AI Product
Development

Most AI projects fail between demo and production. We close that gap, engineering LLM integrations, ML pipelines, and AI-native platforms that actually hold up at scale.

From prototype to production-grade AI.

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LLM Integration & Fine-Tuning

We integrate the right large language model for your product, OpenAI, Anthropic, Gemini, or open-source, and fine-tune or prompt-engineer it to behave reliably within your specific use case.

ML Pipeline Engineering

End-to-end data pipelines for training, evaluation, and inference, built to handle real production loads, not just lab conditions. We architect for latency, throughput, and cost.

AI-Native SaaS Products

Full-stack products where AI is the core feature, not a bolt-on. We design and build the product architecture, backend, and frontend as a unified system optimized for AI-first workflows.

RAG & Knowledge Systems

Retrieval-augmented generation systems that ground AI responses in your proprietary data, reducing hallucinations and making AI outputs accurate, verifiable, and trustworthy for enterprise use.

AI Evaluation & Monitoring

Production AI requires ongoing measurement. We build evaluation frameworks, quality guardrails, and observability tooling so you know when your models are drifting before your users do.

Ship AI that works
in production.

A compelling AI demo is easy to build. A reliable AI product is not. The gap between a working prototype and a system that performs under real user load, edge cases, and model updates is where most AI projects collapse. We've shipped across both sides of that line.

Production-hardened architecture designed for real traffic and failure modes
Model-agnostic approach, we pick the right tool, not the most hyped one
Guardrails and evaluation pipelines built in, not bolted on after launch
Cost and latency optimization from day one, not a retrofit after your cloud bill explodes

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Built on real engineering,
not hype.

AI is only as valuable as the engineering underneath it. We've been building intelligent software since before LLMs were a board-level conversation, from recommendation engines and computer vision to the generative AI products companies are shipping today.

That experience means we ask the right questions before we write a single line of code: does this problem actually need AI? What's the simplest model that solves it? How does this behave when the data is noisy, the load spikes, or the model gets updated? We've seen what breaks. We build around it.

FAQ

Frequently asked
questions.

What kinds of AI products does Metova build?

We build LLM-powered applications, ML-driven platforms, AI-native SaaS tools, intelligent automation systems, and data pipelines. Whether you're integrating AI into an existing product or building an AI-first platform from scratch, we handle the full stack.

Do you work with OpenAI, Anthropic, and other LLM providers?

Yes. We've worked across the major LLM providers, OpenAI, Anthropic, Google Gemini, and open-source models via Ollama and HuggingFace. We recommend the right model for your use case based on performance, cost, and latency requirements, not brand loyalty.

How do you handle AI reliability and hallucination issues?

Production AI requires guardrails, evaluation pipelines, and fallback logic. We build structured output validation, retrieval-augmented generation (RAG) where appropriate, and continuous monitoring systems that catch model drift and quality degradation before users do.

Can you improve or extend an AI product we've already started?

Yes. We regularly take over AI products mid-build or help teams scale beyond their initial prototype. We'll audit what exists, identify where the architecture breaks down at scale, and build forward from there, rather than starting over unnecessarily.

What does a typical AI product engagement look like?

Most engagements start with a scoping sprint, defining the AI use case, evaluating model options, and mapping the data architecture. From there we move into iterative development, with working software shipped in the first two weeks. We avoid month-long planning phases and ship early.

Projects that involved
AI & intelligent systems

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FitOn • Q1 2023

AI-powered personalization for 8M+ fitness users

Recommendation engine and adaptive workout flows on iOS and Android. ML models trained on user behavior to surface the right content at the right moment.

MyBambu • Q2 2023

Intelligent financial guidance for underserved communities

AI-driven financial insights and fraud detection for a mobile banking platform serving the unbanked. Built with compliance-grade data handling and sub-100ms inference.