AI & Machine Learning
AI agents and models that run in production, not in a slide deck.
Overview
AI work fails most often at the handoff between a working prototype and a production system somebody actually depends on. XETUP builds both sides of that gap with the same team: the model or agent logic, and the infrastructure that keeps it running reliably once real users are on it.
The work spans conversational AI agents that handle real customer interactions, LLM integration into existing business systems, computer vision for physical operations, and predictive models built on a client's own data rather than generic templates.
What's Included
- AI agent design and deployment for customer service, operations, and internal workflows
- LLM integration into existing applications, including retrieval-augmented generation over private data
- Computer vision systems for inventory, monitoring, or quality inspection
- Predictive models trained on client-specific data, not off-the-shelf datasets
- Production infrastructure for AI systems: inference hosting, monitoring, and cost control
- Ongoing model evaluation and iteration once the system is live
Built For
- Businesses replacing manual, repetitive processes with an AI agent that actually understands context
- Companies that already have data and need it turned into a working prediction or automation system
- Teams that tried an off-the-shelf AI tool and hit its ceiling
- Organizations that want the AI system built by the same team maintaining the infrastructure it runs on
- Customer service or operations teams drowning in repetitive tickets and manual triage
- Manufacturing and retail operations wanting computer-vision quality or inventory checks on the floor
- Enterprises piloting an AI governance and evaluation framework before a wider rollout
How We Actually Work
Named practices, not marketing language. This is the specific methodology applied to this service line, described as what it is, not as a certification XETUP does not hold.
Retrieval-Augmented Generation (RAG)
Answers are grounded in your own verified documents and data at query time, instead of relying only on what a general-purpose model memorized during training, which is what keeps responses accurate to your business.
LLMOps & MLOps
Model and prompt versions, evaluation results, and rollback paths are managed the same way software releases are, not left as a one-off script someone ran once.
Evaluation Harnesses
New agent behavior is scored against a fixed test set before it ships, so a prompt change that quietly breaks something is caught before a real customer sees it.
Guardrails & Content-Safety Layers
Input and output filtering, plus resistance testing against prompt injection and jailbreak attempts, before an agent is allowed to talk to a real customer unsupervised.
Model-Agnostic Architecture
The system is built to swap the underlying LLM provider without a rewrite, so a pricing change or a model deprecation from one vendor doesn't strand the whole system.
Reasons Teams Choose Us for This
One team, prototype to production
The same engineers who build the agent or model also build the infrastructure that keeps it running, closing the gap where most AI projects die: a demo that never becomes a real system.
Model-agnostic by design
Not locked into one AI vendor's pricing, rate limits, or roadmap. The underlying model can change without a system rewrite.
Grounded in your data, not templates
Every system is built on the client's own data and workflows, which is what actually lowers hallucination risk, not a generic off-the-shelf prompt.
Cost control from day one
Inference spend is monitored and capped from the first deployment, so usage growth doesn't turn into a surprise bill.
Questions About This Service
Almost always the former. Training foundation models from scratch rarely makes sense for a business problem. We build on proven LLMs and fine-tune or ground them on your data, which ships faster and costs less.
Through retrieval grounded on your actual data instead of the model's general knowledge, plus guardrails and evaluation before it ever talks to a real customer, and monitoring once it does.
Off-the-shelf tools work until you need something specific to your business: private data, a particular workflow, or integration with your existing systems. That's exactly the gap custom AI engineering closes.
You do. There's no lock-in to a platform you don't control, the system runs on infrastructure you own or we manage on your behalf, your choice.
Hosting for inference, cost monitoring so usage doesn't spiral, logging for every model response, and a plan for what happens when the underlying model provider changes their API.
Tell us about your project
We'll respond with a concrete plan, not a sales pitch, within hours.