AI and automation
LLM and AI agent development
LLM and AI agent development is the engineering work of turning a large language model into a dependable product: connecting it to your data through retrieval (RAG), giving it memory and the tools it may use, setting limits on what it can do, and testing its answers. Loxvo designs, builds and maintains these applications and agents for small, medium and larger businesses.
What it covers
A language model knows nothing about your company and forgets each conversation when it ends. Useful AI products fix both. Retrieval-augmented generation (RAG) lets the model answer from your documents, prices, tickets and policies, with sources. Memory lets an agent carry what it learned about a customer or a project from one session to the next. Tool access lets it act: look up an order, create a ticket, update the CRM, schedule a call.
We build these on models from OpenAI, Anthropic and Google, or on open models you host yourself when data must stay in your environment. Every build includes an evaluation set, so quality is measured before launch and after each change, along with logging, cost tracking and limits on what the agent is allowed to do. Our QA engineers test AI features the way they test any other software, plus the checks that are specific to models.
What you get
- LLM applications and internal assistants
- Retrieval (RAG) over your documents and data
- Agents with long-term memory
- Tool use and integrations (APIs, MCP servers)
- Model selection, including self-hosted open models
- Evaluation, monitoring and cost control
Tools and platforms
- OpenAI API
- Claude API
- Gemini API
- LangChain
- LlamaIndex
- MCP
- pgvector
- Pinecone
- Python
- Node.js
How it works
Four steps, each one visible.
- (01)
Define
The job the AI must do, the data it needs, what it may never do, and how success is measured.
- (02)
Prototype
A working version on your real data, with an evaluation set built from real questions.
- (03)
Harden
Guardrails, permissions, logging, cost limits and testing of the failure cases.
- (04)
Operate
Deployment, monitoring and regular evaluation as models and your data change.
Questions
LLM and AI agent development: common questions.
What is the difference between an LLM application and an AI agent?
An LLM application uses a language model to answer or generate, for example a support assistant that replies from your documentation. An AI agent can also take actions with tools, such as looking up records, updating systems or sending messages, and decides which steps to take to finish a task.
What is RAG and why does it matter?
Retrieval-augmented generation (RAG) means the system first finds the relevant passages in your own documents and data, then asks the model to answer from them. It keeps answers current and specific to your business and lets the answer cite its sources.
What does memory mean for an AI agent?
Memory is stored information that an agent can recall later: facts about a customer, decisions made in earlier sessions, preferences and task history. Without it, every conversation starts from zero.
Can the AI run on our own infrastructure?
Yes. Where data must not leave your environment we can use open models hosted in your cloud account or on your servers, with the trade-offs in cost and quality explained up front.
How do you know the AI's answers are good enough?
We build an evaluation set from real questions and expected answers, score the system against it before launch and after every change, and monitor live conversations for failures.
Related
Services that go with it.
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Learn moreTell us what you are building.
A short call is enough to see whether we are the right team. You will speak to someone who can answer technical questions.