Thin product wrappers
A generic chat interface over a model is easy to copy and offers little beyond what users already get from general AI assistants.
AI & Automation · Generative AI
Generative AI development services for products people keep using
We help you turn a generative AI idea into a product with a clear job, an interface designed around imperfect outputs, sensible safety controls and running costs that make commercial sense.
What is Generative AI Applications?
Generative AI development services build new applications and product features in which models generate text, images, audio or code, such as drafting assistants, proposal generators, design tools or personalized learning content. Beyond calling a model, the work involves product design for outputs that are sometimes wrong, workflows for editing and approval, content safety, usage-based cost planning and the full web or mobile application around it. The goal is a product whose value holds up after the novelty wears off.
Many generative AI products launch as a text box in front of a model and lose users once the novelty fades, because they don't fit a real workflow, require too much prompting skill or produce output that takes longer to fix than to write. Others become unprofitable as usage grows. Successful products start from a specific job, constrain inputs so results are consistent, make editing easy and are priced with model costs in mind.
Why many generative AI products don't last
Inconsistent output quality
The same request can produce strong and weak results, which frustrates users unless the product guides and constrains inputs.
Unit economics
Model costs scale with every generation, so pricing, quotas and model choice must be planned before launch.
Safety, rights and misuse
Generated content can be inaccurate, inappropriate or raise copyright questions, and open tools attract misuse.
Our Generative AI Applications service
Product discovery
Defining the user, the job the product does, what a good output looks like and why users would choose it over a general assistant.
Rapid prototyping
A working prototype tested with real users and inputs, to validate output quality and usefulness before full development.
Generation pipelines
Structured prompts, templates, retrieval of user or company data, multi-step generation and output validation tuned for the use case.
Review-friendly interfaces
Editing, regeneration of specific sections, version comparison and approval steps that make imperfect output quick to fix.
Safety and content controls
Input and output moderation, usage limits, abuse detection and policies appropriate to your audience.
Full application build
Accounts, billing and quotas, admin tools, analytics and the web or mobile app, built for production rather than demonstration.
Included capabilities
- Guided inputs and templates
- Generation from user-supplied sources
- Section-level regeneration and editing
- Version history and comparison
- Image generation and editing workflows
- Input and output moderation
- Usage quotas and plan limits
- Per-generation cost tracking
- Export to documents and other tools
What Generative AI Applications changes for your business
Validated before full investment
Prototype testing shows whether output quality is good enough before the main build.
A product, not a wrapper
Workflow-specific design and data make the product more useful than a generic assistant.
Costs aligned with pricing
Unit economics are modeled early, so growth doesn't erode margins unexpectedly.
Users stay in control
Editing and approval features make generated content something users can trust and refine.
Where it makes the biggest difference
Proposal and report drafting
A tool that drafts proposals, reports or tender responses from a brief and a library of past documents, for staff to edit.
Marketing creative assistant
Generating copy variations and image concepts within brand templates, with review before anything is published.
Personalized learning content
Practice questions, explanations and summaries generated from approved course material and checked by educators.
Listing and catalogue tools
Helping sellers or agents write property, product or service listings from photos and a few structured details.
Seven steps from idea to launched product
Prototype testing and cost modeling come before the main build, so the product is proven and priced before you scale it.
Discover
Interview target users, define the core job and the quality bar, and identify why a general assistant isn't enough.
Prototype
Build a narrow prototype and test it with real inputs to see whether output is good enough to be useful.
Model the economics
Estimate cost per user and per generation, and design pricing, quotas and model choices around it.
Design the experience
Design inputs, editing and review flows so users can steer and correct output easily.
Build
Develop the generation pipeline, application, safety controls and admin tools in iterations.
Evaluate
Run quality test sets, safety tests and user testing before launch, and fix the weakest cases.
Launch and learn
Release to early users, monitor usage, edits and costs, and improve based on what people actually do.
What it connects to
Generative AI Applications
- Text and image model APIs
- Self-hosted open-weight models
- Payment and subscription billing
- Cloud file storage
- Document export formats
- Product analytics
- Content moderation services
Built with
Security, quality and scale
Moderation
Inputs and outputs checked against content policies suited to your audience and region.
Tenant data isolation
Each customer's uploaded sources and generated content are kept separate and access-controlled.
Abuse and cost protection
Rate limits, quotas and anomaly alerts protect against misuse that drives up model costs.
Provider data terms
Provider settings reviewed so user content is handled according to your privacy commitments.
What working with us looks like
Product thinking
We focus on the job the product does and whether users will return, not just on whether the model works.
End-to-end delivery
We build the web and mobile apps, billing and admin tools as well as the AI pipeline.
Economics from day one
We estimate cost per generation during prototyping, so pricing decisions are informed.
Honest prototype reviews
If prototype output isn't good enough to be useful, we say so before you fund the full build.
What it costs
Fixed-price packages with the scope written down, paid stage by stage. Your project goes live once it's fully paid.
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
Generative AI Applications: questions we hear most
How do we make our generative AI product different from general assistants?
Differentiation usually comes from a specific workflow, your own data or domain content, structured inputs and the editing and approval tools around generation. We define this during discovery, because a general text box over a model is easy for users to replace.
What does it cost to run a generative AI application?
Running cost depends on how many generations users make, how long the inputs and outputs are, and which models are used, plus hosting. We model cost per user during prototyping and design quotas and pricing around it.
Who owns the content generated by the application?
Output ownership is set by each model provider's terms and by applicable law, and most major providers' business terms assign output rights to the customer. Copyright protection for purely machine-generated content is unsettled in many jurisdictions, so we recommend legal advice for products where this matters.
How do you prevent harmful or inappropriate output?
We combine provider safety features with our own input and output moderation, limits on what users can request, and abuse monitoring. No combination eliminates every risk, so the policies are matched to your audience and reviewed after launch.
Can generative AI features be added to our existing product?
Yes. Adding features to an existing product is often a better starting point than a new product. When the work is mainly about embedding a language model into existing systems, our LLM integration service covers it.
Next step
Pressure-test your generative AI idea
Describe the product, who it's for and what a great output looks like. We'll help you work out what to prototype first and what the economics might look like.