A clearer implementation path
The organization gains one shared model before development and delivery begin.
Solution design that connects business processes, systems and AI into one model
We translate business needs into architecture, workflows, data structures and integration logic so the organization can see how the target solution will work before implementation begins.
01
A bridge between business expectations and implementation reality
02
Architecture for systems, data, workflow and AI
03
A clearer model of how the target solution should operate
About the service
A structured design phase that reduces uncertainty before implementation
Solution design defines how a process should work, which systems are involved, how data moves and where AI or automation should be embedded.
This stage reduces ambiguity between business and delivery teams because it creates a shared view of the target operating model.
The result is a clear blueprint that supports implementation, governance and future scaling.
A bridge between business expectations and implementation reality
Architecture for systems, data, workflow and AI
A clearer model of how the target solution should operate
Lower delivery risk before build starts
Use cases
Solution design scope
IT solution design
We define the process model, data flow, functional scope and integration points of the target solution.
Definition of process stages, user roles and operational logic.
Design of system responsibilities and integration dependencies.
A target blueprint that can be used directly in delivery planning.
AI and automation architecture
We design where AI and automation should be embedded and how they interact with the wider process and data environment.
Definition of automation points, decision support scenarios and handovers.
Alignment of AI usage with data sources, governance and security requirements.
Architecture ready for future scaling and further use cases.
AI model architecture and provider selection
We design the AI model layer for the organization – choosing the specific model (OpenAI, Claude, Gemini or open-weight) and deciding whether a single-model, multi-model or private AI self-hosted architecture is the better fit.
Model selection per task class: reasoning, coding, knowledge retrieval, multimodality, long context.
Multi-model architecture design with a data routing layer (different models for different processes and data classes).
On-premise / cloud / hybrid decision based on regulation, volume and available MLOps capability.
Quality evaluation plan on your own data and a model versioning strategy post-deployment.
Next step
Let’s review which parts of this service can create operational and business value the fastest in your organization.
Korzyści
Business benefits
Less misalignment between teams
Business and technology teams work from the same assumptions and target state.
Stronger delivery readiness
The solution can be implemented with fewer surprises, gaps and rework.
Who this is for
Who this is for
Business and IT leads
Teams that need one target solution model before delivery.
Organizations integrating multiple systems
Environments where data and workflows must work together cleanly.
Teams preparing AI implementation
Organizations that need architecture before deployment starts.
Enterprise environments
Companies that require design discipline, governance and scalability.
Delivery process
How we work
We deliver the service in a structured model that brings clarity to project stages, integration with the current environment and further development of the solution.
Stage01
Discovery
We review process goals, data dependencies and delivery constraints.
Stage02
Architecture design
We define workflow, systems, integration points and the role of AI.
Stage03
Validation
We align the target model with business, technology and operational stakeholders.
Stage04
Delivery handover
We prepare the design so it can move into implementation with clear assumptions.
Stage05
Refinement
We update the model as scope evolves or further implementation waves are planned.
Stage 1 of 5
Projekt rozwiązania dopasowany do procesów organizacji
Architektura integracji, danych i AI
Czytelny model przed etapem implementacji
FAQ
Frequently asked questions
Why separate design from implementation?
Because it reduces delivery risk. A clear target model helps teams avoid rework, misalignment and architecture gaps later in the project.
Does this include integrations and workflow design?
Yes. We cover process logic, systems, data, integrations and the role of AI in one delivery model.
Can this be used as a starting point for implementation?
Yes. The output is prepared so it can directly support the next implementation phase.
Do you also design the choice of a specific AI model?
Yes. AI model selection – OpenAI, Anthropic Claude, Google Gemini or open-weight models (Llama, Mistral) – is part of the solution architecture. We also decide whether to standardise on one model, build a multi-model architecture with a data routing layer, or run a private AI in a self-hosted model for sensitive data.
Kontakt
Let’s talk about your needs!
Filling out the form takes just a moment, and we will get in touch to understand your requirements.
In-depth analysis
Projektowanie rozwiązań IT, AI i automatyzacji
Projektowanie rozwiązań IT oraz architektury AI i automatyzacji pozwala organizacjom uporządkować sposób działania przyszłego systemu jeszcze przed rozpoczęciem implementacji. To etap, na którym biznes i IT uzgadniają model procesów, rolę danych, integracji i sposób wykorzystania AI w praktyce.
Dobrze zaprojektowane rozwiązanie odpowiada nie tylko na pytanie, co ma powstać, ale przede wszystkim jak będzie działać, jak połączy się z istniejącym środowiskiem i jak wpłynie na codzienną pracę zespołów. Dzięki temu wdrożenie przebiega sprawniej i z mniejszym ryzykiem zmian po drodze.
Częścią projektowania jest też dobór konkretnego modelu AI oraz architektury modeli – jednomodelowej, wielomodelowej lub private AI w modelu self-hosted. Wybór między OpenAI, Anthropic Claude, Google Gemini i modelami open-weight rzutuje na koszt operacyjny, czas reakcji, governance i niezależność od dostawcy. Dlatego projektujemy go świadomie, w kontekście realnych procesów i wymagań compliance, a nie wyłącznie na podstawie benchmarków.
Usługa ma największą wartość w organizacjach, które chcą łączyć potrzeby biznesowe z architekturą technologiczną w sposób uporządkowany i gotowy do dalszego rozwoju w środowisku enterprise.