Generative AI
Generative AI development services
We build products on large language models that write, summarise, answer and reason, grounded in your data and wrapped in the guardrails a real business needs.
- GPT
- Claude
- Gemini
- Llama
- Mistral
- Embeddings
Overview
Generative AI, explained.
Generative AI uses large language models (LLMs) and related models to create text, code, images and structured data from instructions. Used well, it removes hours of drafting, searching and copying from everyday work. Used carelessly, it produces confident mistakes, leaks data or costs far more than expected.
Our generative AI development focuses on the parts that decide whether an LLM product succeeds: grounding answers in your own content with retrieval-augmented generation (RAG), structured outputs that other systems can rely on, evaluation sets that catch regressions, and controls on cost, privacy and tone.
We work across GPT, Claude, Gemini and open models such as Llama and Mistral, and we design so you can swap models later without rebuilding the product.
What we deliver
Generative AI services we deliver.
LLM-powered applications
Purpose-built web and mobile apps where a language model does the core job: drafting, analysing, classifying or answering.
RAG knowledge assistants
Assistants that search your documents, policies and databases and answer with citations, respecting user permissions.
AI copilots inside your tools
Side-panel helpers that draft emails, reports, proposals or code using the context of the screen the user is on.
Document & data generation
Contracts, product descriptions, summaries and structured JSON produced from templates, rules and source data.
Fine-tuning & prompt engineering
Prompt systems, few-shot examples and, where justified, fine-tuned models for consistent tone and format.
Evaluation & guardrails
Test sets, automatic scoring, content filters and fallbacks that keep quality measurable and outputs safe.
Use cases
Where it creates real value.
Professional services
First drafts of proposals and reports assembled from past work, with every claim traceable to its source document.
Education
Question banks, lesson summaries and feedback on written answers, generated to a syllabus and reviewed by teachers.
E-commerce
Product titles and descriptions at catalogue scale, consistent with brand tone and checked against product data.
Internal operations
A company-wide assistant that answers HR, IT and policy questions from the handbook instead of a ticket queue.
How we deliver
A clear path from idea to launch.
Define the job and the bar
We collect real inputs and write down what an excellent output looks like, so quality can be scored from day one.
Ground it in your data
We prepare your content for retrieval, choose chunking and search strategies, and decide what the model may and may not see.
Build, evaluate, iterate
Prompts, tools and model choices are tuned against the evaluation set until the product reliably meets the bar.
Ship with controls
Rate limits, cost budgets, logging, feedback buttons and human review where needed, then continuous improvement.
What you receive
- Prompt and retrieval design documented in your repository
- Evaluation set with automatic scoring
- Production app or API with guardrails and logging
- Usage and cost dashboard
- Runbook for updating content and models
Technology
Tools we build with.
- GPT
- Claude
- Gemini
- Llama
- Mistral
- Embeddings
- Vector search
- Structured outputs
- LangChain-style orchestration
- Python
- Node.js
Built with the same care as our own products
We build and run our own software, including the FinHance LMS and two Android apps, so we design generative AI features with real users, real data and long-term maintenance in mind, not just a demo.
See our productsGenerative AI FAQ
Common questions.
Answers to what clients ask most about our generative ai services.
Generative AI development is building software that uses models such as large language models to create text, code, images or structured data, and connecting those models to a company’s data, tools and users with the right quality checks and safeguards.
We ground answers in your own content using retrieval, ask the model to cite sources, constrain outputs to structured formats where possible, and measure hallucination rates on an evaluation set before launch.
We use enterprise API terms or self-hosted models that do not train on your data, send models only what a task needs, and can deploy inside your own cloud account.
Yes. We keep prompts, retrieval and evaluation separate from the model provider, so moving between GPT, Claude, Gemini or an open model is a configuration change plus a re-run of the evaluation set.
Let’s talk
Let’s make your idea bloom.
Tell us what you’re building. We’ll get back to you with clear next steps.