01 · DATA
Prepare enterprise knowledge
Clean, structure and organize documents, terminology and domain information for retrieval, grounding, model adaptation and testing.
Explore Data for AIPrivate GenAI · Private AI Assistants
Private AI assistants grounded in your enterprise knowledge
Build secure multilingual AI assistants that answer from approved organizational knowledge while controlling access, protecting sensitive information and evaluating production behavior.
ECOChat
Private AI grounded in your own information
Connect approved documents, repositories and enterprise knowledge to a secure multilingual assistant designed around your users, permissions and information environment.
Grounded knowledge Role based access Multilingual AI
A Representative Vendor in the 2024 "Market Guide for Data Masking and Synthetic Data"
A Sample Vendor in the 2023, 2024 "Hype CycleTM for Natural Language Technologies"
From enterprise data to trusted answers
A private assistant begins with the knowledge it is allowed to use
Enterprise knowledge is rarely ready for AI. Documents, policies, websites, repositories and terminology need to be prepared, protected and connected before an assistant can use them reliably.
Pangeanic brings those layers together so private assistants work from governed information rather than an uncontrolled pool of corporate data.
02 · PRIVACY
Protect sensitive information
Detect, mask or anonymize personal and confidential information before protected content reaches retrieval or generative AI workflows.
Explore Data Masking03 · KNOWLEDGE
Retrieve approved sources
Connect assistants to authorized repositories and retrieve the information relevant to each user, task and access policy.
Explore Enterprise RAG04 · TRUST
Evaluate assistant behavior
Measure retrieval quality, grounded response accuracy, multilingual performance and failure patterns before broader production use.
Explore AI EvaluationNeed additional training or evaluation data?
Internal knowledge does not always cover every language, domain or evaluation scenario. Pangeanic also provides ready to license and bespoke multilingual and multimodal datasets for model adaptation, benchmarking and testing.
Specialized assistants
One organization. Multiple assistants. One governed knowledge layer.
Different teams need different knowledge, permissions and operating rules. Private AI allows organizations to create specialized assistants while sharing common foundations for retrieval, security, evaluation and governance.
Shared AI foundation
Govern knowledge once. Use it where it belongs.
Reusable knowledge collections, access policies and retrieval services can support multiple assistants without rebuilding the entire AI stack for every department.
Knowledge
Documents, websites, repositories and approved collections.
Permissions
Users, groups, roles and assistant specific access.
Retrieval
Relevant context from authorized enterprise sources.
Governance
Logging, feedback, evaluation and controlled change.
HR
HR assistant
Give employees controlled access to policies, onboarding, benefits and internal procedures while respecting role based permissions.
LEGAL
Legal and compliance assistant
Search contracts, regulations, policies and approved legal knowledge while keeping restricted information within defined access boundaries.
OPERATIONS
Operations assistant
Help teams navigate procedures, technical documentation, supplier processes and operational knowledge across fragmented repositories.
CUSTOMER SERVICE
Customer service assistant
Provide grounded answers from approved product and support information while separating customer facing knowledge from internal operational content.
Applied R&D · SAVIA
Pangeanic has already developed a multi assistant architecture around organizational knowledge
Pangeanic coordinated SAVIA, an applied R&D project for creating specialized virtual assistants over governed organizational knowledge, with reusable knowledge collections, differentiated users, granular permissions, traceability and feedback.
Multiple assistants Enterprise RAG Granular permissions Traceability
Grounded enterprise AI
From question to grounded answer
A private assistant should know who is asking, what information that user is allowed to access and which sources can support the answer. Retrieval, privacy controls and evaluation sit between the prompt and the response.
01 // USER
Identify context
User, role, language, department and task establish the operating context for the request before enterprise knowledge is accessed.
Context first
The same question can require different information depending on who asks it and why.
02 // ACCESS
Apply permissions
Identity and access policies determine which repositories, collections and sources the assistant is authorized to consult.
Controlled visibility
HR, legal, operations and external users do not need access to the same knowledge.
03 // RETRIEVAL
Find relevant knowledge
Enterprise RAG retrieves the information required for the task from approved organizational sources rather than relying only on what the underlying model already knows.
Grounding layer
Documents, repositories, websites and governed collections become usable context at query time.
04 // PRIVACY
Protect sensitive data
Personal, confidential or restricted information can be detected, masked or anonymized before it reaches downstream AI processes.
Privacy by workflow
Protection can be applied before retrieval, generation, logging or external model interaction according to the architecture.
05 // GENERATION
Generate with context
The selected model produces a response using retrieved knowledge, system instructions, domain terminology and the context of the task.
Model flexibility
The assistant architecture does not have to depend on a single public model or provider.
06 // TRUST
Evaluate and trace
Responses can be evaluated, logged and returned with supporting sources so organizations can inspect performance and identify failure patterns.
Production evidence
Retrieval quality, grounded accuracy and multilingual behavior can be measured rather than assumed.
From prompt to controlled workflow
The language model is only one component of the system
Private GenAI combines governed knowledge, permissions, retrieval, privacy protection, model execution and evaluation. The objective is not simply to generate an answer, but to control how that answer is produced and what evidence supports it.
Deployment under your control
Private AI should fit the environment, not force the environment to fit the model
The right architecture depends on the information being processed, the systems already in place and the level of external connectivity the organization permits. Private AI assistants can therefore be designed for dedicated cloud, on premises and isolated environments.
01 // PRIVATE CLOUD
Dedicated environments with cloud flexibility
Run assistants in dedicated infrastructure while retaining the scalability, integration options and operational flexibility of cloud deployment.
Enterprise knowledge Controlled access API integration
02 // ON PREMISES
AI inside your own infrastructure
Models, retrieval services and enterprise integrations can operate within infrastructure controlled directly by the organization, keeping sensitive workflows close to the systems that own them.
Local models Internal repositories Data control
03 // ISOLATED
AI for restricted environments
Where external connectivity is prohibited or tightly constrained, assistants can be designed around locally available models, controlled knowledge sources and isolated infrastructure.
Air gapped Restricted networks Local inference
Production proof
Secure deployment is already part of Pangeanic’s production history
Private GenAI builds on deployment disciplines Pangeanic has already applied to multilingual AI systems: controlled infrastructure, specialized models, governed data flows and integration with operational environments.
01 // VERITONE · IRON BANK
Language AI prepared for a U.S. Department of Defense environment
Pangeanic prepared customized neural language technology for Veritone for deployment through the U.S. Department of Defense Iron Bank environment, extending an earlier relationship in which Pangeanic technology had already been integrated into private enterprise infrastructure.
Demonstrates
Specialized models, controlled software delivery, private infrastructure and multilingual processing in security sensitive environments.
02 // SPANISH TAX AGENCY
Dedicated AI infrastructure for public administration
Spain’s Tax Agency uses Pangeanic’s ECO platform in a dedicated SaaS environment to provide document translation services across a large, geographically distributed public administration.
Demonstrates
Dedicated infrastructure, authorized institutional users, high volume document processing and controlled multilingual workflows.
From private language AI to Private GenAI
The models have changed. The control problem has not.
Earlier deployments centered on neural language technology. Today the stack may include RAG, generative models, small models, agents and enterprise knowledge systems. The operational disciplines remain familiar: control where data moves, which models can use it, who can access the system and how performance is evaluated.
DEPLOYMENT WITHOUT DOGMA
Choose the degree of sovereignty your organization actually needs
Private GenAI does not require every organization to build the same fortress. An enterprise knowledge assistant, a public administration handling sensitive documents and an isolated security environment may all require different combinations of models, infrastructure, access controls and human oversight.
The appropriate architecture should follow the sensitivity of the information, operational constraints and acceptable level of external dependency.
LEVEL 01 · CONTROLLED CLOUD
Governed enterprise AI
For organizations that want the productivity of generative AI while retaining stronger control over data access, enterprise knowledge, retrieval and model usage.
Typical fit: internal knowledge assistants, document intelligence, regulated workflows and multilingual corporate search.
LEVEL 02 · PRIVATE INFRASTRUCTURE
Private cloud or on premises AI
For enterprises, public administrations and regulated organizations that need models, retrieval systems and sensitive information to remain inside controlled infrastructure.
Typical fit: public sector, financial services, legal information and sensitive enterprise repositories.
LEVEL 03 · ISOLATED
Air gapped and restricted AI
For environments where external model calls or continuous connectivity are incompatible with operational security, information sensitivity or infrastructure policy.
Typical fit: defense, law enforcement, critical infrastructure and other environments with strict operational boundaries.
Grounded enterprise AI
From question to grounded answer
A private assistant should know who is asking, what information that user is allowed to access and which sources can support the answer. Retrieval, privacy controls and evaluation sit between the prompt and the response.
01 // USER
Identify context
User, role, language, department and task establish the operating context for the request before enterprise knowledge is accessed.
Context first
The same question can require different information depending on who asks it and why.
02 // ACCESS
Apply permissions
Identity and access policies determine which repositories, collections and sources the assistant is authorized to consult.
Controlled visibility
HR, legal, operations and external users do not need access to the same knowledge.
03 // RETRIEVAL
Find relevant knowledge
Enterprise RAG retrieves the information required for the task from approved organizational sources rather than relying only on what the underlying model already knows.
Grounding layer
Documents, repositories, websites and governed collections become usable context at query time.
04 // PRIVACY
Protect sensitive data
Personal, confidential or restricted information can be detected, masked or anonymized before it reaches downstream AI processes.
Privacy by workflow
Protection can be applied before retrieval, generation, logging or external model interaction according to the architecture.
05 // GENERATION
Generate with context
The selected model produces a response using retrieved knowledge, system instructions, domain terminology and the context of the task.
Model flexibility
The assistant architecture does not have to depend on a single public model or provider.
06 // TRUST
Evaluate and trace
Responses can be evaluated, logged and returned with supporting sources so organizations can inspect performance and identify failure patterns.
Production evidence
Retrieval quality, grounded accuracy and multilingual behavior can be measured rather than assumed.
From prompt to controlled workflow
The language model is only one component of the system
Private GenAI combines governed knowledge, permissions, retrieval, privacy protection, model execution and evaluation. The objective is not simply to generate an answer, but to control how that answer is produced and what evidence supports it.
Multilingual private AI
Ask in one language. Retrieve knowledge in another.
Enterprise knowledge rarely exists in a single language. Policies may be written in English, technical documentation in German, customer information in Japanese and local procedures in Spanish. A private assistant should be able to work across that knowledge without forcing the organization to translate everything first.
Multilingual by architecture
Language should not become another enterprise data silo
Pangeanic combines multilingual retrieval, language technology, terminology control and generative AI so users can interact with organizational knowledge in the language that makes sense for them.
The assistant can retrieve relevant information across languages, preserve the connection to the original source and generate an answer in the user's working language.
Multilingual retrieval Enterprise terminology Cross language knowledge Private inference
01 // RETRIEVE
Search beyond the query language
A question in one language can surface relevant enterprise knowledge written in another, allowing multilingual organizations to use information that would otherwise remain difficult to find.
Explore Enterprise RAG02 // UNDERSTAND
Preserve domain terminology
Product names, legal expressions, technical vocabulary and organization specific terminology can be incorporated into the language layer rather than left to generic model behavior.
03 // ADAPT
Generate for the user, not for the source document
Retrieved knowledge can be transformed into useful responses in the user's language while maintaining the meaning, terminology and context of the underlying enterprise information.
Explore Deep Adaptive AI Translation04 // TRACE
Keep the original evidence visible
Multilingual generation should not hide where an answer came from. Retrieved sources can remain available for verification even when the final response is produced in another language.
One knowledge base. Many users.
Multilingual access without duplicating the knowledge layer
A Spanish employee can ask about a procedure documented in English. A Japanese engineer can retrieve information from European technical documentation. A customer service team can work from product knowledge created across several markets.
The objective is not merely translation. It is to make enterprise knowledge usable across languages while preserving control over sources, terminology and access.
Enterprise integration
Built around your systems, not around ours
A private AI assistant should become part of the organization’s information environment rather than create another isolated platform. Architecture, models, knowledge sources and deployment choices can be adapted to the systems and controls already in place.
01 // KNOWLEDGE SOURCES
Connect existing enterprise knowledge
Documents, internal repositories, websites, structured information and approved knowledge collections can become part of the retrieval layer without requiring teams to rebuild their information estate.
Explore Enterprise RAG02 // DOCUMENTS
Turn document collections into usable knowledge
Private Document Intelligence can help extract, structure and prepare information from document intensive environments before that knowledge reaches retrieval and generative AI workflows.
Explore Private Document Intelligence03 // MODELS
Choose the model strategy that fits the task
Private AI does not need to revolve around one external model. Architectures can combine local models, specialized small models and approved external models according to the task, risk profile and infrastructure available.
04 // APPLICATIONS
Integrate AI into existing workflows
Assistants can sit behind enterprise applications, internal portals and business processes through controlled services and APIs instead of forcing users into a disconnected AI destination.
05 // PEOPLE
Keep human expertise in the loop
Domain experts can review outputs, identify failure patterns, validate terminology and provide feedback that improves the assistant as real production use reveals new requirements.
Explore AI Data Operations06 // EVOLUTION
Change components without rebuilding the whole system
Separating enterprise knowledge, retrieval, model execution and evaluation reduces dependence on any single model generation and makes the architecture easier to evolve over time.
Sovereign by design
Keep control of the knowledge layer as AI models continue to change
Models will continue to improve. Enterprise knowledge, permissions, terminology, workflows and governance remain organizational assets. A private AI architecture should preserve control of those assets while allowing the intelligence layer to evolve.
Private AI assistants · FAQ
Questions enterprises ask before deploying a private AI assistant
Private AI is not a single deployment model. The right architecture depends on your knowledge sources, users, languages, security requirements and existing technology environment.
What makes an AI assistant private?
Privacy comes from controlling where information is processed, which models can access it, who can retrieve particular knowledge and what data can leave the organization. Depending on the requirements, this can involve dedicated cloud infrastructure, on premises deployment, local models or isolated environments.
Can a private AI assistant use our existing documents and repositories?
Yes. Enterprise RAG can connect approved documents, websites, repositories, structured information and internal services to a governed retrieval layer. The objective is to make existing organizational knowledge usable by AI rather than create another disconnected information silo.
Do we have to use a specific language model?
No. The architecture can be designed around locally deployed models, specialized small models, approved external models or combinations of them. Model choice should follow the task, infrastructure, performance requirements and level of control required by the organization.
Can the assistant work with enterprise knowledge in several languages?
Yes. Pangeanic's multilingual architecture can retrieve relevant knowledge across languages and produce responses in the user's working language. Domain terminology and the connection to the original source can remain part of the workflow.
How do you reduce unsupported or inaccurate answers?
Retrieval grounding, controlled sources, system instructions, source traceability and evaluation can all be used to reduce unsupported responses. Production testing should measure retrieval quality and grounded answer accuracy rather than assuming that a model will behave correctly because it performs well in general benchmarks.
How does a private AI assistant project start?
We normally begin by identifying the users, business tasks, knowledge sources, languages, permissions, deployment constraints and measurable success criteria. A focused implementation can then validate retrieval and assistant behavior before the architecture is expanded to additional knowledge, users or specialized assistants.
Build your private AI assistant
Start with the knowledge your organization already owns
Tell us who needs the assistant, what knowledge it should access, which languages it must understand and where the system needs to operate. We can help define a private AI architecture around your existing information, security requirements and business workflows.
Enterprise RAG Private cloud On premises Multilingual AI Model flexibility
Talk to Pangeanic
Discuss your use case with our AI team
Share your knowledge environment, deployment constraints and intended users. We will help identify the most practical route from enterprise information to a controlled production assistant.