Systems in production
The model, the data it reads, the limits it works within, and the testing, monitoring, and security that let people rely on it: AI engineering services for companies across the United States.
The discipline
Anyone can get a useful answer from a language model once. A business needs the right answer on the ten‑thousandth try, on a Tuesday, from a document it has never seen, without exposing what it should not. AI engineering is choosing the model for the job, giving it your documents through retrieval rather than hope, and setting what it may and may not do.
It is testing against real cases before anyone depends on it, then running in production with monitoring, access control, and a sensible bill, so that when a model changes, your system does not. The model is a part. The system is what you own.
What it includes
Where the system runs, what it may reach, and what happens when a call fails are settled first, because everything else inherits them. The model is chosen last, by measurement: accuracy on your data, latency your users will tolerate, privacy your counsel will accept, and a cost that survives volume.
The model answers from your contracts, tickets, and records, with sources, and people see only what they could already see. Retrieval is where a system fails without anyone noticing, so much of our care goes to how documents are split, indexed, permissioned, and kept current.
Before anyone relies on it, we build a test set from your real cases and measure the system against it, then keep measuring after every change. You see the numbers, not a demonstration.
An agent calls tools: a search, a database, a ticketing queue, a mailbox. We define each tool narrowly, bound its steps and what it may do without approval, log every call, and test the whole loop like any other code.
The system lands inside your environment, speaking to the software you run, with traces for every call, alerts for drift and failure, and a record of what the model was given and returned, because production AI systems change under you.
The model gets the least access its job needs, redaction where the data calls for it, and an audit trail your reviewers can read. Every document it reads is treated as untrusted, because text can carry instructions. Spend is metered and capped, so a busy week reaches a report before an invoice.
The method
We start with the work, not the technology: what is done by hand, what it costs, where it goes wrong. Some should be automated and some should not, and we say which.
A written architecture covers the model, the data, the limits, the tests, and the cost. Decisions that are expensive to reverse get made here, slowly, and the design is yours whether or not you proceed.
A working system runs on your real data, early, and is measured against your cases. We show you what it gets right and wrong, and fix the second list before going live.
It goes into production with monitoring, alerts, and a log of every action. We stay through the first weeks of real use, then hand over documentation your people can run from, or keep running it ourselves.
The clients
Nearly all the work happens on calls, shared screens, and inside your systems. When being in the room matters, we come to you.
An owner wants one dependable system for the job that keeps them at the desk after hours, built by someone senior who answers the phone afterwards, with no platform to learn.
A pilot worked in the demo and failed in the department, or an executive was asked to do something with AI and would like it to be the right something. We find out why, or find the first problem worth solving.
Engineering and data teams want a specialist beside them for architecture review, evaluation, and the questions their security review will ask, working in their repositories and under their process.
Common questions
If you have a steady stream of AI work and someone senior to direct it, hire. If you have a few specific systems and no one to design and check them, a practice gives you that person for as long as the work needs, and no longer. Many clients do both.
It is the engineering of systems built on large language models, given your data, your tools, and your rules. The interface may still look like a chat window. Everything behind it is different. If the problem involves reading, writing, sorting, or deciding from text, it is probably what you need.
Yes. We deploy into your cloud accounts, your private network, or, when the data requires it, hardware you control. We work with the major commercial models and open-weight models you can run yourself, and are loyal to none.
That is part of the design. We size models to the task, cache what repeats, cap spending, and report usage plainly, so the monthly figure is estimated before launch and held after.
A first call
A first conversation, by phone or email, with the person who would do the work. No deck, no discovery workshop, no obligation.
Your message reached info@underbeat.com. We respond when we can take the time it deserves.
If it cannot wait, call 718.501.4294.