AI integration in your software.
Krafter integrates AI components into your software, existing or in development: LLMs, RAG on your data, agents, automation. The model that fits your use case (GPT, Claude, Gemini, Mistral), with the guardrails that come with it. AI in production, not exploratory research.
In production. Not in a demo.
The problem
You don’t want a chatbot. You want smarter software.
Everyone wants AI in their product. Few know what it really involves: which model to pick, what each request costs, what happens when the model gets it wrong, and where the data you feed it ends up.
The demo is easy. Production, much less so. A prototype that impresses in a meeting can turn into a money pit: unpredictable answers, latency going through the roof, runaway API bills, sensitive information sent to a vendor without anyone ever asking the question.
At Krafter, an AI integration starts with one question: where does AI bring measurable value in your software? Summarize, extract, classify, suggest, generate. Everything else (model, architecture, guardrails) follows from the answer.
What we integrate
From a wired-in LLM to an agent that acts: four levels.
LLM integration
#1GPT, Claude, Gemini, Mistral connected to your software: summarization, generation, text analysis, contextual suggestions. The model chosen on measured criteria: cost, quality, latency, confidentiality.
Let’s talkRAG on your data
#2An LLM doesn’t know your business. RAG indexes your document base to produce sourced answers, grounded in your documents rather than generic knowledge.
Let’s talkAI agents and function calling
#3Agents that do more than answer: search the CRM, create a task, send an email, update a record. With human validation where it matters.
Let’s talkIntelligent automation
#4Document classification, extraction from emails and PDFs, record enrichment, automatic summaries, predictive scoring. Repetitive tasks leave your teams’ daily routine.
Let’s talkWhy Krafter
AI already runs in our products, not just in our slides.
Smashr, our sales CRM, has been AI-augmented since day one: call preparation, record enrichment, automatic summaries. It runs in production, with real salespeople relying on it every day.
So we know what a request costs, what happens when the model answers off target, how to cap an API bill, and how to evaluate answer quality over time. Not in theory: on our own invoices and our own users.
And because we build software first and foremost, AI fits cleanly into your architecture: not a gadget bolted onto the product, but a tested, monitored, maintainable component, held to the same standard as the rest of the code.
Conviction
“Generative AI is powerful, but it’s also unpredictable. A good AI system in production is 20% prompt engineering and 80% guardrails.”
Fabien Maquin
Co-founder & CEO, Krafter
How we work
From use case to AI component in production.
A successful AI integration doesn’t start with picking a model. It starts with identifying where AI brings measurable value, and proving it works on your data.
01 / 04
Use case discovery
1 weekWhere does AI bring measurable value in your software? We identify the candidate tasks and the success criteria.
Volume processed, time saved, acceptable error rate, confidentiality constraints: the scope is set before any talk of models.
Model selection and POC
1 to 2 weeksCandidate models are tested on your real data. The choice is made on measured results.
Cost, quality, latency and confidentiality compared on your specific case. If no model holds up, we tell you at this stage, not six months later.
Integration and guardrails
The AI component fits into your software with output validation, fallbacks and cost limits.
Answers checked before display, defined behavior when the model fails, API spending caps, logging of every request.
Measurement and iterations
ongoingAnswer quality, usage costs, adoption: the system is monitored and adjusted after going live.
Prompts evolve, models change versions, prices move. The component is reassessed regularly to stay relevant and cost-effective.
Technical side
Which AI model should you choose for your software?
There is no best model in absolute terms: there is the model that fits your use case. GPT, Claude, Gemini and Mistral each have a different profile of cost, quality, latency and confidentiality, and the ranking changes with the task: summarizing a document, extracting structured data or powering an agent don’t call on the same capabilities. Krafter tests the candidate models on your real data during discovery, and when confidentiality requires it, deploys open source models on your own infrastructure.
- Models
- GPT, Claude, Gemini, Mistral, and self-hosted open source models.
- Approaches
- LLM integration, RAG, agents with function calling, automation.
- Selection
- Tests on your real data: cost, quality, latency, confidentiality.
- Guardrails
- Output validation, fallbacks, cost caps, logging.
- Confidentiality
- No-training clauses, or a model hosted on your infrastructure.
- Proof
- Smashr, our AI-augmented CRM, in production every day.
FAQ
Summarization, extraction, RAG, agents: we start by identifying what AI should bring to your software, not by picking a model.
Talk about my AI projectBefore integrating AI into your software.
It depends on the use case. GPT, Claude, Gemini, Mistral: each model has its own profile of cost, quality, latency and confidentiality. We test the candidates on your real data during discovery, and the choice is made on measured results, not on the vendor’s reputation.
With commercial APIs, your data goes through the vendor’s servers, covered by no-training clauses and data residency options. When that is not enough (sensitive data, regulatory constraints), we deploy open source models on your own infrastructure: nothing leaves your premises.
A simple integration (summarization, extraction, generation on an existing flow) sits between €3,000 and €8,000 excl. VAT. A complete RAG system on your document base: between €10,000 and €25,000 excl. VAT. An AI agent with process automation: between €15,000 and €40,000 excl. VAT. Add the API usage cost, estimated during discovery.
RAG (Retrieval-Augmented Generation) lets the model answer from your documents rather than from its generic knowledge. Your document base is indexed, the relevant passages are retrieved for each question, and the model generates a sourced answer. It is the reference technique for putting an LLM to work on your business.
On repetitive, structured tasks (classification, extraction, data entry), yes, and that is a good thing. On anything that requires judgment and nuance, AI is an accelerator, not a replacement: it prepares the work, the human decides.
No. Pre-trained models work without your own data: no training required. For a RAG system, a document base of a few dozen documents is enough to get useful answers.
Let’s build a product people → actually use
30 minutes on a call with a founder. Answer within 24 to 48 hours. Pitch deck optional.