From AI agents and automation to software modernization, security, and cloud reliability, Sintelium supports the full journey from technical strategy to delivery.

AI Transformation & Agentic Automation

Turn AI opportunities into reliable agents, workflows, and integrated systems.

  • AI opportunity assessment
  • AI adoption strategy
  • Custom AI agents
See category

Custom Software Engineering

Build dependable applications, APIs, and platforms tailored to your real operating model.

  • Product discovery and technical requirements
  • Web application and dashboard development
  • Backend and API engineering
See category

Modernization & Architecture

Reduce friction, technical debt, and risk with pragmatic modernization and system redesign.

  • Legacy application audits and modernization roadmaps
  • Codebase restructuring and large-scale refactoring
  • Architecture reviews and system redesign
See category

Performance, Cloud & Reliability

Improve responsiveness, resilience, and operational confidence under pressure.

  • Performance profiling and bottleneck analysis
  • Scalability architecture and resilience testing
  • Cloud infrastructure architecture and migration
See category

Security & Quality Engineering

Reduce technical risk with secure engineering, testing, and remediation support.

  • Secure code review and threat modeling
  • Dependency and software supply-chain vulnerability review
  • Automated unit, integration, and end-to-end testing
See category

Data & Integrations

Connect data sources, streamline workflows, and build AI-ready information flows.

  • Data architecture and modeling
  • ETL/ELT pipelines and workflow orchestration
  • API integration and data exchange design
See category

Advisory & Engineering Enablement

Align team capability, architecture decisions, and execution strategy with business needs.

  • Technology strategy and solution architecture
  • Fractional technical leadership / CTO advisory
  • AI adoption enablement for development teams
See category

Common business challenges

  • Manual, repetitive work is slowing teams down
  • Systems are difficult to integrate and maintain
  • AI pilots are not ready for operational use
  • Applications are under pressure from growth or technical debt

Engagement models

  • Fixed-scope project
  • Time and materials
  • Ongoing retainer