A knowledge assistant
Example approach: retrieve relevant internal documents, answer with source references and provide a fallback when the material does not support an answer. Define a representative question set before choosing the model.
AI integration.
With purpose.
Useful intelligence belongs inside the tools your business runs on. We scope LLM integrations, knowledge assistants and automation around a real task, the right data and a clear definition of useful.
Discuss your projectYour knowledge & systems
LLM + toolsDefined permissions
Relevant context
Structured output
Evaluate. Review. Act.Example architectureRelevant context
Structured output
Start with the task. Then choose the model.
Finding an answer in a knowledge base is a different problem from preparing a system update or processing a document. We define the job, the data it needs and the boundaries it must respect before choosing an AI approach.
What we
can build.
- LLM-powered applications
- Connect a large language model to an existing product through an API. Define the context, expected response format and what the application should do when the answer is incomplete or unusable.
- RAG & knowledge assistants
- Retrieval-augmented generation brings relevant documents or product information into the model’s context before it answers. We scope retrieval, source references, access rules and how to check answer quality.
- Agents & tool calling
- Give a model access to a carefully scoped set of tools. An agentic workflow can prepare actions across APIs, with explicit permissions, human approval where needed and a record of what happened.
- Workflow automation
- Connect document processing, classification or information extraction to the next business step. Use rules where they are sufficient and a model where language or unstructured input makes it useful.
From an idea to
a testable workflow.
A reviewed system update
Example approach: extract structured information from an incoming document, validate required fields and prepare an API update for human review. The model proposes; the application enforces permissions.
Evaluation is part of the build.
We agree what a useful result looks like and test against representative inputs. Accuracy, response time, model cost, data access and failure handling all affect the design. These are approaches we can scope, not claims of measured results from the portfolio.
A little clarity.
Before we build.
Can AI work with our existing software?
An integration can connect through APIs or a defined data pipeline. We first review the available interfaces, access permissions and how the result should enter the existing workflow.
What data does a knowledge assistant need?
Relevant, accessible source material and clear rules about who may use it. We review document quality, update frequency and permissions, then define how retrieval and answers will be evaluated.
How do you handle incorrect answers?
We design for that possibility through validation, source references, test cases, fallback behaviour and human review where appropriate. The right combination depends on what the output will be used for.
Can we start with a pilot?
Yes. A bounded task, representative data and agreed evaluation criteria provide a useful starting point. The pilot should help decide whether an AI integration deserves a wider rollout.
Connected capabilities.
Describe the repetitive task, the information available and what a successful result would look like.
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