Generative AI that solves real processes
We go beyond the chatbot: we build agents and platforms with Gemini and Vertex AI that automate end-to-end processes, with cited, auditable answers.
What we solve
- AI pilots that never reach production.
- Fear of hallucinations in regulated environments.
- Sensitive data that cannot leave the organization.
- Manual processes that drain your teams.
What we deliver
AI agents with ADK
Specialized agents that interview, analyze, propose, and execute — orchestrated with the Google Agent Development Kit.
Sovereign RAG
The AI answers using your documents as the source (that is RAG), searching by meaning rather than exact words; the semantic index (embeddings) lives in your database and nothing leaves your control.
Evidence-backed answers
Every answer cites its source: document and page, ticket, record, or the exact second of an audio/video file.
Multimodal
Text, images, audio, and video as analysis inputs.
Versioned prompts & auditing
Change control over AI behavior, with full logs and rollback.
Human-in-the-loop
AI suggests; your team decides. Confirmation flows for sensitive actions.
live demo — data sovereignty
Sovereign anonymizer
Paste a document with personal data and watch it get redacted in real time. Detection runs entirely in your browser: neither the text nor the findings ever touch a server — not even ours.
nuup://anonymizer — runtime: local browser
network: 0 requests0
02 · document — live redaction
03 · detection log
- CURP 0
- RFC 0
- NSS 0
- CLABE 0
- PHONE 0
- EMAIL 0
- AMOUNT 0
anon-engine v1 :: mx-pii rules loaded (7 patterns)
network :: disabled by design — 0 sockets
ready. awaiting document…
Rule-based detection (regex), no AI — and precisely because of that, nothing is ever sent to any server. The sample letter is 100% synthetic: every name, ID and account is fictitious.
How we work
- 01
Discovery
We find where generative AI pays off: the process, its sources of truth, and how we will measure accuracy.
- 02
PoC with real data
A working proof of concept in 4–6 weeks, using your documents and systems.
- 03
Product in production
Iterative development with continuous delivery until it runs in your environment.
- 04
Operation & improvement
Continuous model evaluation in production and knowledge transfer to your team.
Technologies
- Gemini
- Vertex AI
- ADK
- Document AI
- Cloud Run
- PostgreSQL + pgvector
- Next.js
Education · Nationwide assessment
Generative AI for designing nationwide assessments
A national educational assessment institution generates thousands of exam items with generative AI aligned to its methodology.
Supreme audit · Federal public sector
Featured caseAI-powered financial auditing: from 10 months to hours
A supreme audit institution automated the massive analysis of financial documentation with AI on Google Cloud.
Justice · State public sector
AI analysis of court hearings: from courtroom video to the minute in minutes
A state justice institution turns every recorded hearing into a speaker-attributed transcript, a minimum record, metrics, and a legal assistant — with court staff doing nothing beyond registering the session.
Related solution
AI Evidence Analysis
Multimodal investigation intelligence: video, audio, and documents with exact citations.
Related solution
Procedure Simplification
AI agents that simplify government procedures end to end.
Frequently asked questions
Can the AI make up answers?
Our platforms answer with verifiable citations to the source, apply confidence thresholds, and keep humans in the loop for sensitive decisions. If there is not enough evidence, the AI says so.
Where does my data live?
In your own cloud project or on your servers. Embeddings and the semantic index are stored in your database; nothing leaves your control.
How long does a proof of concept take?
Typically 4 to 6 weeks, working with real data from your operation and success metrics agreed from day one.
Let's talk about your next project
Tell us your challenge and we'll propose a concrete path — no strings attached.