Generative AI development

Generative AI is software that creates new content instead of just sorting or scoring what already exists: you ask, and it writes text, produces code, generates images, speaks a reply, or turns a long recording into a short summary. We build those features into real products people use every day, along with everything around the model that makes them work: the interface, the guardrails, and the checks that prove the output is right. We have built software since 2015, and we've shipped generative AI products that are live in front of users.

Copilots and assistants

A copilot is an assistant that lives inside your product and helps a user get something done: drafting, answering, suggesting the next step. We build it into the tool your users already work in, and we ground it in your content so its answers are specific and useful.

Content generation

Features that draft, rewrite, summarise or restructure text on demand. The hard part is making the output reliable: tuning the prompts, setting the tone, and holding the model to the facts so it doesn't make things up. On Fyl we built a summary engine that turns recordings into fact-only summaries, tuned to stay strictly to what was said.

Image generation

Features that create or edit images from a text prompt or an existing asset, built into your product with the controls and review your case calls for.

Voice features

Speech in and speech out: live voice interactions, transcription, and turning recordings into clean summaries. Lizzy AI runs live voice interviews on OpenAI's Realtime models, then transcribes and scores each conversation, while SOARR transcribes and summarises patient encounters into structured medical notes. For transcription, we use models like Deepgram and tune them for accuracy, down to punctuation, speaker labeling, and custom vocabulary for names.

Chatbots grounded in your data

Chat that answers questions using your own content. We ground the model in your data so replies stay on-topic and accurate. We've done this on a client's own product catalogue, switched a live chatbot from one model to another when that made sense, and worked out the AI running costs up front so there are no surprises later. When a grounded-knowledge product is the whole point of the build, that becomes our LLM & RAG work.

what we build

What we build with generative AI

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beyond generative

More than generative AI

Generative features are one part of what we build. We also build AI agents and automation, LLM and RAG products grounded in your data, custom machine learning and predictive analytics, computer vision, and AI dropped into apps you already run. The full picture, and the rest of our generative AI solutions, is on the AI development services page.

Generative AI development is the product we deliver. It's not the same as AI-native development, which is how we build now. We keep the two apart on purpose, because they answer different questions.

how we work

How we deliver custom generative AI development

01

Map the data

Generative quality starts with what the model has to work with, so we map and model your data before anyone writes a prompt.

02

Cost the model up front

We benchmark models for fit, speed and price, and work out the running costs (API, hosting, processing) before we build, not after.

03

Build the feature around the model

The prompt is only a small part of the work, so we also build the reasoning, the grounding, the memory and the interface, and keep the engine swappable so you're not locked to one provider.

04

Evaluate before and after launch

Every build goes through an evaluation step that checks accuracy, bias, prompt attacks and cost, before it goes live and after each change.
wolf
how we work

How we deliver custom generative AI development

01

Map the data

Generative quality starts with what the model has to work with, so we map and model your data before anyone writes a prompt.

02

Cost the model up front

We benchmark models for fit, speed and price, and work out the running costs (API, hosting, processing) before we build, not after.

03

Build the feature around the model

The prompt is only a small part of the work, so we also build the reasoning, the grounding, the memory and the interface, and keep the engine swappable so you're not locked to one provider.

04

Evaluate before and after launch

Every build goes through an evaluation step that checks accuracy, bias, prompt attacks and cost, before it goes live and after each change.

projects

our work

We specialise in premium mobile app development services, web development services, and everything that revolves around them: Product Design, Product Strategy, AI integration, QA and maintenance. Over the years, we've not only built powerful digital products but also played a key role in boosting conversion rates, optimizing performance, adapting to growing user bases, and improving app store rankings and reviews. These efforts have driven greater user engagement and significantly increased revenue.

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.

insights

pack knowledge

Choosing an app development partner in Ireland

How to Choose an App Development Partner in Ireland

blog post publisher

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.

An isometric 3D illustration clustered in the center of a wide magenta-pink banner with subtle geometric patterns and the Wolfpack Digital logo at the top center. The illustration depicts a modern digital and physical payment system, featuring a central smartphone with a yellow screen displaying a digital wallet interface with a balance of "$215.8." Below the phone, a stack of gold coins, a payment card terminal with a large yellow 'PAY' button, and an inserted yellow credit card labeled "BANK" are arranged. To the right, a dark wallet sits behind the setup, and an unrolling physical paper receipt with transaction details is visible. Floating above are two security icons: a yellow speech bubble with a shield and checkmark, and a green speech bubble with a checkmark. The color palette is composed of pink, yellow, gold, and dark grey, with clean lines and a minimalist aesthetic.

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

blog post publisher

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

blog post publisher

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

frequently asked questions

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Building generative AI into a product: copilots and assistants, content generation, image generation, voice features, and chatbots grounded in your data. We build the whole feature around the model, not just the prompt.
Yes. We build generative features into products people already use, along with the interface, guardrails and checks around the model that make them work in production.
We're model-agnostic. We benchmark models for fit, speed and price, keep the engine swappable so you're not locked to one provider, and use what the feature needs, for example OpenAI's Realtime models for live voice and Deepgram for transcription.
We ground the model in your own data, and every build goes through an evaluation step that checks accuracy, bias, prompt attacks and cost, before launch and after each change. Sensitive data is handled under ISO 27001 practices and GDPR.
Generative AI development is the product we deliver. AI-native development is how we build. They answer different questions, so we keep them apart.