LLM & RAG products

Custom LLM products where the language model is the point, grounded in your own data with RAG. It answers from your content, a catalogue, your documents, your records, not the open internet, and we pick the model that fits your budget and speed. We build the whole product around the model: the pipeline that keeps the data current, the retrieval that grounds it, the interface people work in, and the evaluation that keeps answers honest. We've put grounded and generative LLM products into production.

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what it is

A custom LLM product puts a language model at the centre of your software, and RAG is what makes it answer from your business instead of the open internet. It's one part of our wider AI development services, and the two terms do different jobs. Here's the line between them.

LLM (large language model)

The model that writes the answer. It's fluent from broad training, but on its own it can't speak to your business, and it fills the gaps by guessing.

RAG (retrieval-augmented generation)

The layer that grounds it. It pulls the relevant information from a data source you control and hands it to the model first, so replies come from your content, not the model's training. It's the side this page is about.

We start with your data: what you hold, which fields matter, and how they connect. On a book-recommendation product for an online retailer, that meant mapping books, authors, categories, prices, and order history before any model touched it.

We turn your content into embeddings and store it in the vector database that grounds the product, then keep it fresh with a scheduled sync, so it retrieves current information at query time instead of working from a stale snapshot. That grounding is what RAG chatbot development delivers: on a book-recommendation product it means replies drawn from real titles the shop actually stocks, not guesses.

Our LLM integration services wire the retrieved data and the user's message into the model, handle the chat API, and manage session-based or persistent memory so a conversation holds context instead of resetting on every message.

A system prompt keeps the product on-topic and steers it away from sensitive or off-limits subjects, so it behaves consistently in front of real users.

We choose the model for your budget and speed, across Claude, GPT, Gemini, or an open-source one, and we can swap it later.

We test answer and recommendation quality, tune the system, and give you an admin dashboard with analytics that cover sessions, click-throughs, and what's actually working, so you can keep running cost in view.

what we build

process

Understand & connect your data

We map your data sources, the records, fields, and content that matter, and connect to them. Where the product needs to stay current, we keep the data in sync on a schedule so answers reflect live information instead of a stale snapshot.

Embeddings & retrieval

We turn your content into embeddings and store it in a vector database. When a question comes in, the system finds the closest matches and passes them to the model to answer from.

Model & prompt

The retrieved information and the user's message go to the language model, which writes the reply. A system prompt sets what it talks about and what it avoids, and we add conversation memory where the product needs to hold context.

Evaluate, tune & ship

We test answer quality, tune the retrieval and prompts, and give you the admin tooling and analytics to run it. Then we make an explicit, costed call on how it runs, so it performs without surprises on the bill.

process

Understand & connect your data

Embeddings & retrieval

We turn your content into embeddings and store it in a vector database. When a question comes in, the system finds the closest matches and passes them to the model to answer from.

Model & prompt

Evaluate, tune & ship

model choice

We're not tied to one provider. We choose across Claude, GPT, Gemini, and open-source models based on what your product needs: how fast it has to answer, how much it costs to run, and how much control you want over hosting.

We've made that call in real delivery. On one production product we started on one provider's model, then migrated the chat to a different one when it became the better fit, while keeping the first provider for the embeddings because mixing models by task came out cheaper. We also work the managed-versus-self-hosted trade-off with real numbers before committing. A managed API is usually faster to ship, needs less maintenance, and costs less to run at moderate traffic, so it's often the right call. But it's a call we make deliberately, project by project, rather than by habit.

stack

Anthropic Claude

OpenAI GPT

Google Gemini

Meta Llama

Mistral

Responsible AI, grounding & security

The main worry with a language model is that it makes things up. A grounded product cuts that off at the source: because it answers from your data, it can't recommend a book you don't stock or cite a document you don't have. A system prompt keeps it on-topic and steers it clear of sensitive subjects.

We build this into products that carry real weight. SOARR, the clinical documentation platform we built, pairs advanced reasoning with anti-hallucination safeguards, so the notes it generates stay faithful to what was actually said. Grounding, guardrails, and evaluation are how the product earns trust with the people using it.

projects

Our generative AI development services do real work across our portfolio: notes drawn from clinical conversations, first-round interviews run by an AI, spoken exchanges turned into facts you can trust. Explore the projects below to see how the model becomes the product.

Curious to see more?

See Our work

partners whotrustus

From startups to scale-ups and industry giants—brands across various industries choose us as their trusted partners. They rely on us to transform their ideas into stunning products, deliver innovative solutions, and enhance existing projects to help them stand out in the market.

We were able to create a full-feature Romanian/French team in a snap. The collaboration was always easygoing.
Cedric Delfosse
Tech Expansion Director @ Deezer
Their team has an excellent agile approach that allows them to complete sprints quickly and in an organized manner.
Janice Davis
COO & CTO @ Equality AI
Consistently exceeding expectations, Wolfpack Digital has provided impressive deliverables on time to meet benchmarks. The team has been accessible and provided a detailed project overview to ensure a streamlined collaboration. They have supported a successful collaboration with a brand identity.
Krisztina Orosz
Chief Experience Officer, Anyline GmbH
From the first day, Wolfpack was extremely committed, reliable and delivered solutions in outstanding quality. I also enjoyed that the Wolfpack team made lots of very useful suggestions and deeply cared about our company. Working with Wolfpack feels like working with your own team.
Axel Heinz
CEO and Founder of Makerist
They nicely replicated our brand elements into the design without much input from us. They had good ownership and delivery of the agreed tasks. Their help was essential in allowing us to focus on core back-end functionality with our in-house team. Wolfpack Digital was willing to adapt to our needs and learned the specific technologies we wanted to use on the project.
Tibor Barna
CTO at GoodBox
The highly functional app has drawn enough users to make up 20% of the customer base across platforms. Wolfpack Digital is a communicative and reliable team that’s easily capable of integrating with the in-house development team. We communicated well. They responded to us rapidly, demonstrating their understanding of our business and any errors we might face.
Peter Kondrup
CTO at FirstAgenda
They are informed, easy to work with, and communicative, bringing plenty of experience to the table. Overall, the quality of the work is brilliant. My business partner was skeptical about outsourcing it initially, but he ended up having his current applications rebuilt by Wolfpack too.
Raymond Valentine
CEO at FlexiDrive
Working with Wolfpack Digital has been a blast. Gina and her team are very product-oriented and made sure they understood our needs before diving into building the app. This upfront effort paid off by meeting the deadline weeks ahead of schedule.
Flaviu Simihaian
CEO and Co-founder of Amplicare
We were happy with Wolfpack Digital’s work, it's proven, very useful and we were able to manage projects from long distance. Thank you for your services!
Chen Lev
CEO at Storyball
Wonderful culture with the company and people. Incredibly professional, competent, and knowledgeable.
Christopher Dyson
Founder @ Pearls
Their management and communication are impressive, as well as their maturity in technical decision-making.
Radu Gatej
CTO @ BrainCapture
They have understood the project needs very fast and always come up with the best solutions.
Alina Tarcea
Product Owner @ Bank DiMare
The speed at which development progressed and the quality of the final result was impressive.
Brendan Staunton
CEO @ Amara Therapeutics
"Thanks to Wolfpack Digital, we saw a 50% increase in app downloads, stronger customer retention, and a 2–5% rise in online sales."
Damian Burke
Online Development Manager & Digital Transformation Specialist, Corrib Oil ULC
"Their design talent is the best we have seen anywhere. They have a unique ability to take dense, complex industrial data and translate it into intuitive, consumer-grade interfaces that look incredible."
Roam AI
Keith Johnson, Vice President of Software Development

insights

Choosing an app development partner in Ireland

How to Choose an App Development Partner in Ireland

Adrian Florian

co-CEO

Reading time: 10 min

Sep 8, 2026

A practical guide to choosing an app development partner in Ireland, covering costs, grants, nearshore trade-offs, and taking an AI prototype to production.

How we audit fintech codebases in the AI era (and what still needs a human)

Victor Motogna

Head of Engineering

Reading time: 11 min

Aug 31, 2026

Victor Motogna, Head of Engineering at Wolfpack Digital, breaks down how to decompose a fintech code audit into small deterministic and LLM-driven steps, and why every model flag has to be reproduced before it counts as a finding.

A Wolfpack Digital team member wearing a pink #webyte t-shirt, with colleagues in the background

Wolfpack Digital on Company Culture

Corina

Marketing Specialist

Reading time: 2 min

Feb 9, 2021

What is company culture and why does it matter? Our take on building a company we love, where every team member enjoys the journey, and the seven values that guide us.

FAQ

What's the difference between an LLM and a RAG product?

An LLM (large language model) writes answers from what it was trained on, so on its own it can't speak to your business. A RAG product (retrieval-augmented generation) pulls the relevant information from a data source you control and gives it to the model first, so the answers come from your content, not the model's general training.

What's included in an LLM or RAG build?

The whole product around the model, not just the prompt: the RAG layer (embeddings, a vector database, and similarity search and retrieval), plus system prompts and guardrails, conversation memory, evaluation and tuning, and the admin tooling and analytics to run it.

Will it make things up?

Much less than a raw chatbot, because it answers from your data rather than the model's general training, and a system prompt keeps it on-topic. We also test answer quality and tune it before it ships.

Can you connect it to our own data and systems?

Yes. We connect to your existing database and data sources, map which records and fields matter, and build retrieval around them. On a recommendation product for an online retailer, that meant working from their live catalogue, pricing, and order history.

How does it stay current when our data changes?

It re-syncs from your sources on a schedule, daily or weekly depending on how fast your data moves, so it answers from live information rather than yesterday's data.

How do you check it's accurate before launch?

Before it ships, we run evaluation tests on the answers, tune the retrieval and the prompts, and use debug tooling to find the weak spots. Once it's live, an admin dashboard and analytics show you how it's performing.

Does my data stay private?

The product answers from your data, and we build the pipeline around your sources. We decide with you how the model is hosted, a managed API or self-hosted on your own infrastructure, which matters for enterprise RAG solutions where data control is one of the levers you have.

Which model will you use?

Whichever fits your product. We choose across Claude, GPT, Gemini, and open-source models based on speed and cost, and we can switch later.

How much does it cost to run?

It depends on traffic and on how the model is hosted, a managed API or self-hosted, and we work that out with real numbers before committing. We make the call deliberately, project by project.

Have you built this for a regulated field like healthcare?

Yes. SOARR is a medical documentation platform that turns clinician–patient conversations into structured notes, with anti-hallucination safeguards for a setting where the notes have to be right. Lizzy AI runs live, AI-graded first-round interviews. Both are live products.

Why work with Wolfpack on LLM & RAG?

We've put grounded and generative LLM products into production, including SOARR, Lizzy AI and Fyl. We're a senior team of 70+ across Cluj-Napoca and Dublin, building software since 2015, ISO 9001 and ISO 27001 certified and GDPR-compliant, and we build the whole product around the model, not just the prompt. LLM and RAG is one part of our wider AI development services, and you can see how we build on our AI-native development page.

Let's work together