Generative AI built into your product, checked before it ships
Drafts, summaries and documents from your facts and your rules, with an editor in charge.
For a live example of approved, human-checked messaging, see how applyd.ai handles inbound leads.
What we build with generative models
Most projects start with one valuable feature. We agree the quality bar and how we will test it before the first prompt.
Drafting and summaries
First drafts and short summaries built from records already in your system, editable before anything is sent.
Document generation
Reports and proposals assembled from your templates. The model fills the sections that need judgement and fixed clauses stay fixed.
Image generation and editing
Visuals within style references and content filters, from resizing campaign assets to text-to-image drafts.
Reading images and audio
Features that read photos and scanned forms or transcribe calls, feeding the same structured data.
Review queues
Bulk generation that a person signs off, with approval screens and a history of what changed.
Brand voice and structured output
Tone guides, banned phrases and examples steer the style, and schemas make every response fit your data.
How a generative pipeline is built
Good generated content comes from a pipeline, not a prompt box: structured inputs, grounding in your facts, automatic checks, and an editor in charge of what ships.
Set up
Done once, then maintainedGeneration run
For each piece of contentA check fails: regenerate from the source facts.
Learn
ContinuouslyBrand and fact pack
Tone of voice, banned phrases, approved claims and product facts in a form the system can use.
- In practice
- A machine-readable style guide and claims register
- You get
- One source of truth for what can be said
Generate, check, then publish
Pick a brief and step through it. The draft is checked against the brand rules and the source facts, the flagged line is fixed from those facts, and an editor approves.
Brief
Source facts
- Weight 260 g
- 6 mm drop
- Recycled upper
Draft from the brief
Made for muddy weekend runs. It weighs only 230 g, with a 6 mm drop and a recycled upper that handles wet ground.
The model wrote this from the brief. It has not been checked yet.
How we keep generated content safe
A fluent draft can still be wrong. The checks sit between the model and your customer.
- Facts come from your recordsGroundedNames, figures and dates are inserted from your data, not recalled by the model.
- Banned claims and drift checksCheckedEach draft is checked against your rules and the source facts before an editor sees it.
- Editor approval where it countsPersonAnything that costs money or trust if wrong is approved by a person before it goes out.
- Provenance on published contentLoggedEach item keeps its sources, checks and approver, and is labelled as AI-assisted where needed.
Tools we commonly work with
We choose per medium, quality bar and cost per request. Naming a tool is not a partnership or an endorsement.
- Text models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Image and speech
- Imagen
- Stable Diffusion
- FLUX
- Whisper
- Structure
- JSON Schema
- Zod
- Pydantic
- Instructor
- Quality and review
- Promptfoo
- Braintrust
- LangSmith
- Review dashboards
- Runtime
- TypeScript
- Python
- Vercel AI SDK
- AWS Bedrock
- Azure OpenAI
Common questions about this work
Keep reading related pages
- LLM integration and RAGAnswers grounded in your own documents and systems.
- AI agent developmentAgents that use tools, with people approving risky actions.
- AI consultingPick the use case and the simplest design before building.
- Security overviewTenant isolation, access, audit and how agents are kept in bounds.
Tell us what you want built. We will scope it with you.
Start an AI project