Generative AI Development
Generative AI products that write, summarise, search and create — grounded in your own data with retrieval, fine-tuning and measurable evals.
What is Generative AI Development?
Generative AI development is the practice of building applications on large language and diffusion models that produce new text, images, code or structured output rather than only classifying existing data. ADM Technology builds these systems on Claude, OpenAI and open-weight models with retrieval-augmented generation over your private content, LoRA fine-tuning where prompting is insufficient, and an automated eval suite that holds faithfulness above 90%, typically moving from prototype to production in eight weeks.
Overview
LLM apps, RAG systems & fine-tuned models
We build generative AI applications end to end: retrieval-augmented generation over your private content, fine-tuned models where prompting is not enough, and text, image and code generation surfaces your users actually reach for. Every build ships with a golden dataset and an eval suite, so quality is a number you can watch rather than a hope.
90%+
Eval pass rate
8 wks
Prototype to production
-70%
Content production time
Exactly what we do, step by step
Every Generative AI Development engagement follows a clear, phased path — so you know precisely what work happens, in what order, and what comes out of each stage.
Use-Case & Model Selection
We define exactly what the system must generate, benchmark candidate models against your own sample inputs, and decide between prompting, retrieval and fine-tuning using measured quality, latency and token cost.
Data Preparation & Embedding
We clean, chunk and embed your documents, product data and media into a vector store, tuning chunk size, overlap and metadata filters so retrieval surfaces the right context on real queries.
RAG Pipeline Build
We build the retrieval-augmented generation layer with hybrid keyword and semantic search, a re-ranking pass and source citations, keeping every generated answer traceable to your content rather than model memory.
Fine-Tuning & Prompt Engineering
Where prompting alone plateaus we fine-tune with LoRA adapters on curated examples, then version prompts like source code so tone, output format and domain accuracy stay stable across releases.
Evals & Guardrails
We build a golden dataset and an automated eval suite scoring faithfulness, relevance and safety on every change, adding schema validation and human review before generated output reaches customers.
Deploy, Stream & Optimise
We ship with streamed responses, prompt caching, model routing and per-tenant token budgets, then watch quality and spend in production so the product stays fast and economical at real volume.
What's included in every engagement
The building blocks we bring to your product — proven, production-grade and tailored to your goals.
Exactly what you walk away with
Tangible, hand-off ready assets — no black boxes, full ownership.
Related services
Generative AI Development — common questions
Straight answers to what teams ask us most before starting. Need specifics? Talk to a senior engineer.
A focused generative AI build sits well below a full product platform, and cost is driven by three things: how much of your data needs preparing, whether fine-tuning is required, and how many surfaces the feature ships to. We scope a narrow first use case, quote it fixed-price with milestones, and forecast ongoing token spend separately so you can model both build and running cost before committing.
Let's build something exceptional together.
Tell us where you're headed. We'll show you how to get there faster — with production-grade software and a senior team.