AI-first systems engineering, end-to-end
Kairais Tech builds production-grade AI and software systems for companies. We combine applied research with disciplined engineering: measurable evaluation, security by default, scalable infrastructure, and long-term maintainability.
AI PRODUCTS & AGENTS
Assistants, copilots, and workflow agents that integrate with your data and tools. We ship with evaluation harnesses, guardrails, and monitoring so behavior stays reliable after launch.
Outcome: AI features that remain trustworthy under real user pressure.
APPLIED AI RESEARCH
Rapid prototyping with rigor: experiments, baselines, ablations, and benchmarking on task-specific metrics. We document findings and convert them into an implementable system plan, not a theoretical artifact.
Outcome: validated direction with measurable performance and clear tradeoffs.
DATA & KNOWLEDGE SYSTEMS
The foundation for AI: clean data models, retrieval pipelines, and governance. We build schema, indexing, pipelines, and audit-friendly data flows that support reliable inference and analytics.
Outcome: data that is queryable, trusted, and engineered for scale.
PRODUCT INTERFACES
Mobile apps, web platforms, dashboards, and internal tools designed around real workflows. Built for performance, accessibility, and a UI system that stays consistent as you scale.
Outcome: premium UX that supports adoption, not friction.
CLOUD, MLOPS & RELIABILITY
GCP deployments, CI/CD, secrets, observability, and cost-aware scaling. For AI systems, we add model lifecycle support: drift detection, versioning, rollbacks, and safe release patterns.
Outcome: predictable operations, clear telemetry, and controlled change.
DEVELOPER TOOLS
We build tools that reduce complexity for engineering teams: Invar, WispDB, and custom internal platforms. Designed for speed, correctness, and long-term maintainability.
Outcome: faster iteration and fewer recurring operational failures.
How we deliver reliably
Problem framing → system architecture → implementation → evaluation → deployment → observability → maintenance. Clear metrics, written acceptance criteria, and ownership after go-live.