YOUR KNOWLEDGE. A MORE USEFUL INTERFACE.
Put language models to work in products your team can use. SkolarAI builds generative AI applications, knowledge assistants, and copilots connected to your content and business workflows.
Discuss your generative AI projectA useful generative AI product needs more than a prompt. We design the information flow, user experience, and evaluation around the questions people need answered and the work they need to complete.
Help users find relevant policies, product documentation, or internal guidance. A retrieval-augmented generation (RAG) workflow retrieves permitted source passages to support an answer and show where the information came from.
Create first drafts of reports, responses, or structured summaries using relevant context. Users can inspect the source material, edit the output, and approve it before it is published or shared.
Extract information, summarize long documents, or turn a question into a guided search. We define expected output formats and checks for missing information so downstream steps can handle uncertain results.
The scope depends on your users, content, and integration requirements. A typical build can include:
We begin with the user task and review the content available to support it. A focused prototype compares retrieval and model choices against representative questions, including questions the system should decline to answer.
We then integrate the application and review response usefulness, source attribution, latency, and operating cost. The rollout includes feedback paths and a process for updating documents. Model or prompt changes should be re-evaluated against the same reference questions.
We agree which content can be processed, how access is enforced, and which deployment options fit your requirements. RAG can make answers easier to verify, but it does not eliminate incorrect output. Source links, uncertainty handling, and human review remain part of the experience where the task requires them.
Retrieval-augmented generation supplies a language model with relevant material from a knowledge source when a question is asked. It can help when answers depend on your documents or changing information, provided retrieval and permissions are designed carefully.
Often the first step is to evaluate an existing model with good retrieval and prompting. Fine-tuning or a different deployment approach can be considered when the task, data, or operating requirements justify it.
Data readiness, integrations, evaluation requirements, and deployment constraints shape the scope. Share a representative workflow and any existing prototype so we can agree on milestones and a project-specific estimate.
We review the evidence against the agreed criteria before planning a production build. Our AI engineering services cover application integration, deployment, monitoring requirements, and handover. Ongoing maintenance is scoped separately.
Tell us who the application is for, the content it should work with, and what a useful answer looks like.