Data Engineering and AI for the real world.
We take off your hands the report someone rebuilds every week, the number nobody trusts, and the process stuck waiting on a person. From diagnosis to production.
Agreed before we start
Six lines of work
Each one solves a specific problem. All of them start at the same place: a free diagnostic.
Does your brand show up when people ask ChatGPT?
When search shows an AI summary, only 8% of queries end in a click, against 15% without one. We place your brand inside those answers, with a measured method and a free diagnostic.
Automated reporting
That report someone rebuilds by hand every week gets assembled, checked and delivered on its own — at the time you choose.
collection · consolidation · scheduled deliveryMarket monitoring
Competitor pricing, portal data and catalog changes arrive every day, with nobody having to open a website.
automated collection · alerts · historyData diagnosis
When every report shows a different number, we trace where each field comes from and define a single source of truth.
lineage · validation · single sourceDedicated sprint
A full day of engineering for when something broke and is already costing money. Closed scope, firm deadline, delivery by end of day.
closed scope · 1 business day · firm deadlineAI agents
Processes that run end to end under your rules: they read documents, decide and record — without relying on a generic template.
orchestration · RAG · integrationCRM with AI agents
Support that qualifies, replies and records every lead in the CRM, with automatic follow-up and the full conversation history.
WhatsApp · qualification · follow-upHow we work
A clear process from diagnosis to production delivery.
Diagnosis
We map the current process, where it stalls and what must leave someone's hands. Free technical conversation.
Scope and deadline
We design the solution with a defined stack, fixed price and delivery date — all agreed before we start.
Implementation
Delivered with full visibility. Tests, documentation and the code in your hands from day one.
Follow-through
The routine runs on its own and monitored. If it fails, you know before your client does — and the next window fixes it.
The cost of living with bad data
Three figures from public sources, with year and origin. None of them are ours.
Poor data quality costs organizations at least $12.9 million a year on average.
Gartner, 2020 research, Data Quality topic page · gartner.com
$12.9M per year, on average
That is what poor data quality costs an organization, at least, according to Gartner (2020). The qualifier is Gartner's own.
59% do not measure
Of organizations do not measure the quality of their own data, according to Gartner. Without measuring, there is no way to know what the problem costs.
39% of time on preparation
The share of time data professionals spend preparing and cleaning data, more than training, selecting and deploying models combined (Anaconda, 2021).
An honest caveat: the Gartner research is from 2020 and its methodology is not public. We treat it as an order of magnitude, not as precision. It is the same standard we apply to the numbers we deliver.
Which process do you want off your hands?
Fill in the fields and our team will reach out on WhatsApp to schedule the diagnostic.
WhatsApp Opened!
Your message is ready. Just send it on WhatsApp to talk to our team.
Integrated Ecosystem
Your sources come in on one side, the pipeline standardizes them, and the data comes out ready on the other — for reports, agents and the CRM. Click a node to run its command.