Forward Engineering
in AI
NEW AI-POWERED FEATURES AND SOLUTIONS
Forward engineering in AI is an approach where we design and build new AI-based products, services, and business capabilities using large language models (LLM) and the Microsoft ecosystem, including Copilot Studio. We design and build AI-powered solutions: from rapid prototypes to full-scale implementations ready to run in your organization.
We help companies move from an AI concept to a working solution that genuinely improves how people and processes operate. This enables organizations to deploy innovations faster and turn them into concrete business outcomes.
FROM FRICTION TO FLOW
Clear AI adoption strategy
Expertise needed for LLM implementation
Fast transition from PoC to production
Consistent AI standards across the organization
SCOPE OF SERVICE:
FORWARD ENGINEERING W AI
AI FORWARD ENGINEERING CAPABILITIES
Design of end-to-end LLM solutions
Process automation using AI agents
Integration with M365 Copilot and Copilot Studio
Customized Human + Machine interaction models
Secure deployments aligned with Responsible AI
AI architecture standardization across the organization
Tools for measuring AI effectiveness and quality
FORWARD ENFINEERING
PLATFORMS AND TECHNOLOGIES
Microsoft Copilot Studio
Building AI agents, workflows, integrations, and Copilot extensions. The platform enables designing action logic, prompting, context, and connections to business processes.
Microsoft M365 Copilot
Extending built-in Copilots with capabilities tailored to organizational processes. Integration with company data enables automation of operational, analytical, and communication tasks.
Azure AI Foundry and Azure OpenAI Service
Model hosting, integrations, vector store, data vectorization, RAG, and AI process orchestration. Stable, secure infrastructure for LLM-first solutions and building agents that operate in client environments.
UiPath + AI
Combining AI agents with process automation. Integration of LLMs, Copilot, and bots enables creating complete end-to-end flows: from content understanding, through decisions, to executing actions in systems.
BUSINESS OUTCOMES
New business capabilities through LLM-first solutions
Knowledge work automation
Controlled path from PoC to production
New AI capabilities without building from scratch
AI standardization across the organization
Faster testing and deployment of AI capabilities
Why us?
We combine AI, architecture, and automation expertise to create LLM-first solutions ready to operate in organizational processes and systems.
- Broad competencies
- AI, architecture, M365, Copilot Studio, and automation in one team, enabling us to design end-to-end solutions.
- LLM-first experience
- We build agents and applications based on language models in projects for large organizations.
- Proprietary AI frameworks
- Human+Machine, AI Blueprint, and AI Governance accelerate design, improve quality, and organize architecture.
- Collaboration with Microsoft
- We work with Azure AI, M365 Copilot, and Copilot Studio technologies, with team expertise confirmed by certifications.
- Enterprise experience
- We deliver AI implementations in organizations with high complexity and security requirements.
- Value-driven approach
- We start from business needs: technology is a tool, not the starting point.
INTELLIGENT AUTOMATION & AI EXPERTS
A team of certified UiPath and Microsoft Copilot experts with deep implementation, analytical, and technical expertise. We work end‑to‑end: identifying high‑ROI processes, building and deploying solutions powered by RPA, AI, and Document Understanding, and ensuring ongoing stability and scale.
Our approach is data‑driven, grounded in governance best practices, and based on close collaboration with the business and technical teams who use the automation every day.
Daniel Chrapczyński
Autonomous Enterprise Manager
Łukasz Ewertowski
Head of Microsoft CoE
Kamil Kamiński
Senior Business Development Manager
FAQ – FORWARD ENGINEERING in AI
Answers to frequently asked questions can be found here
What does the AI project process look like from idea to implementation?
The process includes identifying business value, preparing an AI Blueprint, architecture design, implementation in appropriate AI technologies, quality and security testing, and production launch. Post-deployment, we conduct iterative development aligned with the business roadmap.
Can we integrate AI with our existing systems?
Yes. We integrate AI models and agents with ERP, CRM, DMS systems, and enterprise applications using APIs, connectors, automation, and Copilot Studio. This enables AI to operate securely within existing architecture.
Are language model-based solutions secure?
We build solutions aligned with Responsible AI principles and organizational security policies. We ensure access control, activity logging, data protection, and AI governance required for enterprise environments.
Can we start with a small AI prototype?
Yes. We recommend rapid prototypes focused on business value. They enable testing model performance in practice and serve as the first step toward full implementation.
How do we move from PoC to a stable production version?
We conduct migration from prototype to production version based on AI Blueprint, security standards, and governance. We provide testing, quality monitoring, performance metrics, and complete documentation.
What competencies are needed to implement LLM solutions?
AI projects require a combination of data knowledge, architecture, automation, and prompt engineering. We bring a complete team of experts, including AI architects, Copilot Studio specialists, and process analysts.
How do you measure the outcomes and ROI of AI implementations?
We measure effects by reduced working time, error reduction, automation of knowledge-based tasks, and business value of new AI capabilities. We create dashboards in Azure and Power BI that show response quality, handling time, and impact on processes.
Can AI be deployed in stages across multiple departments?
Yes. We support phased rollout with clear architecture and governance standards to avoid technological chaos and ensure consistent AI agent operation across the entire organization.
Which AI use cases are best to start with?
The best starting point is usually processes that involve repeated work with documents, knowledge, and communication, where the impact can be measured quickly. Common examples include document handling, information retrieval, employee support, ticket classification, response generation, and automation based on knowledge bases. The best use case depends on the available data, the complexity of the process, and the expected business outcome.
What data and systems can AI use in our environment?
That depends on the organization’s architecture and security model. AI solutions can use data from SharePoint, Teams, Outlook, CRM, ERP, knowledge bases, documents, files, and systems that expose APIs. We design integrations so the solution operates in the context of real business data and in line with access and security rules.
How long does it take to implement the first AI solution?
The implementation timeline depends on the complexity of the process, the scope of integrations, and the readiness of the data. In simpler scenarios, the first working solution can be delivered within a few weeks. More complex projects require a longer design and implementation phase. Our goal is usually to help the organization see the first business value as quickly as possible and then develop the solution iteratively.
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