Generative AI · LLM · Agents · MLOps

AI Solutions

We turn AI from a demo into a working part of your business. We find the use cases that pay off, build secure LLM applications on your own data and run them in production with monitoring, evaluation and cost control.

Isometric illustration of an AI chip connected to a neural network, a chat assistant, a vector database, documents, automation and security

Generative AI & LLM apps

Assistants, copilots and chatbots grounded in your documents and systems with retrieval-augmented generation (RAG).

AI agents & automation

Agentic workflows that call your APIs, process documents and hand work over to people when it matters.

MLOps & LLMOps

Pipelines, evaluation, guardrails and monitoring so models stay accurate, safe and affordable after go-live.

What we can build for you

  • Knowledge assistant (RAG) – an internal chat that answers questions from your policies, contracts, wikis and tickets, with links to the source documents. Built on Azure OpenAI or Amazon Bedrock, Azure AI Search or a vector database, and access rights inherited from your existing permissions.
  • Intelligent document processing – extraction of data from invoices, orders, contracts and forms with Azure AI Document Intelligence and LLMs, validated and pushed straight to your ERP or CRM.
  • AI agents – multi-step workflows that use tools (function calling, Model Context Protocol – MCP) to look up data, open tickets, prepare reports or triage e-mails, with a human-in-the-loop approval step.
  • Customer service copilot – suggested answers, ticket summaries, classification and sentiment analysis for your support team.
  • Developer & DevOps copilots – code review assistants, incident summaries from logs and alerts, and natural-language queries over your cloud infrastructure.
  • Predictive models – forecasting, anomaly detection and classification with classic machine learning where an LLM is not the right tool.

Our offer

  1. AI discovery workshop (1–2 days) – we map business processes, rate use cases by value and feasibility, and check data readiness, security and compliance.
  2. Proof of concept (4–6 weeks) – a working prototype on your real data, with an evaluation set and measured quality, latency and cost per request.
  3. Production rollout – an AI landing zone on Azure or AWS: private networking, identity, secrets, prompt and model versioning, CI/CD and observability.
  4. Run & improve – LLMOps: monitoring of quality and token usage, regression evaluation, model upgrades and continuous tuning.

Technologies we use

  • Platforms – Azure AI Foundry, Azure OpenAI Service, Amazon Bedrock, Amazon SageMaker, Google Vertex AI, Databricks Mosaic AI.
  • Models – GPT, Claude, Llama, Mistral and open-weight models, chosen per task by quality, cost and data residency.
  • Building blocks – embeddings, vector search, semantic chunking, prompt engineering, fine-tuning, function calling, agent frameworks (Semantic Kernel, LangChain/LangGraph).
  • Operations – MLflow, Azure Machine Learning, evaluation pipelines, content safety filters, Infrastructure as Code with Terraform.

Responsible and secure AI

Your data stays in your tenant and is not used to train public models. We design with guardrails against prompt injection and data leakage, log and trace every request, and help you prepare for the EU AI Act and GDPR with clear documentation, risk assessment and human oversight.

Why it pays off

  • Faster processes – hours of manual reading, copying and searching reduced to minutes.
  • Knowledge at hand – new employees get answers without searching through dozens of systems.
  • Measured results – every solution ships with metrics, so you know what AI actually delivers.

Have an idea for AI in your company?

Book a short AI discovery call – we'll tell you honestly what is feasible, what it costs and where to start.

Contact uscontact@soltegro.pl