Prototypes are easy. Production is the engineering. We design the systems that survive it.
Autonomous systems doing real work.
Multi-agent systems where specialized AI agents plan, reason, use tools, and collaborate autonomously — handling complex business processes that previously required entire teams. Designed for the failure modes of production, not the optimism of demos.
The right model. The right fit.
We integrate LLMs into existing enterprise systems with precision — model selection, prompt engineering, caching, cost optimization, reliability. No hallucinations shipped to production. No vendor lock-in by default.
AI that knows your business.
Retrieval-Augmented Generation that gives models accurate access to your proprietary knowledge — documentation, databases, past decisions, institutional memory. Evaluated against real accuracy metrics, not vibes.
Processes that run themselves.
Time-intensive, error-prone business workflows become intelligent automated pipelines — rule-based automation combined with AI reasoning to handle the exceptions that break conventional tools.
Model Context Protocol, done right.
MCP servers and clients that give AI agents structured, secure access to your enterprise tools, databases, and APIs — with proper scoping, authentication, and audit trails that enterprise security teams accept.
The best LLM in the world will not save a system designed without state, observability, or failure modes. We choose the structure first.
Demos are linear. Production is adversarial — concurrent users, partial outages, untrusted inputs, regulatory drift. Most AI failures are systems-engineering failures wearing an AI costume.
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 resources, not after.
We don't create dependency. The system you receive is yours — documented, observable, modifiable by your own engineers.
We don't do discovery phases that produce slide decks. Every week ends with something running against your real data.
We map your data, systems, and constraints — before recommending anything.
Working software every week. We ship narrow, end-to-end slices that you can use.
Production isn't a state, it's a discipline. Telemetry, guardrails, runbooks.
Your engineers own the system. We stay available — not as a dependency, as a backstop.
Manual fraud reviews queued for days. Risk team could not scale with transaction volume.
Multi-agent triage with retrieval over historical cases. Confidence-gated auto-resolve, human-in-the-loop for borderline.
Catalog had 4M SKUs. Existing recs were rules-based and could not adapt to seasonal shifts.
Streaming embedding pipeline + hybrid recall. Real-time re-ranking layered over existing search infra. Zero-downtime cutover.
Customer support team answered the same 200 questions every week. Documentation drift broke their playbooks.
RAG over docs, tickets, and Slack history. Citation-required answers. Eval pipeline catches regressions before they ship.
Regulated environments demand AI systems with audit trails, output guardrails, and zero tolerance for hallucination on customer-facing decisions.
Multi-agent review of credit and fraud cases — confidence-gated auto-resolve, escalation for borderline.
KYC, loan docs, statements processed with citation-required extraction. Audit-ready by default.
Continuous policy-to-control mapping. Drift detected before audits, not during them.
8questions. If yours isn't here, just email us.
contact@antashiai.com