Technology at Cognic
Organized by what each technology does — not by what is fashionable. Selection follows requirements.
How Cognic Selects Technology
We select technology per project — matched to the workflow, the data, the systems involved and the security requirements — rather than forcing every requirement into one stack. The list below covers what we genuinely work with across our delivered projects.
A technology list means nothing without the reason behind each choice. Below, every technology is grouped by capability and explained by what it is used for in real systems we have built — the same technologies visible across our published case studies.
AI
Language understanding and generation — assistants, document analysis, classification and content workflows. Selected per task: the model that meets the requirement at the right operating cost, not the biggest name.
Grounding model outputs in your documents and data — the difference between a generic answer and a correct one. Built as RAG systems with retrieval quality engineered, evaluated and monitored.
Multi-step workflow execution with tools, system access and human approval — agents that act inside business processes, with guardrails and observability standard.
Extraction, classification and validation across business documents — invoices, contracts, claims, charts — with human review workflows. See Document AI.
Voice agents handling defined business conversations — support, scheduling, qualification — integrated with business systems. See Voice AI.
Automation
Automating repetitive interface-level work in systems without modern APIs — one tool in the automation stack, not the whole strategy. Delivered across RPA and workflow automation engagements.
Workflow automation across Microsoft 365, Teams, SharePoint and Dynamics environments — used where the client’s stack is Microsoft-centric.
Connecting systems, routing data and orchestrating multi-step processes across APIs — the connective layer of AI automation. Selection depends on complexity, hosting requirements and volume.
Software
Frontend applications — component-driven interfaces that iterate quickly as product feedback arrives. Next.js where server rendering, routing or full-stack structure serves the product.
Enterprise business applications — transaction-heavy systems, security-centric platforms and long-lived maintainable software.
API-driven and I/O-heavy services — real-time workflows and integration layers sharing one language with the frontend.
Backend engineering close to AI and data work — pipelines, model integration and data processing.
Cross-platform applications where forms-and-data patterns dominate; native development where deep device integration justifies it.
Data
Relational data, transactional workflows and evolving reporting — the default for business systems with structured data.
Enterprise transactional systems in Microsoft environments — used across delivered enterprise platforms.
Document-shaped data and schemas that genuinely vary record to record.
Semantic retrieval for RAG systems — storing and querying the embeddings that make grounded AI answers possible.
Operational dashboards, financial analytics and automated reporting — the delivery layer for Data & BI engagements.
Cloud & Infrastructure
Cloud applications, AI services and enterprise workloads — including Azure OCR / Azure AI Document Intelligence used across delivered document processing systems.
Application hosting, data infrastructure and scalable platform architectures — including delivered cloud migration programs.
Environments, CI/CD pipelines, deployment automation and infrastructure-as-code — the operational foundation that keeps delivered systems releasable and observable.
REST APIs and enterprise system integrations — Dynamics 365, EHR systems, CRMs and internal platforms — engineered with authentication, data mapping and failure handling.
How the Stack Comes Together
Individual technologies solve nothing alone. In a delivered system, they compose: an AI engineering project might use Python services around a RAG pipeline, a vector database for retrieval, React for the review interface, PostgreSQL for the workflow data, Dynamics 365 for the ERP integration and Power BI for operational reporting — with Power Automate or RPA orchestrating steps in between. Architecture — not the parts list — is the product.
That composition principle is how we approach every project: requirements first, technologies second, and every selection explained by the work it does. See it applied across our case studies, or read how these choices affect budgets in our software development cost guide and AI development cost guide.
Planning a System?
Tell us the workflow and the constraints — we’ll define the architecture and explain every technology choice before anything is built.