.avif)
.avif)
.avif)
Engineering + AI
Choose the delivery model that fits.
Bring the application, integration, or AI initiative your team needs to ship. MBC provides AI-native engineering capacity with a clear technical owner, agreed milestones, and a practical plan for production.
Embedded specialists: Add engineering, data, or AI capacity alongside your internal team.
Outsourced delivery: Assign a bounded workstream to an MBC team with one delivery owner.
Implementation projects: Agree the scope, acceptance criteria, release plan, and fee before work begins.
Technical Ownership
A named technical lead coordinates architecture, delivery decisions, and review with your team. Responsibilities and access are agreed before work begins.
Solutions
Full-stack development across web, mobile, API, and data layers
Technical leadership and architecture review embedded in your sprint cycles
Knowledge transfer and documentation built into every sprint
AI-Native Delivery
Apply current AI methods to appropriate development and operating tasks, with human review, evaluation, and documented acceptance criteria.
Solutions
Experienced global specialists add capacity around the skills and working hours the engagement requires.
Integrated code review, pair programming, and daily stand-up cadence within your existing workflow
Continuous delivery automation with AI-powered CI/CD, testing, and deployment pipelines
Core Solutions
AI Systems Integration
Connect AI capabilities directly into your existing enterprise stack. We integrate LLMs, computer vision, and NLP systems with your platforms—production-grade, not proof-of-concept.
Architectural excellence combined with rapid iteration cycles
Production-ready solutions with scalable architecture design
Automated testing and performance optimization built-in
AI Software Development
Custom AI-powered applications from concept to production deployment. RAG systems, AI agents, and intelligent workflows that handle real business processes.
Automated code review and testing protocols for consistent quality
Intelligent deployment processes that reduce manual errors
AI-assisted development tools, with gains measured against the team’s baseline
AI Operations & MLOps
Production AI demands production operations. Model monitoring, drift detection, automated retraining pipelines, and infrastructure optimization for reliable AI at scale.
Agree testing, evaluation, monitoring, and support responsibilities for the production environment.
Global specialists selected for the technical requirements, with defined review and quality controls
Plan onboarding, documentation, and knowledge transfer as part of the engagement.
MVP-to-Market Acceleration
Define a focused first release, test it with representative users, and use the findings to guide the next build. Agree scope, release criteria, and feedback ownership before development.
Accelerated development includes market validation and user testing cycles
Architecture decisions account for expected usage, maintenance, and future change.
Test performance and monitoring against agreed production-readiness criteria
Embedded Engineering Capacity
Add specialists around a defined technical need. Agree responsibilities, technical oversight, review cadence, and delivery expectations before work begins.
Define the workstream, team interfaces, and acceptance criteria before adding capacity.
Review technical fit and delivery progress against the agreed scope.
Comprehensive onboarding includes knowledge transfer and cultural integration support
Technical Due Diligence & Architecture Assessment
Evaluate technical assets and development capabilities for M&A transactions with comprehensive assessments covering code quality, scalability, and team capabilities.
Technical asset analysis includes code quality and architecture scalability review
Development capability assessment covers team skills and operational maturity
Investment decision support through detailed technical risk analysis
Advanced Technology Capabilities
Enterprise AI Integration & LLMOps: Production-grade AI integration across your enterprise stack—LLM orchestration, RAG pipelines, model serving infrastructure, and continuous optimization for reliable AI at scale.
Cloud Infrastructure: Plan capacity, deployment, recovery, and operating costs around expected usage. Validate resilience against the requirements agreed for your environment.
Data and Reporting: Build traceable reporting pipelines and operational dashboards around approved source systems.
AI Workflow Development: Build task-focused AI workflows with clear review steps, exception handling, and authorized actions.
Security Requirements: Define access controls, data handling, and review responsibilities for the engagement. Compliance decisions remain with the authorized owner.
