Skip to main content
Trilagi
EN (English)PL (Polski)

Services

We build AI systems that actually work in your organization.

From intelligent knowledge bases to autonomous AI agents, Trilagi delivers production-grade GenAI implementations in the cloud, on-premise, or fully managed by us, with your data always under your control.

  • End-to-end delivery: architecture, implementation, deployment
  • Private and compliant: your data stays under your control
  • Built from production experience, not tutorials
Discuss your project See case studies

Service areas

What we build

  • AI Knowledge Systems

    Find anything. Instantly. Across all your internal knowledge.

    • Enterprise Knowledge Base
    • Multi-source RAG
    • Hybrid Search Implementation
    • Document Intelligence

  • AI Agents & Automation

    Replace repetitive workflows with AI agents that think, decide, and act.

    • Custom AI Agents
    • IT Operations Automation
    • Process Automation with LLMs
    • AI-Powered Incident Response

  • AI Communication & Assistants

    An AI assistant your team will actually use, right where they already work.

    • Microsoft Teams AI Assistant
    • Slack AI Integration
    • Enterprise Chatbot
    • Customer Service AI

  • Data Privacy & Secure AI

    Use AI on sensitive data without the compliance risk.

    • PII Anonymization Pipeline
    • GDPR-Compliant AI Deployment
    • Private LLM Infrastructure
    • AI Security Audit

  • GenAI Infrastructure & Deployment

    Own your AI stack. Run models on your terms.

    • Private LLM Deployment
    • LLM Fine-tuning
    • Cloud AI Architecture (AWS / GCP)
    • MLOps & AI Observability

Why Trilagi

How we work

  • We deliver, not just advise.

    Trilagi takes projects from architecture to production. We don't hand off a strategy deck. We build and deploy the system.

  • Cloud-native and infrastructure-agnostic.

    Whether your environment is AWS, GCP, on-premise, or hybrid, we work where your data lives. We design for managed services, private deployments, and everything in between. Or we run the whole system for you.

  • Compliance is part of the architecture, not an afterthought.

    Data privacy and GDPR requirements shape our architecture from day one.

  • Outcome-focused engagement.

    Every project starts with understanding your business problem. The technology follows the requirement, not the other way around.

Case studies

Selected work

IT Knowledge Assistant for an IT Operations Team

Client: Qlos (IT managed services company)

Challenge
ITOps engineers spent significant time searching across Confluence, Jira Service Management, and ClickUp for relevant runbooks, incident history, and configuration docs during active incidents. That slowed down resolution.
What we built
A conversational AI assistant integrated into the team's daily workflow, capable of querying all three data sources simultaneously. Engineers ask questions in natural language and get grounded, source-linked answers in seconds.
Result
Significant reduction in time-to-answer during incident triage; single interface replacing three separate search experiences.

Teams Service Desk Assistant for Customer Tickets

Client: Qlos

Challenge
ITOps engineers handled customer communication in Jira Service Management while coordinating their work in Microsoft Teams, constantly switching tools to check ticket queues, assess priorities and reply to customers. Understanding an incident also meant searching previous tickets for similar issues, related technical problems and recent changes to the customer's environment.
What we built
A Microsoft Teams assistant connected to Jira Service Management that lets engineers reply to customers and view their assigned tickets directly in chat. It categorizes incidents, assesses their urgency and criticality, and summarizes similar or technically related tickets, pointing out potential links to the customer's other issues and recent changes to their environment.
Result
Customer communication, ticket triage and relevant incident history brought together in Microsoft Teams. Engineers review related issues and recent changes alongside the current ticket, so they can investigate with better context and less manual searching.

Autonomous RCA Agent for Linux Infrastructure

Client: Qlos

Challenge
Root cause analysis for infrastructure incidents required manual data gathering from multiple monitoring systems, logs, and ticketing tools. Under incident pressure, that was slow and error-prone.
What we built
A multi-agent pipeline that automatically captures system state (logs, metrics, topology), correlates signals across sources, and generates a structured root cause hypothesis with supporting evidence.
Result
Automated first-pass diagnosis available as soon as an incident is detected; agent output used directly in post-mortem documentation.

Multi-layer Data Anonymization Before Public LLMs

Client: Qlos

Challenge
Diagnostic data collected from Qlos's and its clients' systems (tickets, logs, documentation) carries personal data (names, e-mail addresses, phone numbers, national ID numbers, postal addresses, usernames), company identifiers (client and provider names, Polish tax and company registry numbers) and infrastructure and operational details (hostnames, IP addresses and ranges, ticket numbers). None of it could be sent to a public LLM as-is.
What we built
An anonymization pipeline with multiple independent detection layers, running inside the environment before any request reaches an external model. Beyond standard personal data, it covers IT-specific identifiers that generic PII tools miss.
Result
Public LLMs can be used on real operational data without exposing personal data or the infrastructure details of Qlos's clients.

Private GPU Inference Infrastructure

Client: Internal project, Trilagi

Challenge
Developing and evaluating AI for IT operations meant running and fine-tuning open-source LLMs on sensitive data, with predictable cost and without depending on external model APIs.
What we built
On-premise GPU infrastructure built on production-grade NVIDIA data-center GPUs and AMD EPYC platforms, serving open-source LLMs for inference and supporting fine-tuning workloads.
Result
Full control over models, data and cost, plus a reference architecture for private LLM deployments that we use every day.

“We needed AI that could actually work with our tools and our data, not a generic chatbot. Trilagi understood the operational context from day one and delivered something our team uses every day.”

Head of IT Operations, Qlos

FAQ

Frequently asked questions

What does Trilagi do?
Trilagi is a deep-tech startup from Łódź, Poland, that designs, builds and deploys production-grade generative AI systems: enterprise knowledge bases (RAG), AI agents, AI assistants for Microsoft Teams and Slack, GDPR-compliant AI and private LLM infrastructure. Its deepest specialization is AI for IT operations, including automated root cause analysis of infrastructure incidents.
Where can the AI systems built by Trilagi run?
Wherever the client's data lives: on-premise in the client's own environment, in the cloud on AWS or GCP, or fully managed by Trilagi. Trilagi also runs its own GPU inference infrastructure for open-source LLMs.
Can we use LLMs on personal or confidential data and stay GDPR-compliant?
Yes. Trilagi builds anonymization pipelines that remove personal data and IT-specific identifiers, such as hostnames, IP addresses and ticket numbers, before any request reaches an external model. Where data must not leave the organization at all, Trilagi deploys private open-source LLMs on the client's infrastructure.
Which tools can Trilagi's knowledge systems and assistants connect to?
Typical sources are Confluence, Jira and Jira Service Management, ClickUp, SharePoint, e-mail and documents such as PDF and DOCX files. Answers combine semantic and keyword (hybrid) search and link to their sources, and assistants can work directly in Microsoft Teams or Slack.
Which language models does Trilagi work with?
Trilagi deploys open-source models such as Llama, Mistral and Qwen on private infrastructure and fine-tunes them on client data when a task requires it. Where the data policy allows, it also uses public LLMs, combined with anonymization when the data is sensitive.
What experience does Trilagi have in IT operations?
For Qlos, an IT managed services company, Trilagi has built an IT knowledge assistant, a Microsoft Teams service desk assistant connected to Jira Service Management, a multi-agent root cause analysis pipeline for Linux infrastructure and a multi-layer data anonymization pipeline. Trilagi's R&D on AI diagnostics for Linux systems is co-funded by the European Union.
What is RCA Assistant?
RCA Assistant is Trilagi's AI product for root cause analysis of Linux infrastructure incidents, built for system administrators and DevOps, SRE and IT operations teams. It gathers logs, monitoring metrics, topology and ticket content around an incident, correlates the signals across sources and proposes a structured root cause hypothesis with the evidence and reasoning behind it. It works with the data a team already has, without installing additional services.
How does a project with Trilagi start?
With a 30-minute call about the business problem. Trilagi then takes the project from architecture through implementation to production deployment, and can also run the system afterwards.

Contact

Let's talk about your project.

Let's set up a 30-minute call about what you need and how we can help.

Meetings are booked through Microsoft Bookings. See how we handle your data in our privacy policy.