On-brand copy & content
Product descriptions, emails, ads and articles drafted in your brand voice from your own product data.
- Follows your style guide and banned words
- Whole catalogues in one run
- Reviewed before anything is published
Generative AI development
Generative AI that creates on-brand content for your business.
We build generative AI into your products and workflows — copy, images, voice and reports drafted in seconds, grounded in your own data and approved by your team before anything goes out.
- Grounded in your own data
- Human approval built in
- Private by default
Models & platforms we build with
OpenAI GPTAnthropic ClaudeGoogle GeminiMeta LlamaMistralStable DiffusionFLUXGoogle ImagenElevenLabsWhisperAzure OpenAIAWS BedrockGoogle Vertex AILangChainLlamaIndex
What we build
Generative AI, put to real work
Six ways businesses use generative AI today — each built around your own content, your rules and your review process.
Images & visuals
Lifestyle scenes, banners and ad variations generated around your real product photos, so the product itself stays accurate.
- New scenes and backgrounds
- Every size and format per channel
- Brand colours and layout rules
Voice & audio
Natural voiceovers for videos, courses and phone menus — plus transcripts and summaries of calls and meetings.
- Text-to-speech in many languages
- Transcripts with speaker labels
- Call and meeting summaries
Summaries & reports
Long documents, email threads and meeting notes turned into short summaries and first-draft reports.
- Each point linked to its source
- Fixed templates for recurring reports
- Decisions and action items pulled out
Personalisation at scale
One approved message adapted for every customer segment, language and channel — without rewriting it by hand.
- Variants per segment and language
- Stays inside approved claims
- Ready for your email or CRM tools
Ask your data
Plain-English questions turned into database queries, charts and written explanations — no SQL needed.
- Natural language to SQL
- Read-only, permission-aware access
- Shows the query behind each answer
How it works
Inside a generative AI feature we build
A prompt and a model are only the start. These five layers are what make the output accurate, on-brand and safe to publish.
-
Request
A person writes a prompt, or your app triggers one — a new product, a ticket, a report due.
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Context
Your brand rules, product data and approved facts are added, so output is grounded in what is true.
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Model
Each task goes to the model that fits on quality, speed and cost — and can be switched later.
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Guardrails
Automatic checks for tone, banned claims, personal data and unsafe content.
-
Human review
Your team approves, edits or rejects — and their edits improve the next draft.
Approved output goes straight into your website, app, CMS or CRM
Choosing the approach
Prompting, RAG or fine-tuning?
Three ways to shape what a model produces. Picking the right one early saves both time and money.
Start here
01
Prompt engineering
Lowest effort
- Best when
- The model already knows the subject and you need control over tone, format and length.
- You'll need
- A clear brief and a handful of examples of good output.
- When things change
- Edit the prompt — live in minutes.
For your own facts
02
RAG (retrieval)
Moderate effort
- Best when
- Output must use your own, up-to-date facts — catalogue, policies or documentation.
- You'll need
- Access to the documents and data the content should draw on.
- When things change
- Add or update a document — no retraining.
For special cases
03
Fine-tuning
Highest effort
- Best when
- A very specific style or format at high volume, or a smaller, cheaper model that must match a larger one.
- You'll need
- Hundreds to thousands of high-quality examples.
- When things change
- Retrain the model when the style or facts change.
Most projects combine prompting and retrieval. We recommend fine-tuning only when tests on your own examples show it clearly beats them.
FAQ
Questions about generative AI
Straight answers on ownership, privacy, accuracy and running costs.
Still have a question?
Tell us what you'd like to create and our team will reply within 24 hours.
Contact UsUnder the business terms of the major providers, including OpenAI, Anthropic and Google, the output belongs to you. Copyright rules for purely AI-generated material differ by country, so for content that matters we keep a person editing and approving it, and we suggest a quick check with your legal team.
No. The business APIs from OpenAI, Anthropic and Google do not train on your inputs or outputs by default, and where data must stay in-house we can run open-source models in your own cloud.
Every request is grounded in your approved sources, your style guide and banned claims are built into the system, each draft runs through automatic checks, and anything public-facing goes to a person for approval.
Image models are good at scenes, backgrounds and variations, but they can change small product details when they draw a product from scratch. So we start from your real product photos and generate around them, which keeps the product itself accurate.
Discovery takes 1–2 weeks and a working prototype on your own content another 2–4, so you see real output within the first month or so. The production build then usually takes 6–12 weeks, depending on how many systems it connects to.
You pay the model provider per use — per token (roughly per word) of text, or per image or minute of audio. We estimate monthly costs before building, and keep them down with caching, smaller models for simple tasks and larger ones only where quality needs it.
Free initial consultation
Talk to a Tech Consultant.
Let's discuss your project requirements and get expert advice from our team.
Talk to our foundersDanny & Ashish · Technical Leads
Reply within 24 hours
Talk to a Tech Consultant.
Let's discuss your project requirements. Get expert advice from our team — free initial consultation.
Enquire about our services
We'll respond to you within 24 hours!
1
First Contact
Our team will contact you in no time.
2
Requirement Analysis
Discuss your project requirements with experts.
3
Create a proposal
Based on your requirements, we create a proposal.
Danny Lalwani
Founder and Technical Lead at CodiFly
Ashish Kharloya
Founder and Technical Lead at CodiFly