Prompt tools & image prompting.
Custom prompt libraries, image-gen pipelines and internal AI tools. Repeatable workflows that scale to your team.
// 01 — what is prompt engineering
A prompt is code — it can be versioned, tested and improved.
A one-off ChatGPT question is an experiment. A production-grade prompt system is repeatable, measured and optimized.
Most AI use is still one-off questions — the user opens ChatGPT, types a prompt, copies the answer. That works for experimenting, but not for repeated team use.
Production-grade prompt engineering is something else. Prompts are templates that carry context, instructions and examples. They’re tested with an eval suite. Costs are measured. Version control in git. This moves AI from one-off experiments to systematic team use.
What we build in this service
Prompt libraries for your team: best practices for writing emails, code review, customer-service replies, content production. Image-gen pipelines: brand-adapted workflows (Higgsfield, ComfyUI, SDXL) for producing hundreds of images in the same style. Custom tools: your own chat UI on company data, your own image-gen panel, your own content assistant that knows the brand voice.
The deliverable: internal systems your team uses daily — without copy-paste prompting.
- 01.
Prompts are code. Version control, tests, cost tracking.
- 02.
Templates > one-off questions. Repeatability brings quality.
- 03.
Internal tools > public chat UIs when company context matters.
// 03 — benefits
Why have prompt tools built externally?
Six reasons why our clients have ordered custom prompt systems instead of a generic ChatGPT subscription.
- 01.
Brand-true templates
Prompts that know your company’s voice, products and terminology. Email replies don’t sound like ChatGPT — they sound like your team.
- 02.
Image gen in your company style
A custom Higgsfield or ComfyUI workflow that produces images in exactly your brand palette and style. No more generic Midjourney images.
- 03.
Cost optimization
We model the costs of different AI models (Opus/Sonnet/Haiku/GPT/Gemini) per use case. Lighter models for fast queries, expensive models only when needed.
- 04.
Eval suite included
Tests for every prompt to make sure a model update doesn’t degrade quality. The eval suite and CI are configured per project to run checks before a prompt change ships.
- 05.
Your own chat UI on company data
An internal chat tool that knows your company’s documentation, customer history or product data. When needed and agreed, the data stays in the EU or in your own environment.
- 06.
Scales to team use
Tools used daily by 10 or 100 people. Access control, an audit log, cost tracking — not just one person’s Claude subscription.
A prompt-tool project in four phases.
Discovery
What does the team do with AI daily? Where is support needed, where a template, where a custom tool.
Prompt design
Drafting the templates, designing the eval suite, model selection per use case, a cost estimate.
Build & testing
Iterating prompts against the eval suite, tuning image-gen workflows, coding the custom tools.
Rollout + training
Team training, documentation, ongoing support for the first weeks until usage becomes routine.
// 05 — example
Example: a brand image-gen pipeline
A typical project: the client has a brand palette and needs hundreds of social media images (product shots, campaign graphics, mood imagery). Designing them manually would take weeks. Generic Midjourney won’t hold the brand voice.
The solution: we build a ComfyUI-based workflow using a brand-palette LoRA model (trained on the client’s reference images) + a recipe (a prompt template + sampler settings + post-processing). The user types a short description, and the workflow produces 4–8 brand-adapted variants.
The deliverable: an internal tool a marketing team member uses weekly. 100 images per week — in days instead of the weeks it used to take.
// 06 — questions
Frequently asked questions.
Click a question to see the answer.
Which AI models do you use?
Text: Anthropic Claude, OpenAI GPT, Google Gemini, Mistral, open models (Llama). Image: Stable Diffusion (SDXL, Flux), Midjourney, DALL-E, Higgsfield. Embeddings: OpenAI, Cohere. We choose by use case and cost, not by brand.
Can we use on-premise models?
Yes — when needed and agreed, Llama 3, Mistral and Stable Diffusion can run on your own servers or in an EU cloud. This is especially useful with sensitive data: the data never leaves the company, and the cost scales predictably with volume. We agree on this per project as needed.
What does it cost?
The price depends on what’s being built — a prompt library, an image-gen pipeline or an internal chat tool on company data — and on the scope of the integrations and the eval suite. We quote per project after the strategy call. Internal tools can also include ongoing maintenance and updates on a monthly fee.
Can the team keep developing the prompts themselves?
Yes — all prompts are text in a git repo. Editing templates is trivial, and the eval suite tells you whether a change improved or degraded things. We can also run a training session on the fundamentals of prompt engineering for your team.
How long does a project take?
A prompt library is typically ready in 2–3 weeks. An image-gen pipeline 3–5 weeks. A custom chat UI 6–10 weeks. That includes discovery + build + testing + rollout. Team training continues afterwards as needed.
Want to scale AI use across your team?
Tell us how you use AI today — we’ll propose three concrete ways to move from one-off experiments to systematic use.
./book_prompt_callor email → mika@noxvisual.fi