Knowledge Search & Q&A
Assistants that answer questions using your own documents and data, with links back to sources.
- Retrieval-augmented generation (RAG)
- Document ingestion and indexing
- Source citations in answers
Intelligent Systems
Put Large Language Models to Practical Use.
We help organizations apply generative AI to real work, such as finding information, drafting content and summarizing documents, with attention to accuracy, privacy and cost.
Where It Helps
Common Situations We Help With.
Generative AI tends to help most where people spend a lot of time reading, searching or writing similar material.
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Staff spend time searching across documents and systems for answers
We build assistants that answer questions from your own content and show the sources they used.
Knowledge Search & Q&A -
Teams repeatedly draft similar documents or responses
We set up drafting tools with templates and review steps, so people stay in control of the final text.
Drafting & Summarization -
Long reports, contracts or tickets need consistent summaries
We connect language models to the systems holding these documents and structure the output your teams need.
LLM Integration -
You want to trial generative AI without exposing sensitive data
We define data-handling rules, test sets and logging before a trial reaches real users.
Quality & Safety Controls
Capabilities
What We Can Help You Deliver.
Each capability is designed with review steps and data-handling rules suited to how sensitive your information is.
Drafting & Summarization
Tools that help teams draft, rewrite and summarize content while keeping people in control of the result.
- Templates and prompt design
- Summaries of long documents
- Human review steps
LLM Integration
Connecting language models to your applications and workflows through secure, well-structured interfaces.
- Model and provider evaluation
- Prompt and output handling
- Usage and cost monitoring
Quality & Safety Controls
Practical measures that reduce wrong or unsuitable output and protect sensitive information.
- Evaluation test sets
- Content and data-handling rules
- Logging for review
How We Work
How an Engagement Comes Together.
Generative AI work starts small and is checked against real examples before it reaches a wider audience.
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Phase 01
Select a Use Case
We choose a contained task and gather representative documents and questions.
OutcomeA defined use case with sample content
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Phase 02
Prepare Content
We organize, clean and index the sources the model will draw on.
OutcomeA searchable knowledge base with access rules
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Phase 03
Build & Test
We build the tool and test its answers against an agreed evaluation set.
OutcomeA working tool with documented test results
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Phase 04
Pilot & Review
We release to a pilot group, gather feedback and adjust before wider use.
OutcomeFeedback-informed improvements and usage insights
Related Services
Often Combined With.
How do you reduce incorrect or made-up answers?
We ground answers in your own approved sources, show where information came from, test against an agreed set of real questions, and add human review where accuracy matters most.
Will our documents be used to train public models?
Data handling is agreed with you before any build. We help you choose services and configurations that match your privacy and data-retention requirements.
Can generative AI work with the tools our teams already use?
Often, yes. We can connect assistants and drafting tools to existing applications, document stores and workflows, subject to what those systems allow.
Intelligent Systems
Considering Generative AI for Your Team?
Tell us which documents, questions or writing tasks take the most time, and we can help identify a safe place to start.