Writes, summarises, answers
Drafts emails, reports and replies, condenses long documents into a short brief, and answers questions in your own tone. Example: turning a 40-page contract into a one-page summary of the terms that matter.
Guide
AI is not one thing. Here's what the different kinds actually do.
"AI" gets used as a single word for a dozen different tools, one that writes, one that draws, one that codes, one that talks. They're not interchangeable, and picking the wrong one for the job wastes time and money. This guide walks through what each kind actually does, in plain business terms, so you know what to ask for.
What AI can create, or do
Six kinds of work, each handled by a different kind of AI.
"AI" is a category, not a product. Underneath it are separate tools built for separate jobs. Here's what each one actually produces, and a business example of it at work.
Creates and edits visuals
Generates product shots, ad creative and social graphics from a description, or edits existing photos, backgrounds, staging, touch-ups. Example: a listing photo virtually staged in minutes instead of a paid shoot.
Builds and fixes software
Writes and edits application code, finds bugs, and speeds up building internal tools and integrations. Example: a custom dashboard or an integration between two systems built in days, not months.
Voiceovers, jingles, sound
Generates natural-sounding voiceovers in multiple languages, background music and sound effects for ads and video. Example: a training video narrated and re-recorded in three languages without booking a studio.
Clips and animation
Produces short video clips, animated explainers and motion graphics from text or still images. Example: a product demo clip generated for a launch campaign without a film crew.
Multi-step work that acts, not just answers
Carries out a sequence of steps toward a goal, checking a customer's order, updating three systems, sending a confirmation, rather than answering one question at a time. Example: a lead that messages at midnight gets qualified, logged in the CRM and booked onto a calendar before morning.
The main model families
Different models are strong at different things.
Behind every AI tool sits a "model", the underlying engine that does the actual work. Different families of models are known for being strong in different areas, and there is no single model that is best at everything.
General-purpose assistants like Claude (Anthropic), Gemini (Google) and GPT (OpenAI) are the ones most people have tried, they read, write, reason and answer questions across almost any topic, and increasingly handle images and documents too. Beyond them sit narrower families built for one job each.
We're vendor-neutral. We don't sell one platform, we match the model to the task, and we're honest when a smaller or open-source model does the job just as well for less.
01
General-purpose assistants
Claude, Gemini and GPT are the household names, strong all-rounders for writing, summarising, answering questions and reasoning through a problem. Good default for most business text work.
02
Image models
Purpose-built for generating and editing visuals, product shots, ad creative, staged photos. Quality and style vary by model, so we test a few against your brand before picking one.
03
Code-focused models
Tuned specifically for writing, reviewing and fixing software, often faster and more reliable at that one job than a general-purpose model.
04
Speech & music models
Built for voice cloning, natural-sounding narration, and generating music or sound effects, useful for training content, ads and multilingual customer communication.
05
Open-source models
Models you can run on your own infrastructure instead of a vendor's servers. Useful when data privacy, cost at scale, or full control matter more than having the newest features first.
How to choose
You don't need to know the models. You need to know the job.
Picking a tool before you've defined the problem is how businesses end up with an expensive project that never should have started. A short checklist keeps the decision honest.
01
Start from the job, not the tool
Name the outcome you want, faster replies, cleaner listings, fewer support tickets, before you name a single model or vendor. The job dictates the tool, never the other way round.
02
Mix models where it makes sense
A single workflow can use a text model to draft, an image model to illustrate, and an agent to carry out the follow-up steps. You're not locked into one for everything.
03
Keep your data in mind
Some jobs are fine with any capable model. Others, anything touching sensitive customer or financial data, deserve a closer look at where the model runs and who can see the data.
04
Prove it small
Test the shortlisted model on your real work, not a demo, before committing. A working proof-of-concept on your own data tells you more than any spec sheet.
Common questions
What business owners ask us about "which AI" to use.
Do we need to pick one AI for our whole business?
No. Most businesses end up using a small mix, one model for writing, another for images, another for a specific automation, rather than a single tool for everything.
Is one model simply "the best"?
No, it depends on the job. A model that writes excellent marketing copy isn't necessarily the strongest choice for reviewing code or generating images, and the right fit can change with your budget and data needs too.
Can we switch models later if a better fit comes along?
Yes. We build so the model behind a workflow can be swapped without rebuilding everything around it. No lock-in to one vendor or one model.
Keep exploring
Related reading
The AI use-case library
Concrete ways AI takes work off your plate, organised by what you are trying to fix.
Browse use casesThe AI glossary
Plain-English definitions for the terms you'll hear while evaluating AI, no jargon.
Browse the glossaryQuestions to ask first
The questions that reveal where AI will actually pay off, before you spend a thing.
Read the guide
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