SAIESovereign Artificial Intelligence Ecosystem
A ready-to-use workspace that brings together organizational knowledge, assistants and AI models with control over data, access, costs and legal compliance
COMPETENCE DEVELOPMENT PROGRAMME · FOR PUBLIC AUTHORITIES
NASK researched it. The Ministry of Digital Affairs recommended itWe answer — point by point
Documents, knowledge bases, RAG, processes and specialized AI assistants meet in one workspace
SAIE for institutions and businesses
TWIN:DESK supports teams in their daily work, increasing productivity and use of organizational knowledge
SAIE is for organizations that must know where their data goes because a regulator, a contractual clause or plain risk discipline requires it. Banks, public institutions, energy companies, hospitals, telecom operators and defense organizations. You decide which model works on which data: your own model, one running in your environment, or an external model within the scope you define
We built SAIE for the three people in an organization who must agree before an AI deployment can move forward: the executive accountable for business outcomes, the security leader accountable for data and the compliance team accountable to the regulator. Each usually says no for a different reason. SAIE answers all three at once
We built SAIE for teams that see every day how much time is lost on work AI can take over: document analysis, preparation of materials and searching distributed knowledge. And they want to recover that time in a way that passes every audit. Efficiency and compliance do not have to stand on opposite sides. In SAIE, they work together
If you are looking for a simple website chatbot, cheaper options exist and we will gladly point them out. SAIE starts to make sense when model selection is governed by organizational policy rather than a vendor default, and when deployment goes through a risk assessment before anyone sees the first result. That means banking, public administration, energy, defense, healthcare and telecommunications
Some organizations are waiting for regulations to settle. Others are building now so they are ready from day one, and that second group will set the standard in its industry. We provide architecture in which the model is replaceable and a methodology that leads from pilot to scale. You add the knowledge of your business
SAIE is for organizations that want to deploy AI in a way they can stand behind. It is clear who authorized an action, which data the system used and why it made a decision, not to assign blame but to move forward with confidence. That confidence is what lets deployment expand instead of getting stuck at the pilot stage
The system enables company knowledge to be flexibly shaped and shared across projects, workspaces, and knowledge bases
The system integrates knowledge from folders, e-mail, calendars, and Teams without requiring data to be uploaded to a central RAG system
Documents, prompts, context, and responses are stored in a location designated by the customer—on-premises, in colocation, or in the cloud—as decided by the customer
The system operates in accordance with the EU AI Act, GDPR, DORA, NIS2, and cybersecurity requirements
All communication with LLMs passes through a central policy, security and routing gateway, enabling consistent enforcement of ABAC/RBAC rules, MCP tool control, auditing, and access management at the user, role, team, and tenant levels
The system enables advanced AI assistants to be created using natural language
The system complies with applicable regulations and is ready for ISO 42001 certification. It produces compliance evidence. Compliance with the standard has been confirmed by certification
Ready-to-use software. Deployment takes no more than 10 business days with a ready-to-use on-premises architecture
The system combines a central proprietary RAG knowledge base, OCR, metadata-based document filtering, a live view of sources used in each response, and a “knowledge gap” mechanism, allowing it to identify missing context instead of generating uncertain content
The Digital Twin represents a user with persistent memory and controlled personalization. The solution provides persistent user memory, a cognitive profile, a learning journal, memory export, synchronization with e-mail, calendars and messaging platforms, and explicit mechanisms for editing, deleting and resolving memory conflicts
Choose LLMs to match data sensitivity, tasks, and target costs. The system supports any model from more than 100 providers, switching models during a session without losing context, consulting several models at once, comparing results in a split view, and automatically switching to fallback models when a provider is unavailable
The system measures the cost of every request by user, team, project, and model; supports hierarchical budgets, alerts, internal currency, and token logs; and protects against excessive spending through denial-of-wallet safeguards
The solution supports automations, schedules, natural-language if/then rules, A2A delegation, multi-step process orchestration, and DAG-based rollback of agent actions with a visible rollback plan and user confirmation
Integrate the platform with your business and technology processes. The system works with Microsoft 365, Google, Outlook, Teams, Drive, OneDrive, GitHub, Jira, n8n, Langflow, Codex, CloudCode, Kubernetes, Helm, GitOps/IaC, and VSCode/Cursor development environments
Browse the complete catalog of business, administrative and technical platform capabilities
100% security: you will receive the software source code and training in its maintenance and development
You do not waste time stabilizing and adapting open-source solutions
You receive a fully functional platform and the essential know-how from day one, included in the price
SAIE develops on a quarterly rhythm, and we set priorities based on conversations with clients, lessons from implementations and PoCs. We use them to check whether the technology is ready for production: whether it can withstand cost pressure, produce repeatable results and explain its decisions to an auditor. Some PoCs are run together with clients, on their data and processes. If you have a problem we should know about before the next planning cycle, write to us
Creating effective system instructions and workspace settings requires experience that not every process owner possesses
A first encounter with a complex application can be overwhelming, delaying productive use and increasing support requests
Meeting decisions must be copied manually into the task system, resulting in delays and missed commitments
Token consumption by tools running on employee computers remains outside centralized cost reporting and limit management
Without named checkpoints, it is difficult to return to an approved version, compare changes, and identify the currently binding document
Meeting recordings are time-consuming to review, and without separating speakers it is difficult to assign decisions and commitments
Multilingual teams spend time on manual translation and struggle to maintain consistent industry terminology
Long and complex documents require navigation through structure and dependencies that simple text chunking does not preserve
Creating cross-project summaries from multiple sources requires manual data consolidation and format standardization
Calculations performed solely by a language model can be inaccurate and difficult to reproduce, especially in reports subject to audit
Before launching a solution, it is difficult to forecast model costs and identify a configuration that delivers the required quality within budget
Executives need a concise view of AI costs, risks, quality, and usage without analyzing operational reports
Disconnected environments need a controlled local catalog of model packages, versions, and compliance information
In large organizations, documents vary in importance, freshness, and scope, so AI responses must select the right sources based on the user's role and the context of the matter
Traditional context search loses the relationships between documents, concepts, and agent outputs, resulting in fragmented answers that are difficult to justify
Building integrations and interface components usually requires development work, which delays the rollout of new capabilities for business users
A single shared infrastructure makes it difficult to isolate customers, teams, or workloads with different security and performance requirements
Agents and applications built outside the core product need secure, consistent access to search across corporate knowledge
High volumes of scanned documents create queues where urgent files compete with lower-priority jobs and processing status is difficult to track
Without recognizing the context of an individual session, it is difficult to apply the right limits, policies, and behavioral analysis to a specific conversation
In a multi-node environment, requests must reach available resources while taking workload, location, and model requirements into account
The organization needs to enforce regulatory requirements automatically during model use instead of relying solely on manual compliance procedures
Different teams and use cases require consistent rules for access, permitted models, data, and response generation
Distributed AI infrastructure requires a single place to control nodes, capacity, availability, and workload allocation
Manually collecting configuration information creates gaps, inconsistencies, and lengthy coordination at the start of an implementation
Multiple project configurations are difficult to compare, find, and reuse without an organized registry
Changes to standards and procedures require parallel maintenance of different methodology releases and clear assignment of each release to the relevant projects
Implementation knowledge is scattered across files and conversations, leaving the assistant without complete context for a given project
Project initiation is delayed by the manual collection of basic materials needed by the team and the assistant
Experts need a simple way to add missing information during ongoing work without involving a system administrator
New decisions and artifacts created during a project should enter the shared context without recurring manual imports
Copying information into forms consumes consultants' time and increases the risk of omissions or inconsistent answers
Completing extensive tables from multiple source materials is repetitive, time-consuming, and prone to errors
Project managers need a current, data-driven view of execution rather than declarations collected only before reporting
Static screens do not suit different roles and project stages, causing users to waste time looking for the right information
New users need contextual guidance through the process so they can become productive and independent more quickly
Memory, documents, activities, and agent outputs remain in separate silos, limiting the understanding of relationships and the reuse of knowledge
Text alone is not sufficient for convenient handling of forms, tables, and interactive results generated by an assistant within a conversation
Building multi-step agent processes requires specialized knowledge of step sequencing, tools, and context transfer
Ready-made agents, automations, interface components, and knowledge packages are difficult to discover and deploy again across teams
Manual agent validation covers too few scenarios and fails to detect regressions before release to users
Public institutions need to use national services and registries without building every integration from scratch
Autonomous operations must be reproducible, auditable, and reversible when a sequence of actions produces an unwanted outcome
Training content, assistant conversations, and progress measurement operate separately, making it difficult to personalize learning and identify competency gaps
Long conversations exceed model limits and increase costs even though only part of the earlier context is needed for the next response
Administrators lack a single view of which agent instances are running, what profiles they use, and whether they behave as expected
Raw logs are scattered and lack business context, making event analysis and the preparation of audit evidence unnecessarily slow
Without unified oversight, it is difficult to assess source quality, retrieval effectiveness, latency, and the causes of weak knowledge-grounded responses
Unusual user behavior and sudden changes in AI usage patterns may remain unnoticed until damage occurs
Requirements from standards and European regulations are fragmented, while teams need a consistent set of controls and audit-ready evidence
Before deploying an AI use case, its impact on people, data, processes, and the organization must be assessed consistently, with decision rationale retained
Inconsistent, incomplete, or outdated data reduces model reliability, yet data quality is often neither measured nor assigned to accountable owners
Auditors and process owners need a tamper-resistant history of decisions, configuration changes, and model usage
An ARS assessment requires extensive evidence gathering and interpretation of criteria, which prolongs the team's work without expert support
Completing the F0-AC-01 control requires consistent links between answers, documents, responsibilities, and execution status
The rationale for applying or excluding safeguards must be complete, current, and linked to evidence in order to pass a compliance review
Assessing the impact and risk of an AI system requires structured guidance through criteria and related artifacts
Managing an AI system according to the adopted methodology requires continuous access to the correct procedures, records, and implementation guidance
Traditional technical indicators do not show whether an assistant actually saves time, improves decisions, and delivers responses of the expected value
Preparing F6 reports from multiple projects and sources consumes time and makes it difficult to maintain a consistent data scope
Compliance deadlines can be missed when they depend on multiple dates, owners, and stages of a case
Users need timely reminders about planned tasks to reduce delays and the manual tracking of commitments
Access to projects and materials must reflect each user's role and responsibilities without granting excessive permissions
Rigid document fields restrict filtering, access rules, and search based on organization-specific attributes
Tools created within the product should also be available to external automation and development environments through a controlled interface
The same assistant should respond differently according to a user's permissions, responsibilities, and type without maintaining multiple separate configurations
Before an extended absence, knowledge about open matters and decision context must be transferred securely to a substitute
Tasks originate in many conversations and processes, so without a shared registry it is difficult to determine ownership, deadlines, and completion status
Automations maintained outside the product need a simple way to invoke agents, pass data, and receive results
Organizations want to make an assistant available on existing websites without rebuilding the site or creating a separate interface
Model and infrastructure consumption must be controlled by user, team, or project to prevent overload and unplanned costs
Diagnosing workstation failures requires manual review of many technical files and specialist administrator knowledge
Users waste time manually locating local files and attaching them to conversations, especially when they do not remember the exact location
A low cache hit rate unnecessarily increases cost and latency, while the cause of the decline often goes unnoticed
Unlimited or malicious requests can rapidly exhaust an organization's budget before an administrator notices the increase in usage
Repeated questions continue to consume expensive model capacity when semantic similarity is not used effectively to reuse responses
Users and auditors must understand why a request was blocked or modified instead of receiving only a refusal message
Model responses often require data masking, format validation, filtering, or enrichment before they can be passed to an application
Risks related to AI systems are scattered across documents and spreadsheets, making it difficult to track owners, actions, and exposure levels
Models move through assessment, testing, publication, and retirement, while the absence of a consistent version history makes change management unsafe
High-impact decisions require clearly defined human control points, accountability, and the ability to stop automation
Compliance evidence quickly becomes outdated and is stored in many places, increasing the cost of preparing for an inspection
An organization cannot manage risk effectively if it does not know all deployed systems, owners, models, and intended uses
A new model should be released only after the appropriate people document and approve its quality, security, cost, and compliance assessment
Declines in response accuracy, completeness, or safety may remain invisible without continuous measurement against the expected level
Users need consistent sign-in and permission recognition across components without creating multiple accounts
Protective controls and security events are difficult to oversee when configuration and safeguard status are distributed across systems
Implementation information must be recorded in a tamper-resistant way so that the process and accountability can be demonstrated later
Records with high evidentiary value require protection against deletion or modification throughout the required retention period
Field employees and executives need access to the company assistant from a phone without having to start a workstation
A lack of connectivity should not block access to previously shared knowledge and essential assistant capabilities on a mobile device
Commitments regarding assistant quality and effectiveness should be measured and accounted for in one place using consistent definitions
Messages from different processes get lost across multiple channels, so users need one view of events requiring attention
Technical support lacks current information about software installed on a user's computer, extending diagnosis and ticket handling time
Preparing documents, presentations, and spreadsheets requires switching applications and manually transferring content from a conversation
Local tools are difficult to discover and control without an inventory of their configuration, permissions, and availability
When working with long documents, users need to modify a selected section without risking unintended changes to the remaining content
Administrators need a single catalog of available models, versions, parameters, owners, and approved uses
AI-related events require coordinated registration, impact assessment, accountability, and corrective action
Creating and updating required control materials is labor-intensive when evidence and templates remain scattered
After services go live, sources of excessive spending must be identified continuously and changes recommended without reducing response quality
Employees need a simple and confidential channel for raising concerns about AI behavior, together with visibility into subsequent actions
Reports submitted by email should be recognized, organized, and assigned automatically without manual re-entry
Cases arriving through different channels require a shared record of status, communication, evidence, and corrective actions
Each report must quickly reach the correct category and accountable people to avoid inactivity or duplicated effort
Critical cases require measurable response deadlines and automatic escalation when thresholds are exceeded
The history of sensitive case handling must be resistant to modification in order to preserve the evidentiary value of decisions and actions
Required information about concerns and actions taken must be complete and available in a structure compliant with the F6-AFC-01 form
Information from reports should feed management and impact-assessment processes without manually copying data between registers
Planning, execution, and monitoring require a shared model of concepts and relationships so that artifacts are not interpreted differently by each module
FAQ
Key information about SAIE architecture, security and implementation
TWIN:DESK has an integrated Prompt Assistant that helps you formulate commands according to the rigorous, effective CRISPE-CDF prompt engineering standard
Documents are immediately marked as confidential, thanks to which the system permanently blocks the option of using them in conversations conducted using external, cloud AI
No. TWIN:DESK is a multimodal environment for working with AI: it supports voice commands, analysis of attachments, and also provides a "computer mode", allowing AI agents to independently perform tasks on behalf of the user
It uses a gradual disclosure mechanism – beginners see a very simple chat, while experts get full access to the terminal and flow automation options
TWIN:DESK enables deep integration with systems such as EZD RP and EZD PUW, as well as with e-Delivery, KSeF 2.0 and ePUAP authentication systems
It is a full-fledged everyday work environment that completely eliminates the problem of lack of institutional memory. It provides secure handling of internal documents or knowledge bases based on rag technology and provides built-in tools for automating tasks and processes
This is an innovative mode in which the question asked by the employee goes in parallel to up to five selected AI engines. The platform automatically collects their variants, analyzes them and generates one optimal final response, which translates into higher quality of decisions and greater certainty of the results obtained
With this in mind, a virtual budgeting unit called Twin:Coin was created. It allows you to hide highly complex token price lists and facilitates transparent reporting or limiting costs to specific teams
The system can operate fully proactively – using secure integration with the company's mail, task system or calendar, it automatically generates a daily schedule or proposes summaries of meetings held
Instead of making up answers (so-called hallucinations), TWIN:DESK uses the Register of Deficiencies in Knowledge. This function marks gaps in the source information, thus giving managers a precise signal as to which internal procedures or instructions need to be urgently supplemented
Yes, by creating dedicated workspaces to which you can give a unique avatar, define strict operating guidelines in the system prompte and assign only specialized tools (e.g. code interpreter for the IT department)
Digital Twin is a personal AI agent who learns how the user works and uses the accumulated knowledge, documents and history of activities to support everyday tasks. It can help automate repetitive activities, prepare recommendations and implement selected processes, while maintaining the principles of supervision and control specified by the organization
This agent allows you to use your personal sources of knowledge, such as local folders on disk, Outlook mail, Microsoft Teams instant messenger or a private calendar. The biggest advantage of this solution is that artificial intelligence can work on these documents completely locally, without the need to load and copy this sensitive data into a central, corporate knowledge base (rag)
READY FOR THE FIRST STEP?
Configure AI around your organization’s needs
No, the built-in Adaptive Configuration Engine (ACE) automatically selects the appropriate legal requirements and controls based on the organization's profile, differentiating the approach, e.g. for banks and offices
CDF is a proprietary, structured implementation plan that bridges the gap between new legal requirements (e.g. EU AI Act) and technological practice. This methodology forces the organization to build full legal compliance before the start of the solution building phase
The implementation structure includes 8 precisely described phases (from F0 to F6), which cover 12 areas of knowledge and several dozen requirements and control boards
It is an advanced indicator operating in the AI Quality Cockpit, which in the production environment constantly analyzes whether the knowledge used by the model is up-to-date
The platform has a local model (AI Advisor), which gives full data sovereignty – no analyzed documents flow beyond the secure infrastructure of the company
No. The authority or company implements the compliance building system only once, and the platform itself generates the relevant evidence and artifacts for the various regulations in force. In the case of new regulations, it may be necessary to update the relevant controls, patterns or artifacts, but this does not mean re-implementing the entire methodology from the beginning
Yes, the CDF contains preconfigured records and operational evidence that are necessary to audit and manage AI systems according to this standard
The platform natively supports the SBOM (Software Bill of Materials) model and offers ready-made compliance reporting dedicated to the requirements of this legal act
The system introduces hard decision gates based on the "Scale or Finish" principle at the end of the pilot phase. Thanks to this, projects that only burn through the budget without any chance of final production implementation are effectively eliminated
Definitely, because the built-in automated evidence package (aims) independently generates key artifacts for auditors. The tool is prepared on an ongoing basis by, among others, the mandatory Register of AI Systems and Data Protection Impact Assessment (DPIA)
For this purpose, we use three-level Cognitive SLA indicators, which check the quality of decisions, the level of hallucinations and the freshness of the model's cognitive knowledge in real time, instead of focusing solely on the technical availability of servers
The Automated Evidence Package (aims) is responsible for this, which independently and on an ongoing basis generates, among others, Impact Assessments (DPIA) and a centralized Register of AI Systems, based on the history of activities in the system
TWIN:DESK has an integrated Prompt Assistant that helps you formulate commands according to the rigorous, effective CRISPE-CDF prompt engineering standard
Documents are immediately marked as confidential, thanks to which the system permanently blocks the option of using them in conversations conducted using external, cloud AI
No. TWIN:DESK is a multimodal environment for working with AI: it supports voice commands, analysis of attachments, and also provides a "computer mode", allowing AI agents to independently perform tasks on behalf of the user
It uses a gradual disclosure mechanism – beginners see a very simple chat, while experts get full access to the terminal and flow automation options
TWIN:DESK enables deep integration with systems such as EZD RP and EZD PUW, as well as with e-Delivery, KSeF 2.0 and ePUAP authentication systems
It is a full-fledged everyday work environment that completely eliminates the problem of lack of institutional memory. It provides secure handling of internal documents or knowledge bases based on rag technology and provides built-in tools for automating tasks and processes
This is an innovative mode in which the question asked by the employee goes in parallel to up to five selected AI engines. The platform automatically collects their variants, analyzes them and generates one optimal final response, which translates into higher quality of decisions and greater certainty of the results obtained
With this in mind, a virtual budgeting unit called Twin:Coin was created. It allows you to hide highly complex token price lists and facilitates transparent reporting or limiting costs to specific teams
The system can operate fully proactively – using secure integration with the company's mail, task system or calendar, it automatically generates a daily schedule or proposes summaries of meetings held
Instead of making up answers (so-called hallucinations), TWIN:DESK uses the Register of Deficiencies in Knowledge. This function marks gaps in the source information, thus giving managers a precise signal as to which internal procedures or instructions need to be urgently supplemented
Yes, by creating dedicated workspaces to which you can give a unique avatar, define strict operating guidelines in the system prompte and assign only specialized tools (e.g. code interpreter for the IT department)
Digital Twin is a personal AI agent who learns how the user works and uses the accumulated knowledge, documents and history of activities to support everyday tasks. It can help automate repetitive activities, prepare recommendations and implement selected processes, while maintaining the principles of supervision and control specified by the organization
This agent allows you to use your personal sources of knowledge, such as local folders on disk, Outlook mail, Microsoft Teams instant messenger or a private calendar. The biggest advantage of this solution is that artificial intelligence can work on these documents completely locally, without the need to load and copy this sensitive data into a central, corporate knowledge base (rag)
This software provides a central layer of control, acting as a unified, intelligent router for various artificial intelligence models. It effectively solves the problem of the so-called "Shadow AI", securing the company against sensitive data leaks and uncontrolled costs
The tool has FinOps and Anti-DoW mechanisms that set locks for individual tenants and users, protecting, for example, against Denial-of-Wallet attacks
PROXY: The AI operates a “Human in the Decision Loop” mechanism (Human Surveillance), deliberately stopping critical processes until they are authorized by the appropriate person
The platform records in the invariable audit log (WORM) the full history of the system operation, including the model used, configuration, call costs and information needed to recreate the decision-making process. Thanks to this, the organization can demonstrate to the auditor what mechanisms led to the choice of a specific AI model
The MCP Gateway module strictly ensures that agents can only use explicitly assigned and allowed tools (e.g. Jira), while maintaining a full audit of their activities
Definitely yes. Responses returned by the LLM are re-scanned for the presence of unexpectedly generated sensitive data or instructions considered unsafe
Yes, the platform offers a "dry-run" panel that allows you to safely test ABAC rules on simulated traffic before implementing them on production
Yes, the built-in AI Firewall analyzes the transmitted data in real time, less than 50 milliseconds. It automatically detects and neutralizes information such as PESEL numbers, nip numbers and e-mail addresses before leaving the company network
The system effectively uses the Semantic Caching mechanism. Thanks to it, repetitive queries with a similar meaning retrieve ready-made answers directly from the cache, avoiding repeated and expensive polling of the main LLM models
It has specialized protective barriers that filter queries in real time and effectively block any extortion attempts, including Prompt Injection or Jailbreak attacks
The system allows for seamless separation from suppliers (Zero-Code Switch), so the organization mentions the main language model by changing only the settings in the PROXY panel:AI, without the need for expensive rewriting of the company's application code. This allows the organization to easily use the new technologies while maintaining control over the costs and risks of migration
This module does not allow blind queries – with each click it analyzes not only who asks the question, but also from what environment it does it, what data it works with and what is the general level of risk of a given operation. Thanks to this, each operation can be dynamically accepted, limited or blocked in accordance with the organization's security policies