01
AI systems that run every day
Software that does a job your team already does, on a schedule or on a trigger, in your systems. Built to be restarted, watched, and handed over — not a prototype that lives on one laptop.
AI engineering
AI engineering is how we install agent systems.
AI engineering is designing, building, testing, and running AI software that does real work in your business every day. At Helixfield that work is the Field Survey, then a helper install or a shared-facts system, then teaching, then monthly care. We connect it to the data and systems you already pay for, check its answers against real examples before anyone relies on it, and roll it out in stages with a person who can stop it.
We lead with the newest tools that fit the job — OpenAI, Grok, Claude, Gemini, Cursor, or what ships next. Loop & Graph is how we organize the work today — not a promise for forever. We are not locked to one AI brand. Today we work with OpenAI, Grok, Claude, Gemini, Cursor, or whatever fits the job.
- First step
- $9,500 Field Survey
- Fixed fee
- $24,000–$48,000
- Checked on
- Your real work
What it covers
Six things a buyer should expect.
Framed for the person who signs, not the person who codes. Each one is part of every careful install, sized to the job.
02
Quality checks before anyone relies on it
We write down what a good answer looks like, then score the system against ten to twenty real examples from your work. We keep scoring after launch so drift shows up as a number, not a complaint.
03
Your own records, when the job needs them
Some jobs need the system to look things up — contracts, price lists, past tickets, policies. We connect it to the right source, keep it read-only until trusted, and make sure it says it does not know instead of guessing.
04
Several steps in the right order
A job with several steps — read, look up, draft, check, file — becomes a pipeline with a clear order. Each step can be tested on its own, and a person steps in at the point that matters.
05
Safe rollout and day-to-day running
Read access first. Then drafts a person approves. Then, if earned, the system acts on its own for low-risk steps. A log, a cost line, and an off switch are part of the build.
06
Built into what you already pay for
Microsoft 365, Salesforce, NetSuite, your ERP, your ticketing system. We build in, not beside. Permissions are written down. AI seats and API bills stay in your name.
How it runs
Design on paper. Build in stages. Score each one.
The same discipline we use on every setup, written out so you can hold us to it.
- 01
Name the job and the measure
One sentence for the work that repeats. One number that tells you it worked. If either is missing, we start with a Field Survey.
- 02
Look at the data and the systems
What the system must read, what it may write, and who owns each. We ask for read access only at this stage.
- 03
Design on paper first
The steps, the hand-offs, and where a person checks. We pick the AI tools from the job — the newest that fit, not last year’s stack.
- 04
Build in stages, score each one
Read-only first. Each stage is run against real examples from your work and scored before the next stage starts. Invented numbers fail the test.
- 05
Roll out with a switch
A named owner, a log you can read, a monthly cost line, and a way to turn it off. Nothing leaves the company until a person says send.
- 06
Keep it current
Models and tools change. Monthly care re-scores the system when they do, and moves it when a better tool ships.
Which buy
A configured helper, or more engineering?
Most companies start with a standard setup. The Field Survey says which one you need — and says so in writing.
A helper install is enough when…
- One job, two or three tools, and a current AI platform can do the work once it has the facts.
- The system drafts and a person sends. No look-ups into your own records are needed.
- The risk is low enough that a weekly check by the owner is the right amount of oversight.
The install needs more engineering when…
- The system must look things up in your own documents or records to do the job.
- Several steps must hand work to each other in a fixed order.
- You need written proof of quality before go-live, and a way to keep measuring it.
- The systems involved have no ready-made connector, or the permissions need care.
- The work is regulated or high-stakes and has to be rolled out in stages.
Field Survey·Set up a helper·Several helpers, one set of facts
What you leave with
- Working software in your systems, with the code, settings, and permissions in your name.
- A written set of quality checks and the scores at launch.
- A runbook: how to restart it, how to read the log, how to switch it off.
- A monthly cost line from the AI vendors, in plain numbers.
- Thirty days of fixes after launch, then monthly care if you want it.
How it is priced
Engineering is scoped in the Field Survey ($9,500) and priced as one of the published setups: $24,000 to $48,000. When a build is bigger than the rate card, we write a fixed quote before we start. AI seats and API usage stay on your bill. If a buyer insists on hourly, the fallback is $285 per hour.
What this is not
Not a fifth service next to survey, install, teach, and stay. Not custom model training. Not a research project with no owner. Not a chatbot that talks to the public unsupervised. Not a promised savings number. Helixfield builds AI systems for one named job at a time, on the tools that fit it today, with a person who can still say no.
Questions
Direct answers
Briefing
Name the job that needs a careful install.
We will say whether a $9,500 Field Survey is the right first step — and whether the job needs a standard helper or more engineering.