AI services
AI that works inside your product — not demos that die after the pitch
Xcrino builds LLM features, RAG systems, AI agents and classical ML — with evaluation, guardrails and human approval where consequences are real.
- LLM
- RAG
- Agents
- ML
- NLP
- Vision
- 260+
- Projects delivered
- 99+
- Certified developers
- 94%
- Client satisfaction
- 6
- Countries served
Overview
What AI does Xcrino deliver?
Production features: support assistants, document extraction, forecasting, recommendation and autonomous agents that call your APIs.
We start with whether AI is the right tool — and what data you actually have — before proposing architecture.
Capabilities
AI offerings
From proof-of-concept to production with monitoring.
- LLM
RAG & chat
Grounded answers over your docs with citation and access control.
- Agents
Tool-using agents
Multi-step workflows inside ERP, CRM and support stacks.
- ML
Classical ML
Forecasting, scoring, anomaly detection when data supports it.
- Ops
MLOps & safety
Eval suites, logging, red-teaming and human-in-the-loop.
Process
How AI projects run
Feasibility before build; metrics before scale.
- 01
Assess
Use case, data quality, risk and success metrics.
- 02
Prototype
Small eval set and architecture spike with real inputs.
- 03
Production
Integration, guardrails, monitoring and rollout.
- 04
Improve
Feedback loops, model updates and cost tuning.
FAQ
Questions about AI services
Scope, timelines and how we work with your team.
Which models do you use?
OpenAI, Anthropic, open weights and hosted APIs — chosen for cost, latency and data residency requirements.
Can AI be added to our existing app?
Yes — we integrate via APIs and embed assistants without rewriting your core product.
How do you reduce hallucinations?
RAG, structured outputs, eval datasets, guardrails and human review on high-stakes actions.
Is our data used to train public models?
No — we use enterprise API terms or self-hosted models so your data is not used for third-party training.
How long does an AI proof of concept take?
Typically 3–6 weeks, ending with measured results on your real data and a go/no-go recommendation.