Automated prospecting, 24/7 customer support, and instant access to your company knowledge.
Deploy in 30 days. ROI from week one.
They're not separate tools. They're two complete pipelines, designed together around the real workflows of a company that wants to grow.
You work directly with the people building your system — not account managers, not support tickets. The founders.
We pick the best model for every task. We test every emerging technology and integrate it where it delivers real results.
We’re model-agnostic by design: we select the right model for each task without being locked to any single vendor. In 2026, the best open-source models have matched — and in some benchmarks surpassed — commercial ones. On-premise deployment is no longer a compromise: it’s the rational choice.
Commercial models: Anthropic Claude Fable 5 / Mythos 5 (top reasoning), Claude Opus 4.8 (agentic coding, 1M context), Claude Sonnet 5 — OpenAI GPT-5.6 Sol (all-rounder, agentic) — Google Gemini 3.1 Pro (multimodal, long-context up to 2M tokens) — xAI Grok 4.5 (real-time data, cost-efficient) — Alibaba Qwen3.7 Max (multilingual)
Open-source frontier — self-hostable: Zhipu GLM 5.2 (MIT — outperformed closed models on SWE-Bench Pro, July 2026), DeepSeek V4 / R3 (MIT — near-frontier at 1/10 the cost), Kimi K2.7 (top open-weight on Artificial Analysis Intelligence Index), Qwen 4.1 32B (Apache 2.0), Microsoft Phi-5 (MIT, optimized for on-premise), Google Gemma 4.5, Mistral Medium 4 (Apache 2.0)
The right choice depends on: data sensitivity, latency requirements, volume, budget, and regulatory context. For sensitive data: local inference — zero cloud traffic, zero per-call API costs at scale.
“Naive RAG” — chunk the doc, vectorize, pass to the LLM — is obsolete. Production-grade systems require evolved architectures:
GraphRAG — Extracts entities and relationships to build a knowledge graph. Enables multi-hop reasoning: answers questions that require connecting information scattered across multiple documents.
Agentic RAG — Retrieval becomes dynamic. The agent runs multiple sub-queries, self-evaluates retrieved document quality (Self-RAG), and triggers fallback web searches when the company corpus doesn’t have the answer (Corrective RAG / CRAG).
Hybrid Retrieval + Cross-Encoder Reranking — Semantic vector search combined with lexical BM25 precision. A neural reranker re-orders retrieved chunks by relevance before generation. Critical for product codes, invoice numbers, exact references.
Multimodal RAG — Indexes technical diagrams, tables, charts, and images. The system extracts context directly from the visual component — no manual transcription required.
Parent-Child Chunking + RAPTOR — Search on precise fragments, generate with the full parent context. RAPTOR indexes by abstraction level: from specific details up to the document’s thematic overview.
MCP (Model Context Protocol) — The open standard for connecting AI agents to external tools, databases, and resources natively. Agents access external context as an extension of their reasoning — not as a traditional REST call.
Proprietary orchestration environment — Manages persistent sessions, contextual memory, human-in-the-loop supervision, and complex task scheduling across multi-agent architectures.
Enterprise systems: CRM (Salesforce, HubSpot, Zoho), ERP, SQL/NoSQL databases, email, LinkedIn, WhatsApp. Every integration documented, reversible, and least-privilege by design.
On-premise deployment on dedicated GPU infrastructure for local inference. Open-source models run internally for sensitive data or high-volume workloads: zero dependency on shared cloud, zero risk of your data training someone else’s model, full data sovereignty from day one.
Dedicated private cloud option available for organizations that prefer scalability without vendor lock-in.
The question every business manager asks before adopting an AI agent isn't "does it work?" — it's "if it does something wrong, will I notice? can I stop it? can I understand why?". The answer must always be yes.
A chatbot answers predefined questions following fixed scripts. An AI agent is an autonomous system that perceives context, plans actions, uses external tools (databases, APIs, email, CRM) and completes complex objectives without continuous human intervention. It can search for information, qualify a lead, update a management system and send a communication — all in a single flow.
The most suitable processes are repetitive ones, based on structured data with definable rules: prospecting and lead qualification, FAQ management and first-level customer support, research and synthesis of company documents, automated monitoring and reporting. In general: everything that requires time but not strategic judgment is a candidate for automation.
Yes. AI Evolution agents connect to CRMs, ERPs, Google/Excel sheets, email inboxes, company databases and any system with an API. Integration is part of the initial project: the existing infrastructure is not replaced, we connect on top of it.
The first agent in production typically requires 3-4 weeks from the initial brief: one week of process analysis, one for development and configuration, one for testing in a real environment. The pilot starts with defined KPIs and real data. If the results are positive, we scale; otherwise we stop without additional costs.
Yes. Agents are designed to evolve: they update with new business rules, adapt to new workflows and improve based on operational feedback. Maintenance is continuous and included in the service.
No, by default. AI Evolution agents work on on-premise infrastructure or private cloud dedicated to the client. Company data is not used to train public models and does not leave the perimeters agreed upon contractually.
The infrastructure is hardened according to best practices: SSH access with keys, multi-factor authentication, network segmentation, automatic security updates and continuous monitoring. Every deployment includes an initial security assessment and complete architecture documentation.
Yes, it's a design priority. Every agent action is logged and traceable: what it read, what reasoning it applied, what action it performed. Logs are viewable by the client in real time. We don't sell black boxes: operational transparency is part of the service.
The agent operates with dedicated credentials with minimum privileges: it only accesses what is needed for its specific task. Credentials are encrypted, periodically rotated and never exposed in plain text. Access is revoked immediately when necessary.
Every agent operates within defined perimeters: it has no access to systems or data outside its scope. In case of anomalous output, the system generates an alert and escalates to human supervision before proceeding. Complete logs allow identifying the cause, correcting the behavior and updating operational rules quickly.
GDPR compliance is integrated into the project from the start: legal basis for processing, data minimization, data subjects' rights, limited retention. Before any deployment involving personal data, a Data Processing Agreement (DPA) is drafted and, if necessary, a DPIA. We always recommend a check with your DPO.
AI Evolution agents generally fall into the limited or minimal risk category under the EU AI Act. For high-risk systems (e.g. HR, credit, security) we apply the required measures: technical documentation, mandatory human supervision, registration of automated decisions. We follow regulatory developments and update deployments accordingly.
Operational responsibility remains with the client: the agent acts on behalf of the company, not autonomously. AI Evolution is responsible for the technical correctness of the system and compliance with agreed specifications. The service contract clearly defines perimeters, operational limits and escalation procedures to human supervision.
Yes, under GDPR and the AI Act transparency is required: anyone interacting with an automated system must be informed. For AI customer care, this translates into a clear statement in the interface. For internal processes involving employee data, privacy notices must be updated. We support you in drafting all necessary documentation.
Yes. Service contracts define operational SLAs, response times in case of anomalies, escalation procedures and conditions for exiting the service. In case of interruption, your data remains accessible and the system can be transferred or restored on another infrastructure. Your data is always exportable and portable.
We respond within 24 hours. No strings attached.