Private AI Analytics + Technology Consulting
For teams working with sensitive business and institutional data: ask better questions, inspect the evidence behind answers, and control which models can receive your information.
YOU
Which regions contributed most to sales?
ASSISTANT
West led the period at $1.2M. The query and returned rows remain available in the Work panel.
Illustrative interface with fictional data
PRIVATE AI ANALYTICS
Private AI Analytics · two independent products
EntailDB turns questions into reviewable database analysis. Ardua AI controls which models may receive the data and records the decisions it governs. Each product can stand alone; together they form Private AI Analytics.
ENTAILDB
Explore connected databases in plain language, refine the analysis through conversation, and open the Work panel to inspect SQL and rows.
ARDUA AI
Ardua AI applies your data-handling rules to model requests routed through it. Choose where processing may happen and keep a record of those decisions.
Ask follow-up questions when a fixed report cannot anticipate the next one.
See the query and returned rows behind the answer instead of accepting a black box.
Use local or approved cloud models according to the data and the task.
Review the handling decisions Ardua AI governed and why they were made.
Evidence before commitment
Start with the worked example and technical papers, then define the evidence your own evaluation needs.
See how repeated order totals turned revenue growth into an apparent decline, with the faulty and corrected figures side by side.
Examine the example →The fidelity paper describes evaluations across PostgreSQL, MySQL, SQL Server, and MongoDB, including the data used, observed failures, and limits. The findings apply to the tested configurations and questions.
Read the evidence and limits ↗Use questions with known answers and an agreed data-handling policy to assess whether the products fit your work.
See what an evaluation includes →A failure worth catching
In a product exercise using sample data, a faulty AI-generated query reported a $54.32 million drop in bike revenue. Revenue actually grew by $2.15 million. See how counting the same sale repeatedly reversed the result—and where EntailDB helps.
See the mistake and corrected calculationPublic-sector and data-sovereign deployment
Ardua AI supports deployments in which protected information, policy decisions, and eligible model processing remain on infrastructure an institution controls. Local inference can reduce dependence on recurring per-use charges, while approved cloud models remain available when policy and economics justify them.
Explore data-sovereign deploymentPrivate AI consulting
Start with one agreed data source and a small set of questions your team can check against known answers. Evaluate the analysis and data-handling controls before deciding whether to expand.
Discuss a Private AI Analytics engagementAgree on one data source, representative questions, and what a useful answer must show.
Connect a read-only account and configure the agreed model and data-handling rules.
Compare answers with reference results, inspect queries, and test the agreed controls.
Receive written findings, observed limits, and recommendations for a next step.
A data owner who can explain the records, representative questions and reference answers, and read-only access to the agreed database. We agree how access will work before connecting.
A configured evaluation, reviewed example answers with query evidence, results of the agreed control checks, and a written assessment of fit, limitations, and deployment options.
We agree the duration and deliverables after reviewing access, data complexity, and model requirements. Local hardware is assessed as part of that scope; there is no assumed GPU purchase.
Consulting
Ardua advises organizations on complex systems where technology, governance, operating practice, and public accountability have to work together.
Use-case selection, data classification, model-routing policy, deployment design, and production-readiness assessment.
Local-first and controlled-hybrid architectures, model infrastructure, governed capabilities, and integration planning.
Architecture, procurement support, implementation oversight, and modernization for IFMIS and related platforms.
Government platforms, central-bank payment systems, institutional reform, quality assurance, and capacity building.
Experience
The best technology decisions account for the institution that must operate, govern, and defend them.
Jim M. Ramsey
Founder and Principal Consultant
Jim brings more than four decades of experience in systems architecture, database design, public financial management, central-bank infrastructure, and international development. His work spans ministries of finance, central banks, securities regulators, and multilateral institutions.
His recent work includes co-authoring an IMF white paper on digital innovation for public financial management. He now applies that same concern for governance and operational reality to private AI systems.
Start a conversation
Whether you are evaluating private AI, modernizing a financial system, or working through a difficult architecture decision, we would like to hear about it.
contact@ardua.com