AI Agents & LLM Solutions

AI agents that actually do the work

The AI noise is loud, but most of it is chatbots with personality. We build something else — AI agents that plug into your real systems, follow your business rules, and take actions that measurably reduce manual work.

An AI agent isn't a chat widget. It's software that understands context, follows business rules, connects to your systems and handles tasks that would otherwise require a person. Think of it as a team member that works 24/7, doesn't need coffee, and actually reads every email.

What AI agents can do for your business

• Customer support automation — agents that understand your product, answer accurately from your knowledge base, escalate complex cases to humans, and learn from interactions
• Document analysis — contracts, invoices, applications and reports — extracting structured data, flagging anomalies and generating summaries
• Internal knowledge assistants — agents your team can ask about company processes, project status, technical docs or past decisions — answering from your real data, not generic internet content
• Workflow orchestration — agents that watch incoming requests, classify them, route to the right team or system and follow up automatically
• Data enrichment and research — agents that collect, cross-reference and aggregate information from multiple sources for sales, compliance or operations teams

How we build them

We use large language models (OpenAI GPT-4/5, Anthropic Claude, open-source models) as the reasoning engine — but the real work is in the integration layer, wiring the agent to your databases, APIs, CRM, email, file storage and anywhere else it needs to reach to be useful.

Every agent we build ships with guardrails: clear boundaries on what it can and can't do, human-in-the-loop escalation for sensitive decisions, audit logging for compliance, and testing against edge cases so behavior stays predictable.

Not a black box

We don't deploy agents and walk away. Every agent comes with monitoring dashboards, performance metrics and clear documentation on its capabilities and limits. You'll know exactly what it does, how well, and when it needs a human.

Technologies

OpenAI API, Anthropic Claude, LangChain, vector databases (Pinecone, pgvector, Weaviate), RAG pipelines, custom tool integrations, Node.js, Python — stitched together based on what your specific use case actually needs. For sensitive data we recommend storing it in Latvia or an EU region — we help keep data within EU borders and aligned with GDPR.

Frequently Asked Questions

How much does AI agent development cost?

Proof of concept (2–3 weeks, one use case, limited integrations) — €3,000–8,000. Production-ready agent (6–10 weeks, integrations, monitoring, docs) — €12,000–30,000. Complex multi-agent system (2–4 months) — from €30,000. Maintenance — from €500/month. We always give a concrete quote before the project.

How much does LLM usage cost?

Pure LLM usage costs (on OpenAI or Anthropic) are low per request, but they add up. A typical business with ~1,000 interactions/day — €150–1,000/month depending on complexity and chosen model. We optimize costs via caching, model selection by task complexity and shorter prompts where possible.

Does our data stay private?

Yes. OpenAI and Anthropic's enterprise APIs don't retain data for training (default). Azure OpenAI and AWS Bedrock also support private deployments. For highly sensitive use cases we can deploy open-source models (Llama, Mistral) in your own infrastructure — data never leaves your environment.

Can agents replace our team?

Our approach is to augment, not replace. Agents take on the repetitive, tedious work so people can focus on complex decisions, strategy and relationships. The best systems we've built are ones where humans and agents work together — not in place of each other.

Do agents hallucinate or give wrong answers?

Without the right architecture — yes. With RAG (Retrieval-Augmented Generation), structured grounding, strict prompts, guardrails and validation layers, we reduce hallucination risk substantially. For high-stakes use we always add a human confirmation step.