AI Solutions
Generative AI Grounded in Your Own Knowledge
We build generative AI applications that help teams find information, draft content, and process documents using approved business knowledge. We connect these applications to your existing tools and design access controls, evaluation, and deployment around your requirements.
What We Deliver
Useful generative AI applications need relevant sources, appropriate access controls, and a place in everyday work. We design these together and test the results against real tasks.
We connect the application to approved knowledge sources so it can retrieve relevant information and cite supporting documents. Users can inspect those sources, while evaluation checks whether answers are supported and useful.
We design authentication, document-level access, network controls, and data handling around your security requirements. Before deployment, we review where information is processed, what is retained, and which users can access each source.
We embed generative AI in the CRM, collaboration, and line-of-business tools people already use, rather than asking them to adopt a separate application.
We test answers against representative tasks, checking source support, usefulness, response time, and cost. Pilot feedback helps determine whether the application is ready for wider use.
Use Cases
Help employees find answers across approved policies, procedures, and technical documentation, with links to supporting sources.
Give support teams relevant knowledge and draft responses to review before sending.
Extract, summarize, and organize information from business documents, with review steps for uncertain or consequential outputs.
Produce first drafts and concise summaries from approved material, with people responsible for reviewing the final output.
Illustrative example. Access rules, fallback behavior, and how long logs are kept are agreed with your team, including how sensitive data is handled.
Our Approach
We identify candidate use cases such as document processing, enterprise search, or service assistance, then agree on success measures before any build starts.
We prepare approved content for retrieval, with ingestion, indexing, and search where needed, and carry existing permissions through so people only see what they are allowed to.
We compare foundation models on quality, cost, latency, and data residency, then design prompts, guardrails, and fallbacks for the chosen one.
We integrate the application into your workflow, validate access controls and output quality, and roll out in phases. We agree on ownership, monitoring, and a process for updating knowledge sources after launch.
We review the selected provider's terms and configuration with your team before deployment, including model-training use, retention, and processing locations. We select services and settings that meet your requirements and document any limitations.
We retrieve information from approved sources, test responses against representative questions, and check whether citations support the answer. We also design fallback behavior for missing or conflicting information and add human review where errors could have significant consequences. These measures reduce risk; they do not eliminate incorrect answers.
We agree on how source documents are updated, who owns them, and when changes should become available to the application. Retrieval and evaluation checks help identify outdated content, broken ingestion, and changes in answer quality.
It depends on the task, cost, latency, and where your data must stay. We test candidate models on your real questions and recommend the one that performs best for your constraints.
Keep Exploring
Next Step
Tell us where your data, applications, or workflows are slowing your team down. We'll discuss your priorities and identify a practical starting point.
