# AI Engineering & Machine Learning Systems

> AI engineering and applied machine learning: model integration, evaluation, observability, and the production systems that make AI reliable. By Sentient Arc.

AI engineering and applied machine learning: model integration, evaluation, and the production systems around the model that make it reliable.

## Key takeaways
- AI engineering is the discipline of turning a model into a reliable product: integration, evaluation, observability, and cost control around it.
- A prototype that dazzles in a notebook is not a product; in production the model meets messy data, latency budgets, cost ceilings, and failure modes no one scripted.
- Sentient Arc treats AI features like any other critical system, with clear interfaces, evaluation harnesses, and observability, so behavior is measured rather than hoped for.
- Both classical machine learning and large language models are applied, chosen per task rather than forced into one stack.

## The demo works. Production is different.
A prototype that dazzles in a notebook is not a product. In production the model meets messy data, real latency budgets, cost ceilings, and failure modes no one scripted.

Most AI projects stall in exactly this gap, between a promising demo and a system you can trust, monitor, and afford to run.

## Engineering discipline around the model
We treat AI features like any other critical system: clear interfaces, evaluation harnesses, observability, and cost controls, so behavior is measured, not hoped for.

We integrate the right model for the job, wrap it in evals and guardrails, and design the data and infrastructure it depends on to scale.

## What we build
- **Model integration**: LLMs and ML models wired into your product behind clean, typed interfaces.
- **Evaluation harnesses**: Automated evals that quantify quality and catch regressions before users do.
- **Data & inference infra**: Pipelines, vector stores, and serving designed for cost, latency, and scale.
- **AI strategy & review**: Architecture, feasibility, and build-vs-buy guidance grounded in delivery reality.

## Stack
- **Models:** Claude, OpenAI, Open-weight LLMs, Embeddings
- **ML & data:** Python, PyTorch, Pandas, Feature pipelines
- **Production:** Evals, Observability, Vercel, AWS

## FAQ
**What is AI engineering?**

AI engineering is the discipline of turning models into reliable products: integration, evaluation, observability, cost control, and the data and infrastructure around the model, not just prompting.

**Do you do machine learning, or only LLMs?**

Both. We apply classical machine learning where it fits, classification, anomaly detection, forecasting, and large language models where reasoning over language is the job.

**Can you rescue an AI prototype that stalled?**

Often, yes. The usual gap is the engineering around the model: evals, data quality, latency, cost. We harden those so a demo becomes a dependable system.

**Do you offer AI consulting?**

Yes, architecture review, feasibility, and build-vs-buy guidance, with the same systems-first lens we bring to delivery.

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Canonical page: https://www.sentientarc.com/capabilities/ai-engineering