Our Services
End-to-End Engineering
for Ambitious Teams
Six specialized service lines — each designed to solve a different class of problem, all united by our problem-first philosophy.
Why Haivisoft Differs
Problem-First Scoping
We diagnose before prescribing — every engagement starts with understanding the actual constraint.
Product-Grade Delivery
Architecture, monitoring, and CI/CD are first-class deliverables — not post-launch afterthoughts.
Transparent Governance
Shared dashboards, weekly metrics reviews, and open communication at every stage.
Knowledge Transfer Built In
Documentation, training, and enablement are part of every engagement — not optional add-ons.
Questions? Answers.
What is the scope of Haivisoft services?
We offer six service lines: Product Design & Engineering, AI & LLM Engineering, AI Engineering Pods, AI Academy, GCC Setup, and Performance Acceleration. Each can be engaged independently or combined for end-to-end delivery.
How do AI Engineering Pods work?
Pods are cross-functional teams (backend, frontend, ML, QA, delivery lead) that integrate into your sprint cadence, use your tools, and deliver with shared accountability. Pod activation is under 2 weeks.
Can you help us set up a GCC?
Yes. We design the operating model, set up governance frameworks, recruit and ramp the team, and establish delivery excellence processes for independent operation.
What does the AI Academy offer?
Role-based learning tracks for engineers, PMs, and leaders with hands-on labs, real-world projects, assessments, and certification. Programs are customized to your team context and technology stack.
How do you ensure quality?
Automated testing, CI/CD pipelines, DORA metrics tracking, quality gates, sprint reviews, and transparent dashboards. We measure velocity, defect rate, cycle time, and test coverage.
Can we combine multiple services?
Absolutely. Many clients engage us for product engineering with embedded pods while running AI Academy in parallel for their existing team.
What technologies do you use?
Our core stack includes Node.js, PostgreSQL, Azure, and modern frontend frameworks. For AI workloads, we work with leading LLM providers, vector databases, and MLOps toolchains — selecting the right tool for each problem.