How the company uses technology to solve business problems.

Production AI that sits inside existing software, not a disconnected demo.

Used to qualify work, retrieve company knowledge, call tools, and keep quality visible through evaluation and observability.

• LLMs
• RAG
• AI agents
• AI workflows
• AI evaluation
• Tool / function calling

Application platforms that can take traffic, change, and integration load.

Used when a product, AI layer, or automation path needs APIs and services that fail safely.

• APIs
• Microservices
• Distributed systems
• Event-driven architecture
• High-concurrency systems

Stores and pipelines that keep operational truth close to the software that uses it.

Used for systems of record, caches, search, and the data AI retrieval and automation depend on.

• PostgreSQL
• MySQL
• Redis
• Elasticsearch
• Data pipelines

Contracts between the systems that already run the business.

Used so AI and new products can read and write real records instead of sitting beside CRM, ERP, and internal tools.

• REST
• GraphQL
• gRPC
• Webhooks
• Third-party APIs

Delivery and runtime that engineering teams can operate day to day.

Used to ship, observe, and recover — so AI and product changes do not depend on heroics.

• AWS
• Docker
• CI/CD
• Monitoring
• Observability

Practice that helps teams deliver better software faster.

Used to raise test coverage, documentation, review, and delivery path quality — without treating AI as a replacement for engineers.

• Testing
• Automation
• Performance engineering
• Architecture
• AI-assisted development