Knowledge retrieval & assistants
Build application experiences that retrieve relevant business content and use it to inform a model response.
- Search and question-answering over approved information
- Source references and review of retrieved context
Services
AI capabilities.
Built into useful workflows.
We integrate OpenAI APIs and Azure OpenAI into business applications, and use retrieval-augmented generation (RAG) to connect language-model responses with relevant business information. The starting point is a defined task, an appropriate data source, and a result your team can evaluate.
What we bring
From an AI idea to an application people can use.
Put AI into the existing workflow with clear inputs, access rules, review points, and application behavior.
Workflow assistance
Add language-model capabilities to appropriate business tasks such as drafting, summarization, and information review.
- Connect assistance to an existing application journey
- Keep consequential actions behind user review
API & application integration
Connect AI services to .NET, Node.js, web, and mobile applications with defined data flows and permissions.
- Control which information is available to a request
- Handle errors, latency, and service limits in the application
AI-assisted engineering
Use AI tools to support requirements exploration, implementation, tests, and documentation while engineers retain responsibility for delivery.
- Architecture decisions and code review by engineers
- Validation against business requirements before release
From context to delivery
A connected way forward.
- 01
Define the task and data
- 02
Build a focused prototype
- 03
Evaluate with real scenarios
- 04
Integrate and improve
Build new. Evolve what exists.
Evaluate the result in your business context.
AI features need testing beyond a successful API response. We define representative questions and tasks, review the quality of retrieved information, and assess the usefulness of the output with the people who will use it.
- Review access boundaries and the information sent to a model
- Test unsupported questions and incomplete source material
- Plan feedback, human review, and ongoing evaluation
Let’s discuss your application
Your business context.
Our next conversation.
AI & LLM integrationTalk to our team.