LLM development company
engineered past the demo.
An LLM demo is easy. A reliable LLM system is the engineering. The demo answers three questions you picked. Production answers the ones a real user asks, on a budget, fast, without making things up. We are an LLM development company that builds for that gap: evaluation you can measure, token cost and latency you can predict, grounding and guardrails that hold when the input gets weird. For SaaS, healthcare, and expert-led teams.
Scoped estimate in 3 to 5 days. No obligation, NDA on request.
“Alex and his team built the core of our Healthcare SaaS. Their grasp of HIPAA and GDPR was crucial for our telemedicine features, and they added AI into the EMR so providers could make better data-driven calls. They know the Microsoft stack and held to WCAG 2.1 throughout. For a healthcare product that needs regulatory care and real engineering, HighCraft.io is the partner you want.”
Oleg Shumar
Owner, GetTrusted.io
Selected clients and shipped projects
Awesome Kyiv
Shelfit
Who you work with
We have shipped LLM systems that run in production
HighCraft is a senior team that pairs full-stack engineering with applied AI for healthcare, SaaS, and expert-led businesses. We have earned Top Rated and a 100 percent Job Success Score on Upwork, one five-star delivery at a time.
We built this at the model layer, not just around it. WebReaper, our open-source .NET scraper, ships an MCP server and a bring-your-own-model AI fallback, so the model is swappable by design. Quell renders its coaching audio with on-device neural text-to-speech, no network round-trip. We keep systems model-agnostic across 3 providers, Azure OpenAI, Claude, and open models, so a better or cheaper model is a config change. On a healthcare platform we shipped AI that reads a patient lab PDF and turns it into plain language a provider can use mid-call. You work with the engineers who built those, not a sales layer in front of them.
2 weeks
idea to working prototype
End to end
prototype to production
Senior
engineers, no handoffs
The honest worry with an LLM is not the happy path. It is the prompt-injection, the data leak, and the confident wrong answer the demo never showed you. We design against the OWASP Top 10 for LLM Applications from the start: input handling, output checks, least-privilege tool access, and an eval set that catches a regression before your users do. A model you cannot measure is a model you cannot trust in production.
What we actually engineer
The model layer, where production is won or lost
Four engineering jobs that turn a clever demo into a system you can run.
Context engineering and retrieval
What you put in the prompt matters more than the prompt wording. We design the retrieval, the chunking, and the context window so the model sees the right facts and not a wall of noise. The unglamorous part, keeping the index fresh and the context tight, is most of the accuracy and most of the work.
Evaluation harness and LLMOps
We build an eval set from your real cases and score every change against it, so you know a tweak helped instead of hoping. Then we monitor the same numbers in production: quality, cost, latency, and the answers that drifted. You cannot improve what you do not measure, and a vibe check is not a measurement.
Fine-tuning, RAG, or prompting
Three tools, three costs, one right answer per problem. Prompting is cheapest and often enough. RAG grounds the model in your data. Fine-tuning is for tone and format at scale, not for facts. We pick the cheapest option that clears your accuracy bar, and tell you why, before the bill arrives.
Model selection and routing
We keep systems model-agnostic across Azure OpenAI, Claude, and open models, so switching is a config change, not a rewrite. A router sends the easy calls to a small cheap model and saves the frontier one for the hard ones. We will run open, self-hosted, or on-device models when privacy, cost, or offline use makes that the right call.
When you do not need an LLM at all
Not every problem wants a model. A rules engine, a search box, or a plain database query often beats an LLM on cost, speed, and predictability, and we will say so before quoting one. An LLM earns its place when the input is messy language and the judgment is fuzzy. For anything a deterministic system already does well, we point you there instead of selling you tokens you will pay for forever.
100%
Job Success on Upwork
5.0
Average client rating
Top Rated
Agency on Upwork
11 yrs
Engineering leadership
HIPAA
Aligned delivery
Recognition
Awards and accreditations
Verified on Upwork and recognized by independent agency directories.
Built for the rules healthcare runs on. Practices documented, not implied.
Security & trustAI Prototype Sprint
Validate the workflow before you fund the platform.
A two-week sprint that turns a complex workflow into a working prototype, architecture direction, and a build estimate you can act on.
- Working prototype
- Workflow map
- Architecture recommendation
- AI opportunity and risk assessment
- Delivery roadmap
- Fixed or phased build estimate
Two weeks, one fixed scope. You own everything we build, whether or not you continue.
Week 1
Discover the workflow, build the spine
Week 2
AI where it pays back, then prototype + estimate
Engagement models
Four ways to engage, and a low-risk way to start
We fit the model to the project and the risk, not to our invoice. Most clients start with a two-week discovery sprint that turns the idea into a working prototype and a real estimate, then move into whichever model fits the build.
Time and materials
You pay for the hours you use, billed weekly or monthly. The right call when scope is still moving and you want to steer as you go.
Dedicated team
A senior team embedded with yours and billed monthly, scaling up or down as the roadmap changes. Built for ongoing work, not a one-off.
Fixed price
Agreed scope, agreed price, agreed date. Works when the requirements are already clear and you want certainty before you sign.
Fixed milestones
Phased delivery, paid one milestone at a time. A way to take on a larger build and de-risk it stage by stage.
What clients say
Clients trust us with messy, real-world software
From regulated healthcare workflows to payment-heavy platforms and internal business systems, the common thread is delivery that survives production.
Alex and his team built the core of our Healthcare SaaS. Their grasp of HIPAA and GDPR was crucial for our telemedicine features, and they added AI into the EMR so providers could make better data-driven calls. They know the Microsoft stack and held to WCAG 2.1 throughout. For a healthcare product that needs regulatory care and real engineering, HighCraft.io is the partner you want.
Oleg Shumar
Owner, GetTrusted.io
They were absolutely phenomenal. The team put in a lot of work to break down what was required of the project and gave an excellent presentation on the process. I highly recommend them and will be working with them again in the future.
Kayode Leonard
Founder, Project Wolf
Really enjoyed working with HighCraft.io. They are true professionals that know how to get things done. They were hardworking and skillful, exactly what we were looking for.
Maxim Grossman
Executive, Enigmex Technologies
HighCraft team did a great job creating a brand new site for my company, and I am loving it. It is exactly what I wanted and the team were true professionals and very nice to work with.
Alina Virstiuk
Founder, AwesomeKyiv
What we do
Three ways we turn complex workflows into working software
Start with a prototype, add AI where it creates leverage, or build the full production platform.
Working prototypes
A working prototype built around the real edge cases, so you can validate scope before funding a full build. The cheapest way to find the edge case nobody mentioned.
AI-enabled features
AI inside the product you already run: intake, search, summarization, classification, recommendations, or workflow assistance, with evaluation and guardrails. Built so a real user opens it twice.
Production platforms
Custom platforms built for real users: integrations, permissions, billing, audit trails, and maintenance. HIPAA-aware where it has to be.
Free vendor-risk check
Before you build, check the risk first.
Answer a few plain-English questions and get a vendor-risk read on ownership, proof of work, data exposure, and handover gaps before you fund the build.
- Takes about 3 minutes
- Built for vendor decisions
The page shows the first risk instantly. Email sends the full report.
Related work
Where the model layer fits the rest of the build
The LLM is one layer. These are the ones above and below it.
Selected work
Software that works, in production
Our clients get to focus on their business, instead of babysitting the stack that holds it together. Client cases below are anonymized where compliance demands; the rest ship under their own names.
How we build
How we build AI workflows that stay controllable
Agentic does not have to mean opaque. We put the controls where the risk is: permissions, approvals, and audit around every AI-assisted step.
Frontend
The product your users and staff actually work in.
API
Typed contracts and validation at the boundary.
Workflow engine
The deterministic spine: states, rules, and handoffs.
Agentic workflow layer
Inspects context, suggests next steps, and triggers tools, with human approval where it matters.
AI / LLM services
Models behind evaluation and fallback logic, not raw and unchecked output.
Integrations
EMR, Stripe, CRM, scheduling, and internal APIs.
Audit, monitoring, permissions
Every AI-assisted step logged, observable, and role-gated.
Controls, not black boxes
- Human approval for sensitive actions
- Tool calls scoped by permissions
- Audit logs for every AI-assisted step
- Evaluation and fallback logic, not raw model output
- Role-based access throughout
- Observability in production
- Integration with EMR, Stripe, CRM, scheduling, or internal APIs
FAQ
Hiring an LLM development company
What buyers ask before they start.
What is an LLM development company, and how is it different from generative AI development?
An LLM development company engineers the model layer: context and retrieval, evaluation, fine-tuning versus RAG versus prompting, model routing, token cost, latency, and guardrails. Generative AI development is the broader build around it, shipping AI features, agents, and chatbots into a product. We do both. This page is the engineering discipline underneath, and the sibling generative AI development page is the product framing on top.
Fine-tuning, RAG, or prompting: which do we need?
Usually prompting first, then RAG, and fine-tuning last. Prompting is cheapest and clears the bar more often than people expect. RAG is how you ground answers in your own data and keep them current. Fine-tuning is for consistent tone, format, or a narrow skill at scale, not for teaching the model new facts. We pick the cheapest option that hits your accuracy target and tell you why.
How do you control token cost and latency?
We measure both per request, then engineer them down. Routing sends easy calls to a smaller, cheaper, faster model and reserves the frontier one for hard ones. Tighter context and caching cut tokens. Streaming and smaller prompts cut perceived latency. The point is a cost and speed budget you can predict, not a bill that surprises you at the end of the month.
Can you run open, self-hosted, or on-device models?
Yes. We keep systems model-agnostic across Azure OpenAI, Claude, and open models, so the choice stays open. We run open or self-hosted models when data cannot leave your environment or the volume makes a hosted bill painful. We have shipped on-device neural text-to-speech in a product that works offline with no network round-trip, so we know the trade-offs first-hand.
How do you evaluate and monitor LLM output in production?
We build an eval set from your real inputs and score every change against it, instead of trusting a hand-wave. In production we track the same signals: answer quality, token cost, latency, and the cases that drifted. When a model update or a prompt change regresses, the eval catches it before your users do. That measurement loop is what separates a system from a demo.
How much does LLM development cost?
Send the use case and we reply with a scoped estimate, usually within 3 to 5 business days. Cost tracks the accuracy bar, the data and retrieval work, and the evaluation, since closing the last gap from a good demo to a reliable system is most of the budget. Running cost is separate and we engineer it down on purpose. You can work hourly, fixed price, or as a dedicated team.
When are you not the right fit?
If a rules engine, a search box, or a plain query already solves your problem, we will point you there instead of selling you a model that costs more and answers less predictably. An LLM earns its place with messy language and fuzzy judgment, not with work a deterministic system already does well. We tell you which case you are in before the build, not after.
Start a project
Tell us about your project
Send the shape of the problem, even if the requirements are still blurry. We reply with a scoped estimate, usually within 3 to 5 business days. No obligation, NDA on request.
- A senior engineer reads every brief, not a sales rep.
- If an off-the-shelf tool fits better, we will tell you.
- NDA on request before you share anything sensitive.
Prefer email? Write to business@highcraft.io
Rather talk it through? Book a 30-minute estimate review
“They were absolutely phenomenal. The team put in a lot of work to break down what was required of the project and gave an excellent presentation on the process. I highly recommend them and will be working with them again in the future.”
Kayode Leonard
Founder, Project Wolf