01
No token tracking for users
Focus on the outcome, not on tokens consumed.
AI use does not have to be planned around the token cost of each request. Specialists can focus on analysing the task, the quality of the solution and the result.
ENTERPRISE AI · SOFTWARE ENGINEERING
An intelligent AI platform for developing, maintaining and managing information systems.
One workspace where organisational knowledge, system source code, architecture and specialised AI agents work alongside IT professionals — from requirements analysis and design to development, testing and maintenance.
One shared workspace where organisational knowledge, system source code and specialised AI agents work together with IT specialists.
- Shorter delivery times
- More effective teamwork
- Better-founded decisions
- Control over IT development
UNDERSTANDS. THINKS. CREATES.
DCS Flow ChatNEXUSIllustrative
User request
Analyse the architecture and source code of the document processing module and suggest improvements while keeping the integration and security requirements.
3 priority areas identified for assessment
Document status changes and validation rules are tightly coupled with the processing logic. Separating these responsibilities should be assessed.
Existing data exchange interfaces must remain compatible. Dependencies on document statuses and data structures should be checked.
It needs to be established whether authorisation rules are applied consistently across all document processing operations.
Supporting sources · 3The team makes the decision
Illustrative demo scenario. NEXUS is a fictitious project; the files, analysis findings and recommendations shown were not obtained from a real system analysis.
DCS Flow Chat
Project: NEXUS · Document processing
User request
In project NEXUS we plan to modernise the document processing module. Analyse the existing architecture and source code, identify the components that hold back its development, and recommend improvements, while keeping the existing integrations and security requirements.
- Context identified
- Source code
- Documentation
Initial analysis result
Three priority areas for assessment have been identified for modernising the NEXUS document processing module.
- 01Document processing serviceSource:
Document status changes and validation rules are tightly coupled with the processing logic. Separating these responsibilities should be assessed.
- 02Integration layerSource:
Existing data exchange interfaces must remain compatible. Dependencies on document statuses and data structures should be checked.
- 03Access right checksSource:
It needs to be established whether authorisation rules are applied consistently across all document processing operations.
- Source code
document-service/src/workflow/Source code component for document processing and status management.
- Architecture documentation
docs/architecture/integrations.mdDescription of the system’s integration and data exchange architecture.
- Security guidelines
security/access-control.mdDocumentation of access rights and authorisation rules.
Clarification needed
May the internal data model be changed during modernisation if the external integration interfaces remain unchanged? This affects the possible architecture solution.
Create the Initiative “NEXUS document module modernisation”, splitting the analysis into architecture, integration, security and testing sub-tasks.
Why DCS Flow?
AI capabilities for the whole organisation, not just individual users.
DCS Flow brings together artificial intelligence, organisational knowledge and teamwork in one software engineering environment.
Instead of each specialist using AI tools on their own, DCS Flow helps the organisation work systematically with a shared project context, retain the results of its work and evolve its information systems — involving both people and specialised AI agents.
Individual AI use
- Each specialist works in their own AI conversation
- System context has to be provided again every time
- Results stay in personal chat history
Organisational AI workspace with DCS Flow
- Colleagues and AI agents work in a shared process
- Shared project context: source code, documentation, guidelines
- Results and decisions stay with the organisation
What makes DCS Flow different
- No token tracking for usersFocus on the outcome, not on tokens consumed.
- Organisational knowledge stays in the organisationKnowledge becomes a shared, long-term resource.
- AI understands the context of your specific systemNot just general AI knowledge, but your system’s source code, architecture and requirements.
- People and AI work as one teamFrom individual AI chats to a shared work process.
What it gives your organisation
- Lower development and maintenance costsLess repetitive work. More efficient use of resources.
- Higher team productivityMore work done with existing resources.
- Less dependence on individual specialists and suppliersKnowledge stays in the organisation — even when people change.
- Reuse of knowledge and solutionsNo need to start from scratch every time — the organisation can use what it has already built.
Benefits are described as potential value. The actual effect depends on the organisation’s systems, available knowledge and the scope of implementation.
02
Organisational knowledge stays in the organisation
Knowledge becomes a shared, long-term resource.
Project documentation, source code, guidelines and approved decisions form a knowledge base available to the organisation. Information does not depend on one specialist’s personal AI chat history.
03
People and AI work as one team
From individual AI chats to a shared work process.
In the Chat, Initiatives and Board modules, colleagues can jointly analyse questions, structure initiatives and prepare tasks with AI support and a shared project context.
04
AI understands the context of your specific system
Not just general AI knowledge, but your system’s source code, architecture and requirements.
DCS Flow uses project repositories, documentation, guidelines and other available knowledge sources to help produce results that fit your specific system and are backed by sources.
05
One AI workspace for the entire system lifecycle
From the initial idea to system maintenance.
Analysis, design, development support, testing and maintenance are treated as connected engineering work rather than isolated AI requests in different tools.
06
A controlled AI environment and organisational guidelines
AI capabilities without losing control over data and engineering decisions.
DCS Flow runs in isolated infrastructure. Project rules, requirements and decisions are managed in the Steering module, while significant engineering decisions are evaluated and made by people.
What can your organisation achieve with DCS Flow?
From a new idea to evolving, maintaining and troubleshooting existing information systems.
Choose how your organisation works and see how DCS Flow helps with specific software engineering tasks.
Suitable for: Central and local government bodies, companies with in-house IT teams, large organisations with complex systems, and organisations that use external IT suppliers but want to keep control of their systems.
Analyse the need for new functionality, clarify requirements, assess the impact on existing systems and prepare implementation tasks.
Expected resultA structured initiative, requirements, solution alternatives and tasks ready for development.
Plan new functions and modernisation while respecting the existing architecture, integrations and technical requirements.
Expected resultA well-founded change proposal with identified dependencies and implementation tasks.
Manage maintenance work more efficiently and retain knowledge about how systems work.
Expected resultA clearly defined maintenance task and recommendations backed by sources.
Analyse defects by linking them to source code, architecture, database data and system logs, where these sources are available.
Expected resultAn evidence-based root cause hypothesis, a fix proposal and a test plan.
Prerequisites and limitations
- Source code and change context are provided by a version control integration, for example SVN.
- Analysing database data and system logs requires separate, authorised data access mechanisms — the version control integration does not provide them.
- The result of the analysis depends on the amount and quality of the available evidence.
Analyse the possible causes of security incidents using the available system evidence.
Expected resultIncident analysis material with possible causes and remediation recommendations.
Prerequisites and limitations
- DCS Flow does not replace SIEM, EDR or a professional incident response process — it supports the analysis of security specialists.
- Using log and event data requires appropriate, authorised access.
Improve the user experience and design new interface mock-ups for the information system.
Expected resultDesign mock-ups and refined user interface solutions.
Retain understanding of the system when employees or external suppliers change.
Expected resultA structured system overview, key dependencies and identified knowledge gaps.
Assess whether changes meet the requirements and prepare test scenarios.
Expected resultA test plan, test scenarios and identified risks.
01How does your organisation work with information systems?
02Which task do you want to solve?
03How does DCS Flow help?
Create and deliver new ideas
We manage our own information systems
Analyse the need for new functionality, clarify requirements, assess the impact on existing systems and prepare implementation tasks.
AI-supported workflow — 5 steps; results are reviewed by specialists
- Step 1: Needs analysisHelps define the business need and the expected value.
- Step 2: Requirements clarificationIdentifies ambiguities and prepares clarifying questions.
- Step 3: Structuring the initiativeSplits the initiative into logical sub-initiatives.
- Step 4: Architecture assessmentAssesses the impact on existing components and dependencies.
- Step 5: Implementation planPrepares a plan and draft development tasks.
Expected result
A structured initiative, requirements, solution alternatives and tasks ready for development.
Potential business benefit
New functions are planned with existing systems and previous decisions in mind, and the knowledge about them stays in the organisation.
Potential value; the actual effect depends on available knowledge and implementation.
Illustrative example
Situation
Residents need to be able to apply online for a service that until now could only be requested in person.
How DCS Flow supports
Helps clarify requirements, assesses the impact on the existing customer register and prepares the initiative structure.
Result
An initiative with sub-initiatives, a list of open questions and draft tasks for review in Board.
Related DCS Flow modules:ChatInitiativesBoardDesign
See DCS Flow in action Higher productivity. Lower costs. Knowledge that creates value.
DCS Flow helps organisations make better use of their IT professionals, retain and develop technology knowledge, and reduce the cost of developing and maintaining information systems.
01
Lower development and maintenance costs
Less repetitive work. More efficient use of resources.
AI helps automate and speed up labour-intensive analysis, documentation and technical design tasks. Specialists spend less time searching for information and investigating existing systems, and more time creating value.
02
Higher team productivity
More work done with existing resources.
A shared workspace, organisational knowledge and specialised AI agents help the team understand tasks faster, collaborate and produce high-quality engineering results. Less time is needed to learn the context and hand information over between specialists.
03
Less dependence on individual specialists and suppliers
Knowledge stays in the organisation — even when people change.
System architecture, source code, documentation, guidelines and past decisions form a knowledge base available to the organisation. Onboarding new specialists and taking over systems becomes easier, reducing the risks of staff turnover and dependence on specific external suppliers.
04
Reuse of knowledge and solutions
Every project adds to the organisation’s technology capital.
DCS Flow helps build and use a shared organisational knowledge library that accumulates information about system architecture, source code, components, requirements, guidelines and previous technical decisions.
When starting a new project or evolving an existing system, AI helps find suitable previous solutions, assess how well they fit the new requirements and prepare adaptation proposals.
No need to start from scratch every time — the organisation can use what it has already built.
Benefits are described as potential value. The actual effect depends on the organisation’s systems, available knowledge and the scope of implementation.
DCS Flow in action
From isolated AI experiments to systematic use of AI in IT engineering.
How do you introduce a new integration while respecting the existing system architecture and security requirements? Follow seven stages from request to a result that specialists can verify.
ChatProject: e-services platform (example)
Illustrative scenarioStage 1 of 7
User request
A system architect asks a question in the context of a specific project.
System architect · E-services platform
How do we introduce a new integration while respecting the existing system architecture and security requirements?
Module: ChatProject: E-services platformTask type: architecture assessment
The request is linked to project knowledge and guidelines already available to the organisation in DCS Flow.
Stage 1 of 7
Illustrative scenario. Projects, files and content are examples and do not reflect real customer data or results. This page does not run an AI analysis — it shows how the DCS Flow work process is structured.
How DCS Flow connects knowledge, AI and engineering work
Select a component to see its role and how it connects to the rest of the platform.
Organisational knowledge→Context management→Agent orchestration→Specialised AI agents→Engineering results
Organisational knowledge
IntegrationsMCP
Tools | Data sources | External systems
DCS Flow AI core
Engineering results
Infrastructureisolated environment
Your systems. Your knowledge. Context that AI uses at work.
Information is not simply pasted into an AI request. Context management selects what is relevant to the specific task. Choose a task to see the selection.
Available knowledge
01
Source code and repositories
Information system source code and its structure.
Selected: integration-gateway, identity-service
02
Documentation
Architecture descriptions, requirements, specifications.
Selected: Architecture description: integration layer
03
Guidelines and standards
Rules, technical standards, security requirements, design principles.
Selected: Security requirements for external integrations
04
Project decisions and knowledge
Previous decisions, terminology and project context.
Selected: ADR-014: asynchronous integrations
05
Tools, data sources and external systems
Access to relevant resources through MCP integrations.
Context management
Selects the information relevant to the task, not everything available
Example task
Context selected for the task
4 of 5 knowledge categories selected
Affected components
Integration layer and authentication service.
Binding rules
Security requirements and the previous architecture decision.
Not used
Tool integrations — not needed for this task.
Illustration. The selection depends on the sources available and connected in the organisation; if information is insufficient, AI points out the missing context.
The quality of an AI result depends not only on the model, but also on how relevant the context is that the system can use.
How DCS Flow works
- Organisational knowledgeSource code (Git, SVN), documentation, guidelines, decisions and MCP integrations.
- Context managementSelects the information relevant to the task, not everything available.
- Specialised AI agentsAnalysis, design, development, testing and maintenance.
- Engineering resultsBacked by sources and verifiable by a specialist.
Isolated environmentDedicated GPU resourcesNo internet access
The quality of an AI result depends not only on the model, but also on how relevant the context is that the system can use.
AI capabilities. Organisational control.
The platform runs in isolated data centre infrastructure with no outbound access to the public internet. Data is stored in DCS Flow or customer infrastructure.
Isolated infrastructure
DCS Flow runs in controlled data centre infrastructure.
No direct access to the public internet
There is no outbound connection to the public internet from the isolated platform environment.
Dedicated GPU resources
AI models run on DCS Flow infrastructure.
Data control
Data is stored in DCS Flow or customer infrastructure, depending on the chosen deployment model.
Human oversight
Significant engineering decisions are made by the organisation’s specialists.
Verifiable results
AI-prepared conclusions can be evaluated using sources and reasoning.
Detailed security information
These topics are covered during the demo and evaluation, in line with your organisation’s requirements and the chosen deployment model.
- Authentication
- Access rights
- Audit logs
- Data encryption
- Backups
- Data retention periods
- Customer data isolation
- Use of data for AI model training
Frequently asked questions
Data is stored in DCS Flow or customer infrastructure, depending on the chosen deployment model. The deployment model is assessed together with the organisation at the start of the collaboration.
No. DCS Flow runs in isolated data centre infrastructure, and there is no outbound access to the public internet from the platform environment.
Data usage terms are agreed at the start of the collaboration. We provide detailed information on this during the demo and evaluation.
Significant engineering decisions are made by the organisation’s specialists. AI prepares analysis, alternatives and proposals; guidelines proposed by AI in the Steering module only take effect after human approval.
AI-prepared conclusions are linked to the sources used — source code, documentation, guidelines or decisions — and to the reasoning. If information is insufficient, AI points out the missing context rather than inventing conclusions.
DCS Flow uses MCP integrations to access tools, data sources and external systems. Specific integrations are assessed against the requirements of your environment.
We review authentication, access rights and audit solutions during the demo and evaluation, in line with your organisation’s requirements.
No. DCS Flow supports specialists — it helps them gain context faster and prepare analysis and documentation. People keep the decisions and the responsibility for the result.
Authentication, access rights, audit and encryption are reviewed in the demo according to your requirements.
How to get started with DCS Flow
The path from first contact to everyday work. A demo is an introduction and does not commit you to a contract.
- IntroductionA demo in the context of your tasks. A trial by agreement.
- AgreementWe agree terms and sign a contract.
- ImplementationRegister the organisation, add projects and users, connect repositories, guidelines, data and tools.
- Daily useYour team starts using DCS Flow in daily work.
The demo does not oblige you to sign a contract. It is an introductory step.
IntroductionStep 1 of 7
Request a demo and try it out
In a demo, we show DCS Flow in the context of your tasks. A trial is possible by agreement. A demo does not commit you to a contract.
Demo content
Discussing your systems and tasks
DCS Flow demonstration
Assessing trial optionsby agreement
Explore DCS Flow in the context of your organisation.
In a demo, see how DCS Flow can help you understand, maintain and evolve your organisation’s information systems.