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AI in everyday operations

We build AI into ticketing, monitoring and reporting, on self-hosted models that run next to those systems. It summarises tickets, logs and alerts, extracts fields from documents and searches runbooks, with a person confirming each result.

Describe what you need

What you get

  • Self-hosted models running next to the systems they serve
  • AI steps in the agreed workflows, such as ticket summaries with a suggested category and team, short summaries of logs and alerts, and first drafts of recurring reports
  • A retrieval index of the documents, runbooks and past tickets in scope
  • Validation checks and guardrails on where the AI runs and what it can access
  • A log of what each AI step was asked and what it returned
  • A written map of what AI, fixed rules and people each handle, including where AI should not be used

How it works

  1. 01

    Find the use cases

    We look at repetitive, data-heavy or hard-to-interpret work and map what AI, fixed rules and people should each handle.

  2. 02

    Prepare the data

    The data the AI will use is cleaned and structured, and documents, runbooks and past tickets are indexed for retrieval.

  3. 03

    Build it into workflows

    Self-hosted models are connected to the ticketing system, monitoring or reporting, with prompts and logic designed for real cases.

  4. 04

    Validate and set guardrails

    Outputs are checked before use, and guardrails limit where the AI runs and what it can access.

  5. 05

    Monitor and adjust

    Inputs, outputs and usage are reviewed, and prompts and logic are adjusted to how the AI is used in practice.

Problems it solves

  • ProblemAn AI licence was bought before anyone defined a use case, and nobody can say what it is for.

    SolutionWe start with a list of tasks: those where AI fits, such as repetitive or data-heavy work, and those better left to fixed rules or people.

  • ProblemNobody can see what the AI was asked or what it returned, so its answers cannot be checked or explained.

    SolutionEach AI step is logged with what it was asked and what it returned. A written map sets what AI handles, what fixed rules handle and what people decide.

  • ProblemA general-purpose AI answers from its training data, so it knows nothing about internal runbooks, past tickets or documents.

    SolutionSelf-hosted models run next to the systems they serve, and a retrieval pipeline grounds their answers in the company's documents, runbooks and tickets.

  • ProblemStaff copy fields from invoices, emails and PDFs into other systems by hand, which is repetitive and slow.

    SolutionAI extracts the fields and maps them to the schema the receiving system already uses, and a person confirms them before they are saved.

What it covers

  • For exampleLlama, Mistral
  • For exampleOllama
  • For examplepgvector, Qdrant
  • Ticket summaries and routing suggestions
  • Log and alert summaries
  • Field extraction from documents
  • First drafts of recurring reports
  • Guardrails on what the AI can access
  • Logging of AI inputs and outputs
Typical scope

Typical engagements run 2–6 weeks, depending on how well defined the use case is, how clean the data is, and whether the workflow already exists.

Next step

Send a short description of what you need and the systems involved. We reply with proposed next steps.

Describe what you need