ENTERPRISE AI & RAGEnterprise AI, RAG & Automation Middleware

Enterprise AI Agents & RAG Knowledge Base Pipelines

Turn enterprise documents and workflows into evaluated AI assistance with role-aware access, source attribution, monitored actions, and explicit fallback behavior.

Target Search Intent:Commercial & Technical — Custom Enterprise AI Agents & RAG Development
Key Specifications4–12 weeks
InvestmentFrom € 8,000
Target Regional FocusDACH (Germany, Austria, Switzerland) · United States & UK · MENA (UAE, Saudi Arabia)
Milestone Roadmap

Engineering Deliverables & Verifiable Outcomes

Fixed-scope, sprint-based implementation plan with transparent deliverables at every milestone.

01

Enterprise Knowledge Ingestion & Vector Pipeline

Week 1–3

Ingesting PDFs, DOCX, Notion, Slack, databases, and APIs with semantic chunking and high-dimensional vector embeddings.

Impact: Documented ingestion coverage, exclusions, permissions, and retrieval test results.
02

Grounded RAG Architecture with Fact Verification

Week 3–6

Multi-stage retrieval, reranking, source attribution, answer constraints, and fallback behavior designed to reduce unsupported answers.

Impact: Verifiable answers directly linked to internal corporate source documents.
03

Autonomous Multi-Step Agent Workflows

Week 6–9

Tool-calling agents capable of querying CRMs, generating proposals, classifying support tickets, and triggering API actions.

Impact: Time saved and exception rates measured against the existing workflow baseline.
04

Security, RBAC & GDPR Compliance Hardening

Week 9–12

Strict tenant isolation, audit logging, data masking, and options for EU-hosted or on-premise private LLM deployment.

Impact: Implemented controls and documented gaps for review by the client’s security, privacy, and legal owners.
✓
Outcome: A production RAG or agent workflow with an agreed evaluation set, quality and latency baselines, access controls, audit logs, and monitored escalation paths.
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From Toy Chatbots to Production Enterprise Agents

Basic chatbots often struggle with unsupported answers, missing business context, permissions, and multi-step actions. Enterprise deployments need evaluated, observable systems with access control, bounded tools, fallbacks, and human review where risk requires it.

We engineer enterprise-grade AI agents that understand your data, execute complex workflows, and integrate directly with your tech stack.

┌─────────────────────────────────────────────────────────────┐
│ Enterprise Document & Database Sources                      │
│ (Contracts, Technical Specs, Notion, CRM, SQL)             │
└──────────────────────────────┬──────────────────────────────┘
                               │ Semantic Chunking & Embedding
                               ▼
┌─────────────────────────────────────────────────────────────┐
│ High-Dimensional Vector DB (Qdrant / Pinecone / pgvector)  │
└──────────────────────────────┬──────────────────────────────┘
                               │ Hybrid Query + Reranking
                               ▼
┌─────────────────────────────────────────────────────────────┐
│ Autonomous Agent Engine (Tool Calling & Fact Verification)  │
└──────────────┬──────────────────────────────┬───────────────┘
               │                              │
               ▼                              ▼
      [Verified Citation]           [API Action: CRM / ERP Sync]

Key Agent Capabilities

  • Autonomous Tool Execution: Agents query your CRM, generate PDF quotes, schedule calendar appointments, or trigger webhook webhooks.
  • Hierarchical Access Control: Respect user permission levels so employees only query documents they are authorized to view.
  • Multilingual Understanding: Native comprehension and responses in German, English, Arabic, and Russian across global teams.

Deliverables & Milestones

StageFocus AreaDeliverable
01. Discovery & Data AuditData mapping & use case scopingTechnical requirements & vector pipeline architecture
02. RAG FoundationChunking, embeddings & vector storeWorking retrieval API with citation accuracy testing
03. Agent ToolingCRM/ERP integrations & action loopsAutonomous multi-step workflow execution
04. Security & DeploymentRBAC, penetration testing, telemetryProduction deployment with monitoring dashboard
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Frequently Asked Questions

Technical & Commercial Clarity

Answers to the most critical architecture, indexing, and investment questions.

Retrieval, reranking, source attribution, constrained prompts, abstention rules, tool permissions, and human escalation can reduce risk but cannot make a probabilistic model error-free. We measure retrieval quality and answer faithfulness on an agreed evaluation set and monitor production failures.

Connected delivery example

See how this capability connects to a real build

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View the case breakdown →
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