AI Solutions & Intelligent Automation

Most AI initiatives stall somewhere between a strategy presentation and a demo that never reached production. We work differently: our engineers work alongside your teams, learn how your organization actually operates, and build the solution themselves. This way of working is known as Forward Deployed Engineering.

An engagement runs in four steps:

1. AI Assessment (2–4 weeks). Interviews with your teams, a map of processes and systems, and a review of data readiness. Deliverable: a short list of use cases, ranked by expected return, cost and risk.

2. Pilot (4–8 weeks). We build one use case end to end, with real users and real data, and measure the outcome.

3. Implementation & integration. We extend to more processes and connect AI to the ERP, CRM and custom systems you already use.

4. Ongoing support & training. Monitoring, optimization, new use cases and staff training, so your teams trust and actually use the new tools.

Training also covers the AI literacy obligation set by the EU AI Act (Regulation (EU) 2024/1689).

We design AI-powered assistants tailored to your business needs, whether customer-facing or internal.

From support agents on your website to internal copilots that help your team find information and complete tasks faster, our solutions are built with accuracy, control and escalation mechanisms in mind.

We also build agents that don't need a chat window at all: they run on a schedule or when something happens in the business (a ticket breaching its SLA, a tender matching your profile, a subscription coming up for renewal) and only report when there is something to say.

Capabilities include:

• Customer support automation with human handoff
• Knowledge base integration (RAG systems)
• Multilingual interaction, including full Greek support
• Task execution via APIs and business logic
• Scheduled and event-triggered agents
• Actions the AI proposes and that run only after a person approves them

An AI assistant is only as useful as the data it can reach, and only as safe as its permissions.

We use the Model Context Protocol (MCP), the open standard now supported by Claude, ChatGPT, Microsoft Copilot and most AI tools, to expose what your systems can do as tools a model can call. The MCP server sits on top of your applications' existing role-based access control (RBAC). Each person signs in with their work account, for example Microsoft Entra ID, and the AI sees exactly what they would see in the application, not a single record more.

What's included:

• MCP servers over ERP, CRM, ticketing, timesheets and custom applications, with no data migration
• Authentication through OAuth 2.0 / OpenID Connect, with roles from your existing user directory
• Access from the AI tool your team already uses, or from a company chat inside your own applications
• Write actions only as proposals a person approves, with step-up authentication (2FA) for high-risk actions
• A full audit trail: who asked what, which tool ran, and what it returned

We run the same model on Noetik's own operations platform, where our team asks in plain language about projects, charges and invoicing, and gets answers only about what they are allowed to see.

Read how we built it →

We integrate AI into your existing systems to automate repetitive tasks and streamline operations.

By connecting AI models with your business logic, CRMs, ERPs and custom applications, we enable intelligent workflows that reduce manual effort and improve consistency.

Typical use cases:

• Document processing and data extraction from invoices, contracts and receipts, including Greek documents
• Email and communication automation
• Reports and answers that combine data from several systems
• Content generation pipelines
• Internal process optimization

We design AI systems with data privacy and control as core principles, not afterthoughts.

Depending on your requirements, we implement architectures that keep your data secure and isolated, in line with GDPR and the requirements of the EU AI Act.

Our capabilities include:

• Processing in EU regions through Amazon Bedrock, Google Cloud Vertex AI or Microsoft Azure
• Local knowledge processing (RAG) using privately hosted vector databases
• On-premise or private cloud deployments for sensitive environments
• Use of open-source models when data must not leave your infrastructure
• Minimizing the data that reaches the model, and masking sensitive fields
• Strict data access and retention policies
• Integration with your existing security and compliance frameworks

This approach is particularly valuable for organizations handling confidential, regulated or proprietary information.

We develop fully customized AI-driven applications, designed to solve specific business challenges.

Whether it’s a SaaS platform, internal tool, or client-facing product, we combine our software engineering expertise with modern AI capabilities to deliver scalable and maintainable solutions.

AI systems rely on external computational resources, which makes cost management and reliability critical.

We design solutions with:

• Visible cost per question and daily spending caps per user
• Model selection strategies (cost vs performance)
• Failover and fallback mechanisms
• A record of every question and action, kept in your own infrastructure
• Security and data privacy considerations

This keeps your AI investment predictable, sustainable and aligned with your business goals.

In a few weeks you'll know where AI can save time and cost in your organization, what it takes to build, and where to start.

Book a meeting →