LLM & Generative Apps
Generative apps built on your data and your rules.
Generative applications draft, summarize, and answer using large language models, grounded in your own content so the output is relevant and trustworthy. We build the apps that put generative AI to work inside your business, on your data, with the controls that make the results something your team can rely on.
What it is
A generative app uses a large language model to draft, summarize, and answer, but constrained to your own content and your own rules. It is an application built for a specific job inside your business, not a raw chatbot you hope produces something useful.
It is not a model left to its own devices with no context, and it is not a tool that invents facts unchecked. The app is grounded in your information through retrieval, so its answers come from your content, and its outputs are constrained to the format and rules you set, and handled in line with your compliance needs.
The outcome is faster drafting, instant answers over your own knowledge, and written work your team can trust. The repetitive writing and reading gets done in seconds, with people reviewing rather than starting from a blank page.
What we build
Generative apps we build.
Drafting tools
Summarization
Q&A over your content
Content generation
Analysis assistants
Internal copilots
Our process
How a generative apps engagement runs.
01
Define the use case
We pin down what the app generates and for whom.
02
Connect your data
We ground the app in your content using retrieval so outputs are relevant.
03
Set the rules
We constrain tone, format, and what the app is allowed to produce.
04
Validate outputs
We check quality against real inputs before launch.
05
Ship and refine
We deploy into your workflow and improve from real use.
How we build
The principles we hold to.
Ground generation in your own data
Outputs come from your content through retrieval, not the model alone.
Constrain outputs to your rules and format
We bound tone, structure, and scope so results stay usable.
Make quality measurable, not assumed
We check outputs against real inputs rather than trusting a demo.
Build it into the tools your team already uses
The app meets people where they work, not in a separate window.
Who it's for
Where this earns its place.
- Teams spending hours drafting or summarizing.
- Businesses with knowledge locked in long documents.
- Anyone producing repetitive written work at volume.
- Leaders who want generative AI applied to a real task, not a pilot.
Questions
Common questions
What is a generative AI app?
A generative AI app uses a large language model to draft, summarize, and answer, but built for a specific job and constrained to your content and rules. It is an application, not a raw chatbot you hope produces something useful. The model is the engine; the app is what makes it reliable and relevant to your business.
How do you stop it making things up?
We ground outputs in your own content through retrieval, so the app answers from your information rather than the model's memory. We also constrain what it can produce and validate quality against real inputs before launch. That combination reduces invented answers far more than relying on the model alone.
Is my data safe?
We design around your data handling requirements and discuss deployment options that fit your constraints, rather than applying a single fixed setup. The right approach depends on your sensitivity and compliance needs, which we work through with you up front. We do not make blanket security claims that ignore your context.
How is this different from AI agents?
Generative apps produce content and answers: drafts, summaries, and responses. Agents take actions and handle tasks across your systems. They overlap and often combine, with a generative app producing the content and an agent acting on it.