Automated L1 Support

67% of B2B leads
are lost if they don't get
a response in 5 minutes.

Your team cannot maintain a response SLA in seconds at 10:00 PM on a Tuesday. We design and implement Corporate Chatbots based on Large Language Models (LLMs) strictly trained on your company's knowledge. 24/7 technical precision without making up data.

⚠️ Lead leak mitigation ✓ RAG Architecture (Own data) 🔒 Data privacy guaranteed
Technological Evolution

Overcoming the obsolescence
of decision trees

Traditional chatbots (based on rules and buttons) generate friction: if the user asks a question outside the exact script, the system collapses and frustrates the customer.

Our assistants use Natural Language Processing (NLP). They understand the context, intention, and semantic nuances of the query, cross-referencing information with your manuals, catalogs, or internal knowledge bases (RAG Architecture) to deliver resolving answers.

Context injection: responds exclusively with your corporate data.
Semantic understanding: understands complex variations of the same question.
Uninterrupted availability (24/7) assuming massive concurrency peaks.
Escape protocol: diverts to a human operator when faced with critical queries.
Demo — Private Clinic Assistant
Hi, I wanted to know the price of a dental cleaning and if you take emergencies today.
Hello! A professional dental cleaning costs €65 (it may be covered if you have an associated mutual insurance). Regarding emergencies, yes, our on-call team attends until 8:00 PM. Would you like me to transfer you to reception to coordinate an urgent visit? 🦷
No emergency needed, but I would like to book the cleaning for Thursday.
Perfect. To process the cleaning booking this Thursday, I am transferring you right now to our admissions team. One moment, please... ⏳
Opportunity Cost

Response latency destroys conversion

Traditional operation
  • 67% of users without a response in less than 5 minutes contact the competition (B2B Data).
  • Your technical or sales staff spends hours weekly repetitively answering the same level 1 questions.
  • Lost opportunities: queries generated on weekends or outside business hours go cold.
AI Infrastructure
  • First Response Time (FRT) reduced to milliseconds, guaranteeing lead retention.
  • Automatic filtering and triaging: your human team only intervenes in high-value operations or sales closures.
  • Total hourly coverage without incurring the costs of night shifts or outsourced support.
Suitability Criteria

This deployment is strategic if...

  • Your customer service (L1) is saturated by recurring queries and basic operations.

  • You offer global services and need to support multiple languages without hiring native agents.

  • You handle an extensive volume of technical, legal, or product documentation that clients do not read themselves.

  • You seek to qualify leads (Triaging) by extracting key variables before assigning the ticket to your sales team.

  • You want to centralize data governance and not rely on third-party SaaS plugins that violate your users' privacy.

Sectorial use cases

Operational applications of the model

🏥

Health and Clinics

Initial patient triaging, resolving doubts about insurance coverage, treatment explanations, and pre/post-operative FAQ management.

⚖️

Legal and B2B Services

Qualification of legal or corporate leads. The assistant extracts the context of the client's problem before scheduling the consultation with the partner.

🛒

E-commerce

Resolution of shipping policies, returns, product specifications, and size guides, mitigating support ticket volume.

🏨

Tourism and Hospitality

Multilingual digital reception: inquiries about facilities, service hours, cancellation policies, and local area guides.

🔧

IT Support and SaaS

Level 1 incident resolution by ingesting your software's technical documentation or API docs. Escalation to Level 2 only if necessary.

📚

Education and Universities

Assistant for prospective students: resolving doubts about study plans, admission requirements, scholarships, and academic calendars.

Service engineering

Implementation phases (2–4 weeks)

01

Data Ingestion (Data Pipeline)

We collect and structure your company's knowledge corpus (Databases, PDFs, URLs, Manuals) for processing.

02

Vectorization and Embeddings

We transform your documentation into vector representations so the Language Model (LLM) can search and retrieve the exact information (RAG Architecture).

03

Prompting and Guardrails

We fine-tune the model's behavior: corporate tone of voice, response limits (to avoid hallucinations), and strict rules for human handoff.

04

Deployment and Telemetry

Installation on your web infrastructure. We activate a telemetry dashboard to audit logs, measure the resolution rate, and continuously retrain the model.

Frequently asked questions

Technical specifications of the Chatbot

We implement a strict RAG (Retrieval-Augmented Generation) architecture. The model is forbidden from using its general knowledge; it only formulates answers by extracting paragraphs from the document database we provide.

Unlike SaaS solutions like Tidio or Intercom, our deployment can be isolated on your own infrastructure (Self-Hosted) or dedicated instances, guaranteeing regulatory compliance (GDPR) and industrial secrecy.

Yes. The AI core is channel agnostic. We can connect the same cognitive brain to a web widget, the official WhatsApp Business API, or channels like Slack and Microsoft Teams for internal support.

We only need to replace or update the source document in the vector database. The bot will acquire the new knowledge in real-time, without requiring code reprogramming.

Yes. Modern LLMs are inherently multilingual. The bot understands the query in the source language (e.g., German), searches for the answer in your documentation (e.g., Spanish), and translates the output to German instantly.

Proof of concept

Audit your current response capacity

Request a technical meeting. We will evaluate your current support volume and the viability of delegating Level 1 to a cognitive assistant.