Answers that cannot be verified.
With no source cited, nobody can defend the result to a client or an auditor.
AI by product phase · The AI inside
AI built in, not as decoration.
AI goes in where it multiplies the product and stays out where it gets in the way. Every system with its source, its cost limit and its way of checking that it still works well.
AI bolted on with no control.
Cost that runs away.
Model calls with no limit and no measurement. The bill shows up at month end.
No way of knowing if it works.
The model changes, quality drops and nobody notices until a client complains.
Four concrete deliveries.
Model-agnostic by design: if tomorrow there is a better or cheaper one, it gets swapped without rewriting the product.
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01
Agents connected to real data
They query your system, not their memory. An answer with the source that supports it.
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02
Cost control per call
Routing, logging and limits per function. Visibility of the spend before the invoice.
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03
Continuous evaluation
A set of cases run on every change. If quality drops, it fires before production.
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04
Compliance and traceability
A log of automated decisions, GDPR and the AI Act considered from the design.
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Documented AI systems
What each one does, with which data and what limits it has.
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Cost and usage panel
Consumption per function and per client, live.
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Evaluation suite
Real cases that verify quality on every release.
What we bring from what is already running.
The closest thing to a case of our own here is Buscafact: a system that reads official tender documents, cites the source of every statement and has a cost ceiling per query. ADG does the same over public indicators. The hard part of putting AI inside a product is not calling the model — it is being able to prove the answer comes from real data and that the bill does not explode the month usage triples.
Highest-impact use cases
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01
Wired to real data, with the source in view
The system answers by querying your data and links the record behind it. With no source cited, the answer does not go out.
It is what separates a demo from something usable.
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02
Cost ceiling per call and per month
A budget per operation, with a cut-off and a warning before it is crossed. The cost of AI stops being a month-end surprise.
Makes predictable what almost nobody makes predictable.
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03
Continuous evaluation against real cases
A set of cases with known answers runs on every change. If the model gets worse, it shows before a customer sees it.
Catches silent degradation.
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04
A trace of what was asked and answered
An auditable record of input, output, source and cost. It is the difference between explaining an automated decision and shrugging.
A requirement, not an extra, once regulation is near.
What it is built with.
Before you call us, read this.
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What auditable AI (AI without a black box) is and why your company needs it
Everyone says "we use AI". Almost nobody explains how it decides. That difference, between a black box that spits out answers and a system you can audit, is what separates a reliable tool from a legal risk with good marketing. This is what auditable AI really means.
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What an AI agent is and when your company needs one
An AI agent is not a chatbot. It is an autonomous system with a bounded role, controlled access to tools and an auditable log. This is the honest definition, no hype.
- Discovery Which problem deserves software and which does not.
- Design Architecture, flows and data model before the code.
- Build Code with tests, reviewed and signed off by an engineer.
- Validation Security, performance and accessibility before shipping.
- Operation and scale Live, measured and with the internal team in charge.
Not sure where to start? Measure your organisation’s AI maturity for free in 3 minutes, or book 30 minutes of direct diagnostic.
Let's start
Let's talk about your operation.
Tell us what costs more than it should. We come back with a concrete plan, realistic timelines and a clear yes or no.
contacto@plantekia.com