Our mission
Every enterprise AI comes with an asterisk: your data leaves your perimeter. We are the asterisk that works in your favour — synthetic replicas, simulation and private models, inside your walls.
Sovereignty is not a feature. For highly regulated enterprises.
Deployed in production inside regulated environments
Trusted by market infrastructure, banks, insurers and the technology partners that serve them.
THE SHIFT
Sovereign AI by 2030
30–40% of all worldwide AI spend
McKinsey
CAGR, synthetic data
Market growth through 2030
MarketsandMarkets · Fortune BI
Sovereign cloud, YoY
US$ 80B already in 2026
Gartner, Feb 2026
AI infrastructure in 2026
+56% year over year
IDC, Apr 2026
One AI agenda, a fragmented estate
Legacy cores, dev, staging and production. Structured and unstructured data scattered across environments that AI must serve but data cannot safely cross.
Two AI stacks, one bottleneck
Classic ML/DL for credit and fraud, and GenAI agents and copilots, both need data to train, fine-tune and evaluate. Data is the fuel for using AI and for improving it.
The privacy wall
LGPD, GDPR and banking secrecy make real data unusable outside production. Manual masking breaks referential integrity.
Enterprise scale is where DIY dies
Hundreds of interlinked tables, auditors and regulators watching. In-house scripts stall at pilot. The problem needs infrastructure, not a tool.
THE PLATFORM
Five stages, one continuous pipeline. Every stage produces a governed asset, and nothing crosses your wall.
Stage 01
Secure connection to every source and environment: legacy cores, lakes, streams and APIs, with zero copy outside the perimeter.
Stage 02
Anonymise, or synthesise with fidelity you can measure, at production scale.
Stage 03
Train ML/DL or fine-tune foundation models on protected data, in-perimeter.
Stage 04
Put GenAI, agents and copilots to work on governed enterprise context.
Stage 05
The platform manages data, models and results end to end, with evidence.
USE CASES
Eight patterns we build on the same governed foundation.
A full, referentially intact copy of your production environment, generated on demand and refreshed as the schema drifts. QA, homologation, capacity and stress testing run against data that behaves like the real thing and carries none of its risk.
Generate the cases your history barely contains: fraud patterns, market shocks, adverse claims, edge-case volumes many times production scale.
PII removed or replaced while keys, joins and business rules stay intact, so the dataset is still usable downstream.
Credit, fraud, churn, claims and demand models trained in-perimeter on synthetic and real blends, with an evaluation harness per use case.
Foundation models deployed locally and fine-tuned on your context, with RAG over governed data, guardrails and full audit trails.
Signed fidelity and privacy reports, lineage and approval history, produced as a by-product of the pipeline rather than a project at the end of it.
Move a governed dataset to a vendor, a partner, an auditor or an academic programme without moving the original.
Automated profiling of tables, relationships and business rules, turning tacit knowledge held by a handful of people into a documented, governed catalog.
INDUSTRIES
We start where the constraint is hardest, and the payoff is largest.
Exchanges, clearing, depositories and post-trade. Capacity testing and scenario simulation on environments where a copy of production is not an option.
Core banking, credit, collections and channels. Test data and model training across estates governed by banking secrecy.
Underwriting, claims and fraud. Rare-event generation where the loss history is too thin to train on.
Clinical, operational and beneficiary data. Research and AI on cohorts that can never leave the institution.
Contracts, proceedings and case files. Private models over confidential corpora, with the corpus staying put.
ARCHITECTURE
Eudalia is installed where your data already lives.
On-premises, your private cloud, your VPC, or fully air-gapped. OCI, AWS, Azure and GCP.
Privacy by design, with re-identification risk measured on every generation, not asserted.
Versioning, lineage, drift detection, human approval gates and reports mapped to LGPD, CMN and CVM requirements.
Use your own LLM and your own keys for enrichment. Models fine-tuned in-perimeter stay in-perimeter.
Your perimeter
Data environments
Eudalia platform
Data, Models and Governance
Business applications
ENGAGEMENT
A scoped use case first. Recurrence once it is proven. Scale on the same governed base.
PHASE 01
A closed scope, a defined deadline and a measurable result on one priority use case, delivered into production inside your perimeter.
proven value
PHASE 02
What was proven becomes continuous operation: a dedicated team and modules, an agreed service level and a predictable cost.
stable operation
PHASE 03
New areas, new use cases and scale, always on the same governance base validated in the first delivery.
continuous scale
TALK TO US
Tell us the scope you want to test. We reply with a scoped conversation, not a generic pitch.