Ready to ship production AI?
Book a free 30-minute call with a senior AI engineer — leave with a realistic scope, a model recommendation and a first estimate.
AI development with senior-engineer ownership
UP2DATE builds production AI — LLM features, RAG pipelines, AI agents and workflow automation — with senior engineers owning quality, safety and evals end to end. Romania nearshore, EU and US clients.
15+Years building software
ISO 27001Information security
EU/USEU & US clients
Production AI
evals, guardrails & monitoring included
What we build
Production AI capabilities, end to end
LLM product features & copilots
We design and ship LLM-powered features inside your product — chat interfaces, inline suggestions, document Q&A and domain-specific copilots — with latency and cost budgets set upfront.
RAG pipelines & knowledge bases
Retrieval-augmented generation systems that let your models answer from private data accurately — document ingestion, chunking strategy, embedding selection and retrieval ranking all owned by us.
AI agents & workflow automation
Multi-step autonomous agents that trigger actions, call external APIs and route between tools — built with observable traces so your team can inspect every decision the agent makes.
Evals, guardrails & monitoring
Systematic evaluation suites, hallucination guards and production dashboards that measure accuracy, refusal rates and cost over time — the reliability layer most AI projects skip.
Data pipelines & vector search
End-to-end data preparation — cleaning, transformation, embedding and indexing into vector stores — so the model always has fresh, relevant context to reason over.
AI strategy & build-vs-buy
An honest assessment of where a custom model or fine-tuned layer outperforms an off-the-shelf API — and where it does not. We help you invest AI budget where it compounds.
Technology stack
The AI stack we use in production
We work across the major model providers and orchestration layers — picking the combination that hits your accuracy, latency and cost targets, not the one that looks best on a slide.
OpenAIAnthropic ClaudeAzure OpenAILangChainPineconepgvectorPython.NET
Model choice, embedding strategy and vector store selection are engineering decisions, not vendor preferences. We benchmark before committing and re-evaluate as model capabilities change.
FAQ
Frequently asked questions
What can AI actually automate in my business?
The highest-value targets are usually repetitive information tasks: document review, data extraction, triage and routing, first-draft generation and customer-facing Q&A over private knowledge. The practical ceiling is determined by how much error you can tolerate and how much audit trail you need — we map this for your specific workflows in a discovery session before writing a line of code.
How do you prevent hallucinations in production?
There is no single switch — it requires layered defences. We combine retrieval-augmented generation (grounding answers in cited sources), structured output schemas (constraining what the model can say), confidence thresholds (routing low-certainty responses to a human), and a continuous eval suite that measures factual accuracy against a golden dataset you maintain. Monitoring in production catches drift before users notice.
Should we build custom AI or buy an off-the-shelf tool?
Buy for generic tasks where a SaaS product already covers 90% of your need. Build when your data is proprietary, your workflow is unusual, or the SaaS per-seat cost exceeds a custom build within 18 months. We run a structured build-vs-buy analysis early in every engagement — if the honest answer is "buy", we tell you before you pay us to build.
How do you handle our data privacy and GDPR?
We process your data under a data processing agreement aligned with GDPR. By default we use Azure OpenAI or self-hosted models so your data never trains a public model. For particularly sensitive domains — healthcare, legal, finance — we scope the architecture around on-premises or VPC-only inference from the start. Data retention policies and access controls are documented before any model sees production data.
What does an AI feature cost?
A focused LLM feature bolt-on — prompt design, retrieval layer, evals and monitoring — typically runs €20k–60k. A full agentic workflow with multiple tools and an observable trace layer is €60k–150k+. Ongoing model costs (API tokens or hosting) depend on usage volume; we estimate these during scoping so there are no surprises after launch. A free discovery call gives you a first-cut range before any commitment.
AI AutomationCase study
AI knowledge chatbot for BRD
A GPT-4 banking assistant trained on 50,000+ real conversations — 24/7, sub-2-second answers in natural Romanian, with smart handoff to human agents.
85%
auto-resolution
96%
customer satisfaction
<2s
response time
