Hire generative AI engineers
who ship past the demo
The prototype worked. Everyone in the room nodded. Then it met real users, real data, and a Monday, and the answers started drifting somewhere between confident and fictional. That is the moment teams hire generative AI engineers who have shipped this before, because the model is the easy part. Retrieval, evaluation, cost control, and an audit trail are the actual product. We build AI features inside real systems for healthcare, SaaS, and expert-led operators whose workflows outgrew the off-the-shelf tools.
Scoped estimate in 1-2 business days. No obligation, NDA on request.
Selected clients and shipped projects
Awesome Kyiv
Shelfit
A senior team, not a sales layer
HighCraft.io is a senior team that pairs full-stack engineering with applied AI for healthcare, SaaS, and expert-led businesses. We earned Top Rated and a 100 percent Job Success Score on Upwork one delivery at a time.
You work with the engineers who build the software, not an account manager in front of them. We ship systems that hold up in production under real scrutiny, with the evaluation, guardrails, and audit trails that let you stop checking the work by hand.
2 weeks
idea to working prototype
End to end
prototype to production
Senior
engineers, no handoffs
The first objection is always the same: how do we stop it saying something embarrassing to a customer. The answer is unglamorous engineering. Scoped retrieval, refusal paths, evaluation sets built from your worst real cases, and human review where the stakes justify it. We map those controls to the NIST AI Risk Management Framework, so your risk reviewer gets a document to read instead of a demo to trust.
The model is the easy part
Four things decide whether an AI feature survives contact with real users.
From prompt to product
We take the prompt that worked once and rebuild it as a feature with retries, timeouts, fallbacks, and a spend ceiling. Users get an answer, and finance gets no surprises in the API bill.
Retrieval over your own data
We connect models to your documents, tickets, and records with chunking, embeddings, and permission checks that respect who is allowed to see what. Answers cite their sources, so nobody has to take the model's word for it.
Evals before release, monitoring after
We build test sets from your real edge cases and score every prompt or model change against them. Accuracy, latency, and cost sit on a dashboard instead of in somebody's gut feeling.
Built into the stack you already run
.NET, Azure, React, Stripe, HIPAA-aware where the data demands it. The AI ships inside the product your team maintains, not beside it in a side app that quietly becomes nobody's job.
Fixed scope. Approved releases. Audit trail.
You see the plan, the price, and the ship date before we write code. Every release goes through code review and approval, and the history stays readable long after the launch call.
100%
Job Success on Upwork
5.0
Average client rating
Top Rated
Agency on Upwork
11 yrs
Engineering leadership
HIPAA
Aligned delivery
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
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.
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. They were very attentive and kept open lines of communication throughout. The quality of the code was outstanding. Great knowledge of agile development and testable code. 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
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.
- 01
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.
- 02
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.
- 03
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.
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 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
Questions buyers ask before they hire generative AI engineers
What buyers ask before they start.
What does a generative AI engineer on your team actually do?
Builds the feature end to end. Data access, retrieval, prompts, evaluation harness, API, UI, monitoring, and the cost guardrails around all of it. Honestly, prompt writing is maybe five percent of the work.
How quickly can engineers start?
Usually within two weeks of a signed scope. The first week is discovery: your data, your workflow, your constraints. You get a written scope with a price and a ship date before anyone commits a line of code.
Can your engineers work alongside our in-house developers?
Yes, and it is the common setup. We join your repo, your board, and your review process, or we run a separate track and hand it over with documentation your team can actually follow. Either way your developers keep the keys.
Who owns the code, the prompts, and the models?
You do, all of it, including evaluation sets and infrastructure definitions. We hold access, not assets. If you walk away after the MVP, you walk away with everything that runs it.
How do you keep sensitive data out of the wrong places?
Permission-aware retrieval, data minimisation before anything reaches a model, logging that redacts what it should, and deployment inside your cloud tenancy when the data calls for it. For healthcare workflows we design HIPAA-aware from the first sprint, not during the security questionnaire panic.
When are you not the right fit?
If the spec is frozen down to the prompt and you only want hands on a keyboard, skip discovery and hire for throughput instead. If an off-the-shelf tool already covers the workflow for a monthly subscription, buy it and spend the budget elsewhere. And if nobody internally can get us access to the data the model needs, fix that first. A generative AI feature with no data behind it is just an expensive autocomplete.
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 1-2 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