# Oogway Labs services

Oogway Labs offers seven production AI engineering services:
- Agentic AI in Production: Agents that survive production traffic — and don't quietly break at 3am.
- AI Readiness Audit & Roadmap: Know where AI will pay off before you invest. 1-2 weeks to a prioritized roadmap.
- RAG & Memory Architecture: Retrieval and memory systems that ground AI in your data, not the internet.
- AI Reliability: Evals, Governance, Cost: Evals, guardrails, and cost discipline, so your AI keeps shipping under real load.
- Voice AI: Front-desk voice agents that handle scheduling and support 24/7.
- AI Product Engineering: Production AI applications, built end-to-end by the same team: frontend, backend, AI, infrastructure.
- Fractional AI Leadership: An internal AI voice for boards and execs, without a 12-month hiring search.

## Frequently asked questions

### What's the difference between an audit, a quick-win build, and production work?

The audit is a 1 to 2 week diagnostic that ranks AI bets by ROI and effort. A quick-win build is a 4 to 6 week scoped V1 shipped to production for real users. Production work is the ongoing reliability layer: evals, governance, cost controls, and tuning for actual load. Most clients enter at the phase that matches where their problem already is.

### Do you build AI agents, or only advise?

Both, with no separation between the team that advises and the team that builds. Code lives in your repos. We design the system, write production code, set up evals, and stay through the reliability work. Strategy without delivery is what most consultancies do. We do not stop at the deck.

### How do you decide whether AI is the right tool for a problem?

Problem first, tool second. We look at the workflow, the failure cost, and the data quality before we look at the model. Sometimes a simple heuristic, a SQL query, or a forms cleanup beats an LLM. If AI is not the right tool, we say so on the audit call rather than billing for a system that should not exist.

### How do you keep AI systems reliable after launch?

Production reliability is its own phase, not an afterthought. Evals catch regressions before users do. Governance and audit trails cover policy and compliance. Cost controls keep LLM spend from doubling every quarter. Reliability tuning targets your actual load, not demo load. The same engineers who built the system stay through the on-call runbook.

### Can we engage you for one phase, or do we have to commit to all three?

Pick the phase you are in. The audit, quick-win, and production phases are each scoped on their own. Many teams come in mid-stream: they already know what to build and need a team that can ship, or they already shipped and need the reliability layer. There is no all-or-nothing commitment.

### What size company do you work with?

From early-stage startups deciding their first AI bet to enterprises operating systems at scale. The unit of work is engineering judgment, not company size. What matters is that the team funding the work cares about outcomes that show up in production, not maturity scorecards or framework adoption.

- [Book a discovery call](https://oogwaylabs.com/contact/)