Internal helpdesk
The chatbot answers employees' HR, IT and admin questions citing up-to-date company policies.
01 / LLM-RAG
Artificial Intelligence
LLM & RAGArtificial Intelligence — Conversation, search and reasoning over your company's data.
Conversation, search and reasoning over your company's data.
We design conversational systems powered by your documents, databases and APIs. We combine frontier Large Language Models with Retrieval-Augmented Generation architectures to deliver reliable, cited, traceable answers — not hallucinations.
−68%
Average response time
92%
Answers with correct citation
4–6 wks
Time-to-PoV
§ A
Overview
General-purpose LLMs don't know your company. Our RAG practice closes the gap: we index internal knowledge (manuals, contracts, tickets, knowledge bases, code, email), make it semantically searchable and inject it into the model's context at query time.
The result: answers that cite the exact source, stay up to date as documents change, respect user permissions and work in Italian, English and 20+ languages. It runs on-premise, in private cloud or on managed models (Azure OpenAI, AWS Bedrock, Vertex AI).
§ B
What's included
- Discovery of high-value use cases (helpdesk, sales support, onboarding, compliance, knowledge search)
- Automated document ingestion from SharePoint, Confluence, Drive, Notion, file systems, databases and APIs
- Chunking, embedding and vector indexing with domain-tuned strategies
- Re-ranking and hybrid search pipelines (semantic + keyword) for higher accuracy
- Guardrails on PII, prompt injection, toxic output and out-of-scope content
- Continuous evaluation with test datasets and RAGAS metrics
- White-label conversational UI or integration into Teams, Slack, existing intranets
§ C
Deliverables
What you get at the end — or along the way — of an engagement on LLM & RAG.
- D/01Documented technical architecture and C4 diagrams
- D/02Automated ingestion and re-indexing pipeline
- D/03API endpoint with authentication and logging
- D/04Quality and cost monitoring dashboard
- D/05Operating runbook and internal team training
§ D
Use cases
Sales copilot
Account managers ask in plain language about pricing, product sheets and customer cases and get answers linked to the CRM.
Compliance & Legal
Contextual search over contracts, regulations and clauses with citation of the exact paragraph.
Customer support
Automatic ticket triage and reply suggestions to agents, lowering average handling time.
§ E
Our process
01
Discovery
Two-week workshop to map use cases, data sources, security requirements and KPIs.
02
Proof of Value
Working prototype in 4–6 weeks on a high-impact use case, evaluated on real datasets.
03
Production pilot
Release to a small user group, feedback collection, prompt and retrieval tuning.
04
Scale-out
Company-wide rollout, SSO integration, monitoring and user enablement.
05
Run & Improve
Evolutive maintenance, model updates, expansion of data sources.
§ F
Technologies
OpenAI GPT-4/5Anthropic ClaudeLlama 3 / MistralLangChain · LlamaIndexpgvector · Qdrant · WeaviateAzure AI SearchCohere Rerank
Indicative stack. We adapt choices to your context, internal skills and existing constraints.
§ G
Frequently asked questions
Q/01Does my data stay private?+
Yes. We only work with providers that guarantee no-training on prompts (Azure OpenAI, AWS Bedrock) or with self-hosted open-source models. All data stays within your perimeter, encrypted at rest and in transit.
Q/02How much does it cost?+
A PoV starts around €25–40k. Runtime costs depend on query volume and chosen models — typically between €0.001 and €0.05 per query.
Q/03What happens when the model is wrong?+
Every answer cites its sources so the user can verify. We implement guardrails, fallback to a human operator and full logging for audit.
Q/04Can I use my own on-premise model?+
Absolutely. We support Llama, Mistral, Qwen and other open-weights with vLLM or TGI on on-prem or private cloud GPUs.
Next step
Let's talk about llm & rag.
A 30-minute call to understand your context and whether we can really help. No commitment.