// Service · AI agents
AI agents & automation
AI that works on your data and your systems — not a generic chatbot. RAG pipelines, agents and process automation with engineering rigor.
§ 01
The problem
Your team loses hours on work that follows rules: sorting emails, extracting data from invoices, answering the same question ten times a day. Hiring more people for that does not scale.
You tried AI: the ChatGPT prototype worked for a week and then nobody could measure whether it answered well, what it cost or why it hallucinated. And generic chatbots talk, but they do not touch your systems.
The difference between a toy and an agent in production is engineering: evaluation, boundaries, monitoring and cost control.
§ 02
Who this is for
Operations with repetitive work
Invoices, support, reports, reconciliations: if it follows rules, an agent can do it.
Knowledge trapped in documents
Contracts, manuals, historical records. A RAG pipeline makes it queryable in natural language.
Teams whose AI pilot didn't scale
The ChatGPT prototype worked; now you need versioning, evaluation, monitoring and cost control.
Existing systems that need AI
We integrate agents into your ERP, CRM or internal software — without rewriting what already works.
§ 03
What's included
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Custom agents
Designed for your workflow, with scoped tools and permissions.
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RAG pipelines
Your documents and data, queryable with precision and citations.
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Process automation
Complete flows: input, decision, action, logging.
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Evaluation & monitoring
LangSmith, quality metrics and cost control from day one.
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Integration with your systems
APIs, webhooks, queues — the agent lives inside your operation.
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Vector databases chosen well
The right search infrastructure for your volume and budget.
§ 04
How we work
We read
Your process and your data. What is worth automating and what is not.
We order
We design the agent, its tools and its limits.
We deliver
To production with evaluation, monitoring and cost control.
§ 05
Frequently asked questions
What processes can be automated with AI?
The ones that follow rules and consume time: email triage and response, document data extraction, first-level support, reporting, reconciliations. On the initial call we identify which ones are worth it in your case.
How much does an AI agent cost to implement?
It depends on the process and the integrations. We prefer starting with a scoped case that proves value in weeks, not a giant platform.
Is my data safe?
We design with least-privilege permissions, data on your infrastructure where it applies, and traceability of every agent action.
What's the difference between this and a chatbot?
A chatbot talks. An agent executes: it queries your systems, makes decisions within defined limits and performs actions — with a log of every step.
What technologies do you work with?
LangChain, LangGraph and LangSmith; Anthropic, OpenAI and open-source models; vector databases like pgvector or Pinecone. We choose based on your case and budget.
Which process do you want to automate?
Tell us how it works today and we'll propose where to start.