure traces back to its source. It runs on a copy, never against production, sized to the data you actually have.
An agent reads the layer every morning and brings you the decisions worth making, each with its numbers, its reasoning and the definition it used. A person approves or rejects. Rejections teach it what you care about.
Our agents only reason with definitions your people approved.
If it isn't defined, the agent can't use it.
Why us
Anyone can connect an LLM to your database. What it can't do is know that your sales team counts a signed contract as a customer and your finance team waits for the first payment. That knowledge lives in people's heads, and it's the difference between an answer and the right answer.
We get it out of their heads first. Then we build.
The agent works from what your team agreed, and shows you which definition every recommendation used.
Recommendations, not autopilot, and a full record of what was suggested, approved and why.
You don't need to hire one to get started, and you keep everything we build.
The people on the first call are the people who build it.
What we won't do: decide what your company should care about. That's yours. We make sure the machine understands it.
Proof
A product company had its numbers in four places: product events in the production database, growth and finance in spreadsheets, and around twenty hand-maintained SQL files run over a connection that dropped mid-query. Every business question meant someone going to fetch the answer, by hand, from production.
We replaced it with one source of truth that rebuilds itself twice a day, with a single agreed definition behind every number and a standing dashboard in place of the queries.
The client's real figures aren't ours to publish; the architecture and numbers above are real, the dashboard figures shown are illustrative.
That's the ground a daily brief stands on. The pilot adds the part that reads it for you.
Who we are
Meaning & data
Mayela turns what your team means into something a machine can use. A PhD in linguistics before she became a data engineer, she runs the Capture and Connect steps: the interviews, the definitions, and the pipelines that hold them.
Agents & decisions
Anthony builds the agent that reads your definitions every morning and brings you decisions, with the approvals, permissions and audit trail that make it a system rather than a demo. He runs the Decide step.
Excellent communicator and a top-notch professional.
In-person course
On February 4, 2027, Mayela teaches Data-Driven AI, part of the AI Architect & Consulting track at GenAI Workshop Fest. Together we look at different data sources and decide how an AI system should reach each one: RAG, SQL, MCP or long context. Not every tool, just the approach that fits and why. In person, in Spanish, on one shared case.
Feb 4-5, 2027 · ADEIT, València Use code MAYELA-GAIWF for 10% off your registration.
Commercial partnership with GenAI Workshop Fest.
Questions
No. That's who this is for. We connect to what you already have and hand over everything we build.
No. It recommends; a person approves or rejects. Every recommendation shows the numbers, the reasoning and the definition behind it.
Read access to the sources behind the decision area you pick: typically your main operational system (via a copy, never production), billing or accounting, and your CRM. If your data can't leave your own cloud, it runs there.
Good. We build on what's there. The semantic layer is the missing piece that makes those tools agree with each other.
You keep the definitions, the connected data and the brief. If it earns its place, we roll it out to more areas. If not, you've paid for one area, not a platform.
It depends on where you're starting from. Answer seven questions and we'll show you the path we'd recommend and what it starts at.
Let's talk
Answer seven questions. It takes about two minutes. We'll show you where we'd start for a company like yours, and what it costs. Your answers go straight to the two of us.
Seven quick questions. About two minutes.
1/8
Definitions sprint
Before any agent makes a recommendation, your team needs one answer to "what counts as an active customer here?", and to the handful of numbers behind it. That's where we'd start.
Starting at $5,000
Credited in full if you go on to the 30-day pilot.
30-day pilot
You have a clear first decision and nobody spending all day on data. That is the setup the pilot is built for.
Starting at $15,000
Custom plan
At your size, or with people already on data, the fastest path depends on what's there today: the tools, the definitions, the people. That's a 30-minute conversation, not a price list.
We'll put a number on it after one call.
What you told us
What happens next
Your answers are on their way to Mayela and Anthony.
Prefer to talk now? +1 (312) 523-1858