RAG development services
grounded in your data.
You have a knowledge base. The AI ignores it. Ask it a real question and it guesses, fluently, from training data instead of your documents. RAG development services fix that. We retrieve the right passage from your own content first, then let the model answer from it, and cite the source so a reply traces back to a document. The buyer wants accurate, grounded, cited answers. That is the work. For SaaS, healthcare, and expert-led teams.
Scoped estimate in 3 to 5 days. No obligation, NDA on request.
“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
Selected clients and shipped projects
Awesome Kyiv
Shelfit
Who you work with
We have shipped retrieval that grounds real answers
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 the retrieval, not just a prompt around it. On a healthcare platform we shipped AI that reads a patient lab PDF and answers in plain language grounded in that document, so a provider gets the source, not a guess. We built the embeddings and indexes an LLM needs, on data synced across systems and tested past 1,000,000 records, and we keep it model-agnostic across Azure OpenAI, Claude, and open models. You work with the engineers who built that, 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 make-or-break of RAG is retrieval, not the model. If the right passage never makes it into the context, even a frontier model answers from a guess. We build the retrieval the way Microsoft’s retrieval-augmented generation guidance lays it out: chunk the source well, embed it, store the vectors, and rerank what comes back so the best evidence lands on top. Then the answer cites the passage it used, so a wrong one is traceable instead of mysterious.
What we actually build
What RAG has to get right to be trusted
The retrieval a chat box hides and a grounded answer depends on.
Retrieval architecture, end to end
Chunking that keeps a thought intact, embeddings that match meaning not keywords, a vector store sized to your corpus, and a reranker that pushes the best passage to the top. Most RAG answers go wrong here, two steps before the model. Get retrieval right and the rest gets easy.
Grounding and citations
Every answer traces back to the passage it came from, with a citation a user can click and check. We constrain the model to answer from retrieved context, so an empty result returns "I do not know" instead of a confident invention. A reply you can audit is a reply you can ship.
Retrieval-quality evaluation
We measure recall and precision on a labelled set of your real questions, not a vibe. If the right passage is in the top results, the model has a chance. If it is not, no prompt saves the answer. We score retrieval first, then the generation, so you know which half to fix.
An index that stays fresh
Documents change, and a stale index quietly answers from last quarter. We re-embed incrementally as sources update, so a new policy is searchable the day it lands, not the next full rebuild. The unglamorous freshness work is most of the maintenance and most of the trust.
When RAG is overkill
Not every question needs retrieval. If your knowledge is a small, fixed FAQ that fits in the prompt, put it in the prompt and skip the index, the vector store, and the moving parts. RAG earns its cost when the corpus is large, changes often, or is too big to paste into a context window. We tell you which case you are in before the build, not after the invoice.
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 retrieval fits the rest of the build
Retrieval is one layer. These are the ones around 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 a RAG development team
What buyers ask before they start.
What is RAG, and what do RAG development services include?
RAG is retrieval-augmented generation: you retrieve relevant passages from your own content, then the model answers from them instead of from training data alone. The services include the retrieval architecture (chunking, embeddings, a vector store, reranking), the grounding and citations, the evaluation that measures whether retrieval found the right passage, and the incremental re-indexing that keeps it current. The model is the small part. Retrieval is the work.
How is RAG different from fine-tuning?
RAG retrieves facts at answer time and grounds the reply in a source you can cite. Fine-tuning bakes tone, format, or a narrow skill into the model weights, and it is the wrong tool for facts that change. If your problem is "answer from our current documents," that is retrieval, because re-indexing a changed document is cheap and retraining is not. We reach for fine-tuning for consistent style at scale, not for keeping knowledge fresh.
How do you keep the AI from making things up?
We make retrieval do the heavy lifting, then constrain the model to what it found. The right passage has to reach the context first, so we tune chunking, embeddings, and reranking until it does, and we attach a citation to every answer. When retrieval comes back empty, the model says it does not know instead of inventing one. You cannot drive invention to zero, but a grounded, cited answer is one you can audit and bound.
How do you keep the index fresh as our documents change?
We re-embed incrementally. When a document is added or edited, that piece is re-chunked and re-indexed, so a new version is searchable without a full rebuild of the whole corpus. We track what changed and when, and fail loudly if a source feed breaks. A retrieval system that silently answers from last quarter is worse than one that admits it is behind, because people still trust it.
How do you measure retrieval quality?
On a labelled set of your real questions, with the right passages marked, we score recall and precision: did the correct passage make the top results, and how much noise came with it. That separates a retrieval problem from a generation problem, which a single end-to-end accuracy number hides. We fix retrieval first, since no prompt rescues an answer when the evidence never arrived. Then we score the generation on top.
How much do RAG development services cost?
Send your corpus size, the sources, and the accuracy bar, and we reply with a scoped estimate, usually within 3 to 5 business days. Cost tracks how messy the documents are to chunk, how high the grounding bar is, and how fresh the index has to stay, since the evaluation and freshness work is most of the budget. You can work hourly, fixed price, or as a dedicated team.
When are you not the right fit?
If your knowledge is a small, fixed FAQ that fits in a prompt, you do not need a retrieval pipeline, and we will say so. We are also the wrong call when the real problem is upstream: dirty, scattered, or duplicated source data that no retrieval layer can rescue, which is data engineering first. RAG earns its cost on a large corpus that changes and has to be answered from accurately. We tell you which case you are in before the build.
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
“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