Enterprise AI engineers,
not a generic agency
Antashiai was founded by backend architects who spent over a decade building mission-critical systems for enterprises in retail and financial services — industries where systems must be correct, fast, and auditable.
Why we exist
Most enterprise AI initiatives fail not because the technology is wrong, but because the architecture is. Systems built on fragile integrations, poor data pipelines, and prompt engineering masquerading as engineering inevitably break in production.
We founded Antashiai to solve that gap — to build the AI systems that engineers would be proud to maintain, that compliance teams can audit, and that businesses can actually depend on.
Our work is grounded in 11+ years of enterprise backend engineering. We’ve built distributed systems at scale, designed event-driven architectures for high-throughput retail operations, and shipped production AI integrations in regulated financial environments. That foundation shapes every system we design.
11+ years
Enterprise backend engineering
Retail & fintech
Domain architecture depth
Distributed systems
High-throughput, fault-tolerant design
Production AI
LLMs, agents, RAG at enterprise scale
Principles that guide
every decision
Values only matter when they're expressed in trade-offs. Here are the constraints that shape how we think and what we ship.
Architecture first
We choose the right structure before writing a line of code. Bad architecture is the most expensive mistake in AI systems — and the hardest to fix later.
Production or nothing
We don't build demos. We don't ship prototypes as products. Every system we deliver is designed for the failure modes, scale, and compliance requirements of production enterprise environments.
Intellectual honesty
We tell you when an approach won't work, when a technology isn't ready, and when the problem is harder than it looks — before you commit, not after.
Knowledge transfer
We don't create dependency. We document everything, train your team, and leave your organization more capable than we found it.
How we work with you
Discovery & Architecture
Week 1–2We map your data landscape, existing systems, and business workflows before recommending any AI approach. We challenge assumptions and tell you if a proposed direction won't work — before you commit.
Pilot & Validation
Week 3–6We build a focused MVP against real business metrics — not a demo, not a prototype. A production-ready pilot that gives you evidence before you scale.
Production Engineering
Week 7–16+We scale what works, harden it for production, and integrate deeply with your enterprise systems. Full observability, security review, documentation, and runbooks included.
Knowledge Transfer
OngoingWe document everything, train your team, and transfer operational ownership. We don't create dependency. Retainer-based support is available for critical systems.
Ready to build AI that
actually works in production?
Whether you’re exploring AI for the first time or scaling a system hitting its limits — we’ll give you an honest assessment of what’s actually possible.