Klarvo.

AI assistant for the defense industry

Technical documentation in defense programs is huge and version control is critical. Engineers spend serious time just finding information.

▶ The solution

I build an AI assistant that works on your procedures, reports and past projects, answers with sources, and is mathematically optimized for speed and low cost.

▶ Use case

When an engineer asks for a subsystem's test requirement, the assistant finds the relevant section of the right document and shows the source. Which data is processed where is decided at the start, together with the organization's security function.

▶ Data you need

Technical documents, requirements and test documents the organization approves for this use.

The industry's data landscape

In the defense industry, programs are long, documentation-heavy and the supply chain has many tiers. Data accumulates in project management systems, technical documents, and test and quality records. Confidentiality requirements decide from the start which data can be processed where.

An in-house AI assistant connects large language models (LLMs) to your company's own documents. An employee asks a question; the assistant finds the relevant documents, writes the answer based on them, and shows where it came from.

The math behind it

Monthly cost ≈ N × (t_in × p_in + t_out × p_out)

  • N: questions per month
  • t_in, t_out: tokens sent to and returned by the model per question
  • p_in, p_out: the model's price per token
Retrieval-augmented generation (RAG) sends the model only the passages relevant to the question, not every document [1]. That shrinks t_in, which cuts cost and speeds up answers. I optimize assistants for speed and cost on exactly this calculation.

How I work

  1. Week 1

    Use case and sources.

    Who is the assistant for, and for which questions? Which documents are in scope, and which are current? How success will be measured is written down here.

  2. Weeks 2–3

    First working version.

    Documents are ingested, the assistant is set up and answers with sources. It is tested with real users' real questions.

  3. Weeks 4–5

    Accuracy and trust.

    Answers are measured regularly against a question set. Wrong or incomplete answers are fixed, and the assistant is made to say when it doesn't know.

  4. Final week

    Handover.

    How to add new documents, how to monitor the system and how to guide users is documented and handed to your team.

Full service details: In-house AI assistants →

▶ Questions and answers

AI assistant for the defense industry — what data do you need?

Technical documents, requirements and test documents the organization approves for this use.

When is this the wrong choice?

You have few documents and everyone knows where they are. A well-organized folder structure and search may be enough. An assistant would be an unnecessary layer. The knowledge is in people's heads, not in documents. No assistant can find knowledge that was never written down. It has to be written down first.

How does this work start?

With a short intro call. I listen to what you need and tell you plainly whether your data is enough for the job and what the first step should be. I reply to every message within 24 hours.

▶ Concepts

Linked to Wikipedia and Wikidata.

▶ Sources

  1. [1]Wikipedia, Retrieval-augmented generation, 2026-10. en.wikipedia.org/wiki/Retrieval-augmented_generation
  2. [2]Wikipedia, Large language model, 2026-10. en.wikipedia.org/wiki/Large_language_model

▶ Intro call

Let's talk about your situation.

In a 30-minute call I listen to what you need and tell you plainly how I can help.

Book an intro call

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