LLM Development Services
Language Models Built With Limits
LLM development services start with the test set, so the model is scored on your real cases before any feature reaches a customer, and its spend stays under a ceiling.
★★★★★ 5-star rated on Google · 14 years · 300+ Las Vegas businesses
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Why Language Model Features Drift
Features drift without a fixed test set
A language model feature usually works in the demo and then slips in production. The inputs are messier than the examples, the wording changes, and the model vendor updates the model without anyone on your side noticing. The first sign is often a customer complaint, not a number.
The usual cause is not the model. Nobody wrote down what a correct answer looks like before the build started, so there was no way to measure the drift when it arrived.
What An Evaluation Set Is
A fixed test set, written before the build
An evaluation set is a list of real inputs from your business, each paired with an answer a competent person would accept. The owner of the outcome reviews it before the model is chosen, and it stays unchanged so every new version is scored the same way.
A first feature usually starts with a few dozen real cases, drawn from requests the business already gets rather than invented ones. The set grows each month as the log shows new kinds of input, and the score history is kept so a regression shows the day it happens.
Choosing The Model For Each Task
Model choice follows the task
| Task | What I measure | Cost control | Where it runs |
|---|---|---|---|
| Classify incoming requests | Accuracy against labeled cases | Short prompts and a daily token cap | Hosted API under your account |
| Summarize long documents | Facts kept and facts invented | Length limits and chunking | Hosted API or private deployment |
| Draft customer replies | Tone, accuracy and banned phrases | Output length cap and caching | Hosted API, with approval before send |
| Answer from internal files | Right source cited, wrong answers caught | Retrieval limits per query | Private index, as on the RAG page |
Cost Control From Day One
Spend limits set before launch
Language model features can run up a bill quietly, because every request costs something and a loop can repeat thousands of times overnight. The budget is written into the build: a cap per request, a daily ceiling and an alert when usage passes the expected line. Nothing goes to production with the ceiling switched off.
- A token cap on every request
- A daily spend ceiling with an alert
- Caching for repeated questions
- A weekly log of cost per feature
- A kill switch the owner can use
Prompt And Source Control
Prompts versioned like code
Prompts and the source files they use are stored in version control, with a note on every change. A change is run against the test set before it replaces the live version, and the old version is kept, so a rollback takes minutes rather than a new build.
How The Build Runs
Five steps from use case to live
1. Pick one use case
I start with one feature that has a clear owner and an outcome you can measure.
2. Write the test set
The owner and I write the cases and the accepted answers before any model is chosen.
3. Build and score
Each candidate model and prompt is scored on the same set, and the cheapest one that passes is chosen.
4. Gate the release
The feature ships only when it clears the score and the cost ceiling, with human review on customer-facing output.
5. Review monthly
The score, the cost and the logged failures go to the owner each month, and the test set grows with them.
Where I Have Built Gated Pipelines
Closest work and what it was not
The builds I can show run language models inside gated pipelines. The deliverable pipeline runs every piece of work through eight phases, each with one owner and a gate before the next, so a draft cannot skip review. The night shift builder runs overnight work through a scout, a veto gate and isolated workers, and the owner reads one morning brief.
Neither is an outside client's language model product, and I do not claim a client result from either. They show the testing and logging discipline a feature build would use.
LLM Development Portfolio
Builds I run, open to check
Website build · Henderson
GIOSTAR: the clinic website, booking and lead tracking
I built and run giostarwellness.com for a regenerative medicine and wellness clinic in Henderson: every service page, online booking, financing and a blog. A website lead is tracked end to end, from the form to a booked consult to an active patient, with no manual steps. I also built its live brand hub, so every page, post and print piece pulls the same colours, type and rules.
See the live sitePortfolio Examples
More of the work
Website build · Las Vegas
WebVegas: a Las Vegas web design brand and its booking funnel
See the live site
Brand mark · Event venue
Desert Inn Oasis: the refined mark and the plan for the new site
Site in build
Brand identity · Pharma
Mili Pharma: the butterfly mark and the lockups
Site in build
Social content system
Quotible: a testimonial-led social series
Content system
Want me to look at your site first?
A free audit, and a same-day reply from Justin, personally.
LLM Development Reviews
What owners wrote on Google
- ★★★★★
After going back and forth on whether to bring in outside help, we ended up working with Justin for some AI consulting on how to structure our operations. He laid out a clear plan, didn't overcomplicate it, and we've been running smoother since. Good fit for a service business. He knows Las Vegas and he knows this stuff.
Kristen S. · Google
- ★★★★★
Justin joined our team a few months before opening to replace a marketing agency that wasn’t performing. He stepped up fast. Within weeks he launched a fully redesigned website with new brand guidelines, built and delivered our marketing plan and AI strategy, and helped implement our system architecture so we could open on time. Since then, he’s made several critical enhancements to the site that helped us generate revenue in our first month. His engagement was supposed to end at opening, but he became such an integral part of our leadership team that we kept him on to help evolve our marketing and tech strategy as we grow. He’s easy to work with, pushes back when it matters, and moves easily between strategy, delivery, and operations depending on what’s needed. If you’re looking for a deeply experienced, knowledgeable, and dedicated marketing, AI, or technology consultant, I highly recommend Justin Harris AI Consulting.
Devin R. · Google
- ★★★★★
Justin has been a true Gem to have as part of our company. His professionalism, immediate attention to branding and extensive details in marketing has made so much improvement on our business. We are forever grateful to have such and amazing marketing asset and cannot thank you enough Justin!
Desi W. · Google
- ★★★★
Justin Harris was very helpful in resolving some issues we had with our Webflow website.
Kristy F. · Google
- ★★★★★
I had the pleasure of working alongside Justin and consistently saw firsthand the level of dedication and professionalism he brings to his work. He was always the first one in the office, setting the tone with his strong work ethic and commitment to excellence. What stood out most was how deeply he cared about every client. He approached each project with thoughtfulness, expertise, and a genuine drive to deliver meaningful results. He combines sharp technical knowledge with a practical, solutions-oriented mindset, which makes him especially effective in the AI space. Any organization looking for thoughtful, reliable, and highly skilled AI consulting support would be in great hands working with him!
Jenevine Y. · Google
- ★★★★★
Hired him to manage our AI systems and it's been pretty smooth. He knew what needed to be done and got it handled without a lot of back and forth. Good experience for our service business.
BC G. · Google
- ★★★★★
I follow Justin on social media and read about his work and his contributions to the AI industry. He really knows how to use the technology in creating a system that allows you to make your business work more efficiently and effectively.
Ana J. · Google
- ★★★★★
Justin was the person I would go to for advice about AI automation for my workload. He knew the subject well and didn't overcomplicate it. For anyone in Las Vegas running a service business and wondering if AI systems are worth looking into, talking to him first is a great move!
Yaneliz M. · Google
- ★★★★★
Working with Justin is always a pleasure. He is professional, trustworthy, and consistently delivers outstanding results. As a longtime Las Vegas local, he has an exceptional knowledge of the territory and truly understands the market. I highly recommend his services to anyone who is serious about growing their business!
k C. · Google
- ★★★★★
From what I've seen, Justin knows the AI automation space better than most people I've come across in Las Vegas. He's the kind of person who actually understands the technical side and can explain it without making you feel lost. Good guy, good reputation.
Afaryn F. · Google
- ★★★★★
I've Known Justin for a while now and he really knows his stuff when it comes to AI consulting. If people in Las Vegas were to ask me for recommendations in this space, he's the best guy for the job, hands down.
Mickey S. · Google
- ★★★★★
After going back and forth on whether to bring in outside help, we reached out to Justin for some AI consulting advice on where our service business could actually use automation. He was straightforward about what made sense and what didn't. Ended up being a useful conversation. Las Vegas has no shortage of people claiming to be experts, and he's one of the few who could back it up.
Vincent M. · Google
- ★★★★★
Known Justin for a few years now and the guy actually knows his stuff when it comes to AI automation. Friends in the Las Vegas service business space keep asking me who to call and I always point them his way.
Chris M. · Google
- ★★★★★
Justin is one of those guys who actually knows what he's talking about and doesn't cut any corners. Will definitely be working with him again.
David S. · Google
- ★★★★★
Justin knows what he's doing highly recommend using his service he will treat you right and give you tremendous value !👍
Gregory J. · Google
- ★★★★★
Known Justin for a many years and the guy actually knows what he's talking about when it comes to AI and automation. Not the type to just throw buzzwords around. Based in Las Vegas and well connected in the space. …
cindy D. · Google
- ★★★★★
Known Justin for a few years now. Guy knows the AI space better than most people I've come across, especially here in Las Vegas where there's no shortage of people claiming to be experts. He actually understands how AI automation works at a practical level, not just the theory. The kind of person who gives you a straight answer with kindness and patience
Katelyn H. · Google
- ★★★★★
Knew Justin before he started building AI systems for businesses, and watching what he's put together is genuinely impressive. He built out an AI automation setup for my website in Las Vegas and it changed how we handle leads completely. The guy actually runs this stuff himself, so he knows what works.
Justin E. · Google
LLM Development Services Cost
What sets the price
I quote a fixed price for a written scope after I have seen the use case and the data it would touch. Testing, integration and the cost controls are scoped as separate lines, so you can see what each part adds.
- Number of use cases in the first scope
- Data the feature must read and how clean it is
- Systems the output must write into
- Size of the test set and how often it grows
- Monthly monitoring after launch
Safety And Data Limits
What the model may see and do
A feature reads only the data its scope names. Customer records do not go to a model unless the owner has approved that flow in writing, and personal details are masked where the task does not need them. Whether a particular data flow meets your legal duties is a question for your own counsel, and the scope says so rather than guessing.
- Only the fields the scope names reach the model
- Personal details masked where the task does not need them
- Output checked before it reaches a customer
- No model writes to a record without approval
- Access logs kept for each feature
Monitoring After Launch
Drift caught by the monthly run
Each month the test set runs again against the live model. A drop in score, a jump in cost or a new kind of failure in the log triggers a review, and the owner decides whether to change the prompt, change the model or narrow the feature. Vendors update models without notice, so the monthly run is the most reliable early warning.
Who Builds The Feature
One builder from scope to log
I am Justin Harris. I have built websites and run digital marketing for Las Vegas businesses for fourteen years, and the AI systems I build use the same discipline I run on my own work. You talk to the person writing the test set, not to a handoff team.
Related AI Services
Other AI work I build
LLM Development Las Vegas NV
Where I meet product teams
Las Vegas
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North Las Vegas
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Boulder City
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Where Scoping Starts
One use case with a test set
The first conversation picks one feature that has an owner and a number it should move. From there I draft the first test set, so the scope arrives with the way to measure it already attached.
LLM Feature Questions
Answers before you commit
Cost And Data Questions
Money and data before the first build
Proof, not promises
Case studies I can show you
The 8-Phase Deliverable Pipeline: How AI Work Ships Clean at Speed
How AI marketing work ships clean at speed: an 8-phase deliverable pipeline with five deterministic gates, a non-author adversarial review, and a rendered-fidelity check.
Read the case studyThe Night-Shift Builder: Autonomous AI Builds That Ship While You Sleep
How JustinHarris.AI runs autonomous AI builds overnight: a Kanban board dispatches work to the right specialist agent, gates it, and ships it by morning.
Read the case studyAI Services Las Vegas
The rest of what I build
LLM Development Services Next Step
A written test plan first
Send me one use case you want to run on a language model, and I will reply with a written plan: the owner, the test set, the cost ceiling and the release gate. You decide before anything is built.