Generative AI Development
LLM features built into your existing product and workflows: summarisation, document extraction, classification and drafting, with evaluation, guardrails and cost control designed in from the first version.
Generative AI as a feature in your product
Most useful generative AI work is narrower than a chatbot or an autonomous agent. It is a single feature inside a product or workflow that reads text and returns something structured: a summary of a long case file, fields extracted from an invoice or contract, a ticket classified and routed, a reply drafted for a person to check, or a copilot panel inside the application your team already uses. As a generative AI development company with a security practice, we build those features with the parts that decide whether they hold up in production. We help you choose between hosted model APIs and open-weight models you run yourself, based on quality, cost, latency and where your data is allowed to go. We design prompts and output schemas so results arrive in a shape other systems can consume, build an evaluation set from your own examples so quality is measured, and add guardrails for malformed output, sensitive data and prompt injection. Deployment includes logging, monitoring and cost tracking. Where a feature needs retrieval over company knowledge, we point you to our RAG applications work; multi-step autonomous tasks belong with AI agents, customer-facing chat with bot development, strategy questions with AI consultancy, and independent testing of the finished feature with AI security testing.
What's included
- Summarisation, extraction, classification and drafting features inside your product
- Model selection across hosted APIs and open-weight models, judged on your data
- Prompt and output-schema design so results feed other systems reliably
- Evaluation sets built from your own examples to measure quality
- Guardrails, data-privacy controls, cost and latency monitoring in production
Who adds LLM features to existing systems
- Product teams adding summarisation, drafting or a copilot panel to an application customers already use
- Operations teams processing large volumes of documents, emails or forms where staff retype or reclassify information by hand
- Engineering teams with a working LLM prototype that now need evaluation, monitoring and cost control before release
- Data-sensitive organisations that want generative AI features but need clear answers on where data is sent and stored
Work that a language model can take off your team
- Staff read long documents to pull out the same handful of fields, and the extracted data must land in another system in a fixed format
- Incoming requests need classifying and routing, and the volume or variety has outgrown keyword rules
- Your team writes similar replies, summaries or reports repeatedly and wants a first draft generated for review
- A prototype built on one model API works in demos, but nobody can say how good it is, what it costs per request or how it fails
- You need to decide between a hosted model and an open-weight model you host, because of data residency, cost or latency
What ships with each feature
Every engagement ends with documents and fixes your team can act on, rather than a presentation.
- Working generative AI feature integrated into your application or workflow, deployed to your environment
- Model selection notes comparing the candidates tested on your examples, with the quality, cost and latency tradeoffs
- Prompt templates and output schemas under version control, with validation for malformed or incomplete responses
- Evaluation set and automated test run so quality is checked before and after every prompt or model change
- Guardrails, logging and cost monitoring, plus a written record of what data is sent to which model and where
How we take a feature from prototype to production
The steps are the same whether the work is an assessment or a build, so you always know what happens next.
- 1
Discover
We start by understanding your systems, goals, and constraints, including scope, risk tolerance, and what success looks like, so the work is aimed at your problem rather than a generic template.
- 2
Assess or build
For security work, we test and analyse against recognised standards. For development, we build in small, reviewable increments. Either way, you see progress early and can change direction.
- 3
Report or ship
You get clear, prioritised deliverables, either a report your engineers can act on or working software shipped to your environment, with the context to understand what was done and why.
- 4
Support
We stay available after delivery: retesting fixes, iterating on the product, and answering the questions that come up once real users and real traffic arrive.
Generative AI Development: common questions
What drives the cost of a generative AI development project?
Where does our data go, and can it stay private?
Which models do you use?
How do you measure whether the output is good enough?
How long does a first generative AI feature take to build?
Specialist work this often leads to
Teams that come to Safe Tech AI for generative AI development frequently need these too.
RAG (Retrieval-Augmented Generation) Applications
AI systems grounded in your own documents and data, so answers are accurate, current, and traceable to a source. That is the difference between a demo and something your team can rely on.
Learn moreAI Agents
Multi-step, tool-using AI systems that complete tasks rather than only answering questions, designed with the guardrails, permissions, and human oversight that make autonomy safe to deploy.
Learn moreAI & LLM Security Testing
Security testing for LLM applications, RAG systems and AI agents. We test for prompt injection, data leakage, unsafe tool use and broken access control, the ways AI features get abused in practice, and hand you reproducible findings with fixes.
Learn more
Put a measured LLM feature into your product.
Bring us a task your team repeats on text or documents, and we'll scope a generative AI feature with evaluation, guardrails and a clear view of where your data goes.