Generative AI development services
Generative AI uses large language models and diffusion models to create new text, images, code and structured data from instructions. Visions turns these models into reliable product features and internal tools, with prompt engineering, evaluation and cost control built in from the start.
Overview
Generative AI explained
Generative AI creates new content, such as text, images, code or structured data, from an instruction. For businesses, its value lies less in open-ended chat and more in specific, repeatable jobs: drafting a first version of a document, summarising a long thread, extracting fields from an unstructured email or describing thousands of products in a consistent style.
We treat generative AI as a product feature, not an experiment. That means defining what good output looks like, grounding the model in your own data where accuracy matters, building review steps where content goes to customers, and tracking quality and cost as usage grows.
- 01Choose the right use case
- 02Design prompts and guardrails
- 03Ground in your data
At a glance
Generative AI
Best for
- Automated first drafts of proposals, reports and responses
- Meeting and call summaries with action items
- Product catalogue enrichment for e-commerce
- Document classification and data extraction
What you gain
- Faster first drafts
- Consistent tone and format
- Unstructured data made usable
Built with
- Python
- OpenAI API
- Anthropic Claude API
- Google Gemini API
- Llama
- Hugging Face
What our Generative AI service includes
GenAI strategy and use-case selection
We help you pick use cases with clear value and measurable outcomes before any build starts.
Content and copy generation
Drafting product descriptions, emails, reports and marketing copy in your brand voice, with review workflows.
Summarisation and extraction
Turning long documents, transcripts and emails into summaries and structured fields your systems can use.
AI features in your product
Writing assistants, smart search, auto-fill and recommendations embedded into web and mobile apps.
Image and media generation
Generating and editing visuals for catalogues, marketing and design workflows.
Prompt engineering and evaluation
Versioned prompts, test datasets and automated evaluation so quality stays consistent as models change.
Benefits of Generative AI
What your business gains when it is designed, built and run well.
Faster first drafts
Teams start from a structured draft instead of a blank page.
Consistent tone and format
Prompts and templates keep output aligned with your brand voice and required structure.
Unstructured data made usable
Emails, PDFs and transcripts become summaries and fields your systems can process.
Better product experiences
Users get assistants, smart search and auto-fill inside the apps they already use.
Our method
How we build Generative AI solutions
A practical, step-by-step approach that proves value early and reduces risk.
Step 1
Choose the right use case
We shortlist tasks where generative AI saves real time and where output quality can be measured.
Step 2
Design prompts and guardrails
We write versioned prompts, define output formats and add checks for length, tone, facts and sensitive content.
Step 3
Ground in your data
Where accuracy matters, we connect the model to your documents or databases so output is based on your information.
Step 4
Evaluate before launch
We test against a sample set and compare models for quality, speed and cost.
Step 5
Integrate and monitor
We add the feature to your app or workflow and track quality, usage and spend over time.
Common use cases
- Automated first drafts of proposals, reports and responses
- Meeting and call summaries with action items
- Product catalogue enrichment for e-commerce
- Document classification and data extraction
- In-app writing and editing assistants
Technologies we work with
- Python
- OpenAI API
- Anthropic Claude API
- Google Gemini API
- Llama
- Hugging Face
- LangChain
- Stable Diffusion
- FastAPI
- React
Where Generative AI helps
Examples of how teams in different industries apply it.
E-commerce and retail
Product descriptions, category copy and attribute extraction at catalogue scale.
Professional services
First drafts of proposals, reports and client updates, reviewed by your experts.
Media and marketing
Content variations, summaries and repurposing across channels.
Software and SaaS
In-app writing assistants, smart search and natural-language features.
Glossary
Generative AI key terms
Plain-English definitions of the terms used on this page, so everyone on your team is speaking the same language.
Read the FAQ- Generative AI
- AI models that produce new text, images, audio or code based on patterns learned from training data and an instruction.
- Large language model (LLM)
- A generative model trained on large amounts of text that can understand and write language.
- Prompt engineering
- Designing and testing the instructions given to a model to get reliable, well-formatted output.
- Grounding
- Supplying a model with trusted source material so its output is based on that material.
Our process
How we deliver
Design first, build carefully, test everything. Every AI, web and app project moves through the same six stages, adapted to your requirements.
- Mockups approved before any code is written
- Tested across screen sizes and browsers
- Direct communication throughout
- Support after launch
Step 01
Discover and plan
We review your goals, requirements and existing systems, agree the scope and assemble the right team.
- Requirements review
- Agreed scope
- Project team
Step 02
Design mockups
Our graphic designer creates mockups first. Development starts only after you approve the design.
- Mockups
- Your approval
- Figma or XD files welcome
Step 03
Develop
We build to closely match the approved design, responsive on desktop, tablet and mobile, with content that is easy to edit.
- Responsive build
- Editable content
- Progress updates
Step 04
Test
We test spacing, typography, interactions and functionality against the mockup, across screen sizes and browsers.
- Design accuracy
- Cross-browser
- Mobile and tablet
Step 05
Launch
We deploy to production, make final refinements and hand over everything you need.
- Production deployment
- Final refinements
- Handover
Step 06
Support
We stay available for maintenance, updates, performance improvements and the next release.
- Maintenance
- Updates
- Performance
FAQ
Generative AI: frequently asked questions
Common questions about our Generative AI service.
What is generative AI used for in business?
Common uses are drafting content, summarising documents, extracting data from unstructured text, answering questions over company knowledge and adding assistant features to software products.
Will our data be used to train public models?
We configure model providers and deployments so that your prompts and data are not used for training, and we can use private or self-hosted models when data must stay in your environment.
How do you keep generated output accurate?
We ground outputs in your data, constrain formats, add automated checks and evaluation datasets, and keep a human review step for content that is published or sent to customers.
Can generative AI be added to an existing application?
Yes. Most projects add AI features to existing web or mobile apps through an API layer, without rebuilding the application.
Which generative AI model is best?
There is no single best model. The right choice depends on the task, required accuracy, speed, cost and where your data may be processed. We test several models on your actual task before recommending one.
How do you control generative AI costs?
We choose an appropriately sized model for each task, cache repeated requests, limit output length and monitor usage per feature with budgets and alerts.
Related services
AI Knowledge Assistants (RAG)
Retrieval-augmented assistants that answer questions from your documents with cited sources.
Custom LLM Solutions & Fine-Tuning
Fine-tuned and self-hosted language models adapted to your domain, tone and data rules.
Agentic AI
Autonomous AI agents that plan, use tools and complete multi-step business tasks with human oversight.