AI applied where it changes an outcome, not everywhere it fits
Generative AI is useful when it removes a specific bottleneck: a slow manual process, an unstructured pile of documents, a repetitive decision. We design AI features around that kind of concrete problem, and we're direct when a simpler solution would serve the business better.
Commonly relevant for
- Financial Services
- Insurance
- Healthcare
- Professional Services
- SaaS & Startups
What’s included
Generative AI applications
Business applications powered by modern language models, designed around a defined use case rather than a general-purpose chatbot.
AI agents
Agents that carry out defined, bounded workflows, such as triaging requests, drafting structured output, or coordinating between systems.
RAG applications
Retrieval-augmented generation that connects a language model to your own controlled knowledge base, instead of relying on general model knowledge alone.
AI-driven automation
Automating repetitive knowledge work and operational steps that currently depend on manual review or manual data entry.
Document intelligence
Extracting structured information from contracts, forms, invoices, and other business documents.
LLM integration
Adding language model capability into an existing application's workflows, rather than building a separate, disconnected AI tool.
AI consulting
An honest assessment of where generative AI would help versus where it would add complexity without a clear return.
AI-assisted software engineering
Using AI tooling inside our own development process, for code review support, test generation, and engineering productivity.
Technology commonly used
See the full Technology page for what each of these enables and how they fit together.
Related services
Have a project worth talking through?
Tell us what you're building or what's slowing your current system down. We'll give you a direct read on scope and approach.