AI implementation
We build AI agents that process requests, check documents and prepare reports. We connect them to your work systems.
How we implement AIDemlabs
Software development
We take responsibility for delivery: from understanding the task to launch and handover to your team.
End-to-end code modernization
We investigate poorly structured and outdated software. We rework or rewrite code to make the system easier to maintain and extend.
How we modernize codeWe start by discussing your task, constraints and expected outcome.
Two services, full-cycle delivery
End-to-end AI implementation
For teams spending time on manual work with text, documents and data. We build AI agents, connect them to your work systems and take the solution from pilot to deployment.
- Process analysis and success criteria
- Agent development, integration and testing
- Deployment, documentation and team handover
End-to-end code modernization
For products that break often, cost too much to maintain or are hard to extend. We investigate code written by other teams, rework or rewrite the necessary parts and deploy the updated system.
- Code audit and modernization plan
- Refactoring, rewriting and behavior verification
- Deployment, documentation and team handover
AI implementation and software modernization are independent services. Choose either on its own or combine them in one project.
Problems we help you solve
Example tasks for each service. We agree on scope and success criteria for your project.
Request processing
- Situation
- Employees read incoming requests and manually copy information into a CRM.
- What we do
- Extract data, check completeness, prepare records and route requests. Pass exceptions to an employee.
- What to measure
- Initial processing time and the number of corrections.
Bugs and instability
- Situation
- The system fails regularly, and fixing one bug introduces others.
- What we do
- Find the causes, cover critical workflows with tests and rework the problematic code.
- What to measure
- Recurring error frequency and recovery time.
Document processing
- Situation
- Your team checks information and gathers missing details.
- What we do
- Extract fields, compare them against rules and prepare discrepancies for human review.
- What to measure
- Review time and data extraction accuracy.
Features are hard to add
- Situation
- Even small changes require lengthy investigation of tangled code.
- What we do
- Separate component responsibilities, simplify dependencies and rewrite parts that block development.
- What to measure
- Lead time for typical changes and post-release defects.
Report preparation
- Situation
- Data is collected manually from several systems.
- What we do
- Retrieve data, summarize indicators and prepare a report with links to sources.
- What to measure
- Preparation time and the number of manual corrections.
An outdated stack
- Situation
- Dependencies are no longer supported, and upgrade obstacles hold back the product.
- What we do
- Plan the transition, update or replace components, and test compatibility and data migration.
- What to measure
- Share of unsupported dependencies and critical workflow test results.
These are example tasks, not ready-made modules or promises of specific metrics. We agree on scope, constraints and success criteria after analysis.
Demlabs products
We develop our own products for working with AI, managing software development and building distributed services.
AI tools for developers
SLC Agent
Project context, cross-session memory and code search for AI agents.
Explore SLC Agent — opens in a new tabProject and development management
Munkly
Work planning, time tracking, budget control and team workload management. Available in the cloud or on your own servers.
Explore Munkly — opens in a new tabBlockchain platform
Cellframe
A platform using post-quantum cryptography for building blockchains and distributed services. SDK, network nodes and client software.
Explore Cellframe — opens in a new tabCellframe / Demlabs
Experience building complex systems
In recent years, much of our work has focused on Cellframe, our own blockchain platform. This includes building SDKs, networking software, cryptographic components and applications for multiple operating systems.
We apply this engineering experience to both AI implementation and existing code: understanding architecture, integrations and operational constraints. Technology follows the task — blockchain is not a required part of a project.
Platform development
SDK, network nodes and client applications.
Technical resources
Documentation and available source code.
Product development
Software releases and update announcements.
From understanding the task to deployment
-
Understand the task
Review the current situation, project goals and technical constraints.
Outcome: Clear goals, a baseline and possible approaches.
-
Agree on a plan
Define stages, integrations, acceptance criteria and the rollout approach.
Outcome: Scope, budget and timeline for each stage.
-
Build and verify
Build the solution according to the agreed plan and test it against real usage scenarios.
Outcome: A solution tested against agreed criteria.
-
Deploy and hand over
Launch the solution, deliver code and documentation, and walk your team through the changes.
Outcome: A working system and an agreed approach to ongoing support.
Timelines and budgets depend on integrations, code condition and operational requirements. We agree on them after analysis and discuss rollout and ongoing support separately.
Frequently asked questions
How do we know whether we need AI?
Start with a process that takes up your team’s time and examples of its input data. We assess where AI is useful and where conventional automation is enough. A pilot helps test the outcome before expanding the implementation.
Do you take on poorly written code from other teams?
Yes. We investigate existing projects, including those built by other teams or with incomplete documentation. After an audit, we recommend what to fix, rework or rewrite. You can commission modernization without AI implementation.
Where is the data stored, and who controls the AI agent?
Before launch, we agree on data storage, external services, agent permissions and actions requiring employee approval. These choices depend on your infrastructure and data requirements.
Does the entire system need to be rewritten?
Not necessarily. We keep working parts where appropriate and change problematic ones in stages. If a full rewrite is needed, we explain why and agree on data migration, functional testing and the switch to the new version.
What does end-to-end delivery include?
Analysis, planning, development, testing, deployment, and code and documentation handover. This applies to both services. We agree on project boundaries, infrastructure responsibilities and ongoing support terms in advance.
What determines cost and timelines?
For AI: the process, integrations and data requirements. For modernization: code condition, the scope of changes and migration. After analysis, we agree on stages and budget. Recurring infrastructure, model and support costs are estimated separately.
Let’s discuss your project
Tell us about your task, current problems and the outcome you need. You do not need a detailed specification for the first conversation.
We respond within one business day.