Logiciel helps enterprises implement Retrieval-Augmented Generation systems that ground LLM outputs in approved business data. From document ingestion and embeddings to vector search, retrieval quality, governance, observability and production deployment, we build RAG systems that make AI more useful, reliable and secure.

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3 models

Engagement models for RAG implementation

5 steps

Our RAG implementation framework

Why Logiciel

Why Retrieval-Augmented Generation Matters for Enterprise AI.

Why Logiciel · 01

LLMs produce generic answers without enterprise-specific knowledge.

Why Logiciel · 02

Business data is scattered across documents, SaaS tools, databases and knowledge bases.

Why Logiciel · 03

Search results are often incomplete, outdated or disconnected from workflows.

Why Logiciel · 04

Teams lack control over which sources AI systems can use.

Why Logiciel · 05

Sensitive data needs access controls before retrieval happens.

Why Logiciel · 06

Users need answers that cite trusted internal context.

Why Logiciel · 07

AI systems become hard to scale when retrieval quality is not measured.

What you get

What You Get When You Work With Logiciel on RAG Implementation.

We build RAG systems that connect enterprise knowledge with secure, production-ready LLM workflows.

A clear RAG implementation roadmap tied

to business use cases

Data source assessment

across documents, databases, tools and knowledge systems

Retrieval architecture

designed around accuracy, security and scalability

Vector databases

embeddings and chunking strategies tuned for enterprise content

LLM workflows grounded in approved knowledge sources

Governance, access controls and auditability

built into retrieval pipelines

A practical RAG operating model your teams can maintain after launch

What we build

Retrieval-Augmented Generation Implementation Solutions Built for Enterprise Workloads.

RAG Strategy and Use Case Planning

Use case discovery, retrieval readiness assessment, solution roadmap and implementation sequencing for enterprise AI adoption.

Enterprise Knowledge Ingestion

Document ingestion, source system connectivity, metadata extraction and content preparation across structured and unstructured data.

Embedding and Vector Database Engineering

Embedding model selection, vector database setup, indexing, similarity search and retrieval infrastructure design.

Chunking and Retrieval Optimization

Chunking strategy, metadata filtering, hybrid search, reranking and retrieval quality tuning for better LLM responses.

LLM and RAG Workflow Integration

RAG pipelines connected to copilots, assistants, support tools, product features and enterprise workflow automation.

RAG Governance and Security

Role-based access, source permissions, audit trails, data handling rules, human review and compliance-aligned retrieval practices.

RAG Observability and Managed Operations

Monitoring for retrieval accuracy, latency, cost, source usage, hallucination risk, response quality and production incidents.

Engagement

Engagement Models Designed for Retrieval-Augmented Generation Implementation Delivery.

Dedicated RAG Engineering Squad

A standing team of LLM engineers, data engineers, cloud specialists and AI product experts embedded into your RAG roadmap.

Engagement

RAG Advisory and Staff Augmentation

Senior RAG consultants and AI architects who strengthen your internal engineering, data, platform or product teams.

Engagement

Outcome-Based RAG Implementation

Fixed-scope engagements with defined retrieval outcomes, delivery milestones and success baselines agreed up front.

Engagement

Under the hood

Retrieval-Augmented Generation Implementation Services We Deliver.

RAG Diagnostic and Roadmap

What it meansDetailed assessment of use cases, knowledge sources, document quality, data access, retrieval needs and governance gaps.

Enterprise Data and Document Ingestion

What it meansSecure ingestion from documents, wikis, CRMs, ERPs, SaaS platforms, databases, storage systems and internal knowledge bases.

Vector Search and Embedding Pipeline Development

What it meansEmbedding pipelines, vector database implementation, indexing workflows, metadata design and search performance tuning.

Retrieval Quality Engineering

What it meansChunking, reranking, hybrid search, relevance testing, source filtering, evaluation datasets and response quality improvement.

LLM Application and Copilot Integration

What it meansRAG-powered copilots, knowledge assistants, support agents, document intelligence tools and product AI experiences.

RAG Governance and Compliance Frameworks

What it meansAccess controls, permissions, auditability, source restrictions, usage monitoring, documentation and responsible AI practices.

Managed RAG Operations

What it meansOngoing monitoring, retrieval tuning, cost review, source updates, quality evaluation, incident response and continuous improvement.

Insights

Retrieval-Augmented Generation Implementation Insights & Frameworks.

Patterns from our AI-first engineering teams that help enterprises build RAG systems that users can trust.

Enterprise RAG Operating Model

How we structure ownership, source governance, retrieval evaluation, access controls, monitoring and continuous improvement across AI teams.

↳ Insights

RAG Readiness Framework

A practical approach to ranking RAG use cases by knowledge quality, source accessibility, retrieval complexity, user impact and governance risk.

↳ Insights

How we work

Our Retrieval-Augmented Generation Implementation Framework.

RAG Diagnostic and Baseline

We assess use cases, knowledge sources, document structure, data access, security controls, user workflows and business priorities.

Source and Retrieval Mapping

We identify which data sources should power the RAG system, how they should be accessed and what permissions must apply.

RAG Pipeline Engineering

We build ingestion workflows, embedding pipelines, vector indexes, retrieval logic, metadata filters and LLM integration layers.

Quality, Governance and Observability

We harden the RAG system with relevance testing, source controls, audit trails, monitoring, alerts, dashboards and runbooks.

RAG Operating Model

We hand over a repeatable RAG practice, including ownership, KPIs, evaluation cadences, source refresh workflows and improvement cycles.

Selected work

Tailored engineering for your industry.

Zeme · Real Estate Cut development costs 50% and launched 3× faster with dedicated dev teams.

Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Leap · Construction Scaled to 7-figure ARR with AI-augmented software teams.

Construction

Scaled to 7-figure ARR with AI-augmented software teams.

KW · Real Estate 56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Resources

Insights for tech leaders.

Whitepaper · PDF

AI Test Generation Evaluation Kit

Framework · PDF

Only 5% Of Your Estate Is Greenfield. A Path For New Services Is A Demo.

Framework · PDF

A Third Of Your Alert Rules Fire And Nobody Acts. AIOps Will Not Fix That.

In their words

What our clients say.

Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.

I would highly recommend them to anyone looking to scale quickly or needing support in engineering, product, or QA.

Patrick Fingles

CEO, Leap

We don't just call them Logiciel; they're part of the Zeme team. Within the first week they were contributing meaningfully to our codebase.

Elior Alayev

Founder & CEO, Zeme

The Logiciel team worked tirelessly and built everything we needed, with security and best practices across our entire platform. It allowed us to become #1 in our industry, and we couldn't have done it without them.

David Buzzelli

Co-Founder, JobProgress

Questions

Frequently asked questions.

What does Retrieval-Augmented Generation Implementation include?

Retrieval-Augmented Generation Implementation includes use case planning, data ingestion, embedding pipelines, vector databases, retrieval design, LLM integration, governance, observability, deployment and managed operations.

What is Retrieval-Augmented Generation?

Retrieval-Augmented Generation is an AI architecture that lets an LLM retrieve relevant information from approved data sources before generating an answer. This helps improve accuracy, context and trust.

How long does Retrieval-Augmented Generation Implementation typically take?

Most engagements produce a diagnostic, roadmap and working RAG prototype within 4-8 weeks, while larger enterprise implementations run across phased rollout waves.

Can Logiciel integrate RAG with our existing enterprise systems?

Yes. We integrate RAG systems with cloud platforms, CRMs, ERPs, SaaS tools, document repositories, databases, knowledge bases, APIs and internal applications depending on your environment.

Do you offer fixed-cost engagements for Retrieval-Augmented Generation Implementation?

Yes. We offer milestone-based pricing once scope, data sources, KPIs, governance requirements, integration needs and delivery milestones are agreed.

Who owns the deliverables from a Retrieval-Augmented Generation Implementation engagement?

You retain ownership of all ingestion pipelines, embeddings, vector indexes, retrieval workflows, prompts, integrations, infrastructure, dashboards, documentation and implementation materials.

How do you handle governance and security for RAG systems?

We implement role-based access, source permissions, audit trails, metadata controls, data handling rules, monitoring, human review workflows and compliance-aligned retrieval practices.

Do you support ongoing RAG operations after launch?

Yes. We run managed operations with observability, retrieval quality tracking, source refresh support, cost review, incident response, relevance tuning and continuous improvement.

Let's build

Accelerate Retrieval-Augmented Generation Implementation.

Ready to turn Retrieval-Augmented Generation Implementation into a trusted foundation for enterprise AI? Partner with Logiciel to connect LLMs with governed knowledge, improve answer quality and operate RAG systems with production-grade control.

Start a RAG implementation assessment

Retrieval-Augmented Generation (RAG) Implementation.

See Logiciel in action.

Why Retrieval-Augmented Generation Matters for Enterprise AI.

LLMs produce generic answers without enterprise-specific knowledge.

Business data is scattered across documents, SaaS tools, databases and knowledge bases.

Search results are often incomplete, outdated or disconnected from workflows.

Teams lack control over which sources AI systems can use.

Sensitive data needs access controls before retrieval happens.

Users need answers that cite trusted internal context.

AI systems become hard to scale when retrieval quality is not measured.

What You Get When You Work With Logiciel on RAG Implementation.

A clear RAG implementation roadmap tied

Data source assessment

Retrieval architecture

Vector databases

LLM workflows grounded in approved knowledge sources

Governance, access controls and auditability

A practical RAG operating model your teams can maintain after launch

Retrieval-Augmented Generation Implementation Solutions Built for Enterprise Workloads.

RAG Strategy and Use Case Planning

Enterprise Knowledge Ingestion

Embedding and Vector Database Engineering

Chunking and Retrieval Optimization

LLM and RAG Workflow Integration

RAG Governance and Security

RAG Observability and Managed Operations

Engagement Models Designed for Retrieval-Augmented Generation Implementation Delivery.

Dedicated RAG Engineering Squad

RAG Advisory and Staff Augmentation

Outcome-Based RAG Implementation

Retrieval-Augmented Generation Implementation Services We Deliver.

RAG Diagnostic and Roadmap

Enterprise Data and Document Ingestion

Vector Search and Embedding Pipeline Development

Retrieval Quality Engineering

LLM Application and Copilot Integration

RAG Governance and Compliance Frameworks

Managed RAG Operations

Retrieval-Augmented Generation Implementation Insights & Frameworks.

Enterprise RAG Operating Model

RAG Readiness Framework

Our Retrieval-Augmented Generation Implementation Framework.

RAG Diagnostic and Baseline

Source and Retrieval Mapping

RAG Pipeline Engineering

Quality, Governance and Observability

RAG Operating Model

Tailored engineering for your industry.

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Scaled to 7-figure ARR with AI-augmented software teams.

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Insights for tech leaders.

AI Test Generation Evaluation Kit

Only 5% Of Your Estate Is Greenfield. A Path For New Services Is A Demo.

A Third Of Your Alert Rules Fire And Nobody Acts. AIOps Will Not Fix That.

What our clients say.

Frequently asked questions.

Accelerate Retrieval-Augmented Generation Implementation.