AI-first engineering

AWS AI/ML Services.

Logiciel helps enterprises build generative AI applications on Amazon Bedrock and machine learning systems on Amazon SageMaker. RAG, agents, fine-tuning, classical ML, evaluation and the operational layer around all of it.

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2 platforms

Bedrock for generative AI, SageMaker for ML

2 frameworks

Reference architecture and evaluation framework

Why Logiciel

Why AI Programmes on AWS Stall After the First Demo.

Why Logiciel · 01

Demos use clean data

Production has the rest of the data.

Why Logiciel · 02

Prompt-only solutions break the moment the underlying documents change.

Why Logiciel · 03

Model selection is treated as a one-off decision instead of a continuous one.

Why Logiciel · 04

Evaluation is informal, so regressions only surface when users complain.

Why Logiciel · 05

Inference costs grow faster than the use case value.

Why Logiciel · 06

Security, audit and data governance teams enter the conversation late.

What you get

What You Get When You Work With Logiciel on AWS AI.

A clear separation between Bedrock for generative AI and SageMaker for ML, with the right tool per workload.

RAG architectures built on the existing data platform, not parallel to it.

An evaluation harness that runs against every change to prompts, models or data.

MLOps and LLMOps pipelines with version control for prompts, models, datasets and evaluators.

Inference cost controls and a strategy for model selection across Bedrock and open-weights models.

Governance, audit logging and guardrails that pass security and compliance review.

What we build

AWS AI and ML Solutions Built for Enterprise Production.

Generative AI on Bedrock

What it meansBedrock-based assistants, copilots, summarisation, extraction and document workflows. Anthropic Claude, Meta Llama and Amazon Nova models, with the right one chosen per use case.

RAG and Knowledge Architectures

What it meansRetrieval pipelines on S3, OpenSearch, Aurora and Bedrock Knowledge Bases, with chunking, embedding and reranking patterns built for enterprise documents.

Agentic AI on AWS

What it meansMulti-step agents built with Bedrock Agents and orchestrators, with tool use, memory, evaluation and human-in-the-loop controls.

Machine Learning on SageMaker

What it meansClassical ML, deep learning, training pipelines, model registry and inference endpoints on SageMaker.

MLOps and LLMOps

What it meansCI/CD for ML and LLMs, evaluation harnesses, drift detection, model and prompt registries, and on-call for AI systems.

AI Governance and Guardrails

What it meansBedrock Guardrails, content filters, PII handling, audit logs, red teaming and policy alignment for enterprise use.

Engagement

Engagement Models Designed for AWS AI/ML Services (Bedrock + SageMaker) Delivery.

Dedicated AI Engineering Squad

A long-running team of AI engineers, data scientists, MLOps specialists and product engineers.

↳ Engagement

AI Advisory and Staff Augmentation

Senior AI architects and engineers who reinforce your in-house AI function during specific phases.

↳ Engagement

Outcome-Based AI Use Cases

Fixed-scope engagements for a defined use case, for example a customer support copilot, a document extraction system or a recommendation engine.

↳ Engagement

Under the hood

AWS AI and ML Services We Deliver.

Generative AI Strategy and Roadmap

Use case selection, risk assessment, model strategy and a phased roadmap aligned with your data and platform readiness.

Included

Bedrock Implementation

Bedrock-based assistants, agents, knowledge bases and guardrails, integrated with your existing systems.

Included

SageMaker Implementation

SageMaker training pipelines, model registry, real-time and batch inference, and feature stores.

Included

RAG Architecture Engineering

Retrieval architectures with chunking, embedding, vector stores, reranking and grounded generation.

Included

Agentic AI Engineering

Multi-step agents with tool use, planning, memory and evaluation.

Included

LLMOps and MLOps

CI/CD for prompts, models and datasets, evaluation harnesses, monitoring and drift detection.

Included

Insights

AWS AI/ML Services (Bedrock \+ SageMaker) Insights & Frameworks.

Patterns from our AI engineers that have run through real enterprise rollouts.

Insights

Enterprise Generative AI Reference Architecture

A reference pattern for production generative AI on Bedrock, with retrieval, evaluation, governance and observability.

Insights

AI Evaluation Framework

A practical approach to evaluating prompts, models and agent behaviours against your business rules and risk model.

Insights

How we work

Our AWS AI/ML Services (Bedrock + SageMaker) Framework.

Use Case Discovery and Risk Review

We work through the use case, the data, the user, the failure modes and the regulatory shape before we choose a pattern.

Architecture and Model Selection

We design the Bedrock and SageMaker architecture, choose models per use case and define the evaluation approach.

Build and Evaluate

We build the system in code, with prompts and models versioned, and run evaluations on every change.

Production Rollout

We move the system into production with observability, guardrails, on-call and rollout controls.

Operate and Improve

We run the AI system as a product, with continuous evaluation, model updates, cost reviews and feedback loops.

Resources

Insights for tech leaders.

Whitepaper · PDF

AI - Powered Product Development Playbook

Whitepaper · PDF

Your Highest-Intent Buyers Are Sitting in Your CRM Four Times

Whitepaper · PDF

How an Energy Retailer Used Agents to Automate 40% of Customer Ops

Questions

Frequently asked questions.

What does AWS AI/ML Services (Bedrock \+ SageMaker) include?

We cover strategy, architecture, build, deployment and operations for AWS AI/ML Services (Bedrock \+ SageMaker), aligned with your business priorities and operating constraints.

How long does AWS AI/ML Services (Bedrock \+ SageMaker) typically take?

Most engagements reach a working pilot within 4-8 weeks, while larger rollouts run across phased waves over several months.

Can Logiciel integrate AWS AI/ML Services (Bedrock \+ SageMaker) with our existing systems?

Yes. We integrate with cloud platforms, CRMs, ERPs, EHR, OT systems, analytics tools and other operational infrastructure depending on the use case.

Do you offer fixed-cost engagements for AWS AI/ML Services (Bedrock \+ SageMaker)?

Yes. We offer milestone-based pricing once scope, KPIs and delivery requirements are agreed.

Who owns the deliverables from a AWS AI/ML Services (Bedrock \+ SageMaker) engagement?

You retain ownership of all workflows, integrations, prompts, infrastructure, systems and implementation assets.

How do you handle governance and compliance for AWS AI/ML Services (Bedrock \+ SageMaker)?

We implement governance frameworks, observability, access controls, audit trails and compliance-aligned deployment practices.

How do you optimize cost for AWS AI/ML Services (Bedrock \+ SageMaker)?

We tune infrastructure, automate resource management, optimise deployment workflows and report operational cost back to teams and product lines.

Do you support ongoing operations after launch for AWS AI/ML Services (Bedrock \+ SageMaker)?

Yes. We run managed operations with SRE, observability, on-call and continuous improvement.

Let's build

Accelerate AWS AI/ML Services (Bedrock \+ SageMaker).

Ready to move AWS AI/ML Services (Bedrock \+ SageMaker) from pilot into production? Partner with Logiciel to design, build and operate AWS AI/ML Services (Bedrock \+ SageMaker) that engineering, security and business teams can all defend.

Plan your Bedrock and SageMaker programme

AWS AI/ML Services.

See Logiciel in action.

Why AI Programmes on AWS Stall After the First Demo.

Demos use clean data

Prompt-only solutions break the moment the underlying documents change.

Model selection is treated as a one-off decision instead of a continuous one.

Evaluation is informal, so regressions only surface when users complain.

Inference costs grow faster than the use case value.

Security, audit and data governance teams enter the conversation late.

What You Get When You Work With Logiciel on AWS AI.

AWS AI and ML Solutions Built for Enterprise Production.

Generative AI on Bedrock

RAG and Knowledge Architectures

Agentic AI on AWS

Machine Learning on SageMaker

MLOps and LLMOps

AI Governance and Guardrails

Engagement Models Designed for AWS AI/ML Services (Bedrock + SageMaker) Delivery.

Dedicated AI Engineering Squad

AI Advisory and Staff Augmentation

Outcome-Based AI Use Cases

AWS AI and ML Services We Deliver.

Generative AI Strategy and Roadmap

Bedrock Implementation

SageMaker Implementation

RAG Architecture Engineering

Agentic AI Engineering

LLMOps and MLOps

AWS AI/ML Services (Bedrock \+ SageMaker) Insights & Frameworks.

Patterns from our AI engineers that have run through real enterprise rollouts.

Enterprise Generative AI Reference Architecture

AI Evaluation Framework

Our AWS AI/ML Services (Bedrock + SageMaker) Framework.

Use Case Discovery and Risk Review

Architecture and Model Selection

Build and Evaluate

Production Rollout

Operate and Improve

Insights for tech leaders.

AI - Powered Product Development Playbook

Your Highest-Intent Buyers Are Sitting in Your CRM Four Times

How an Energy Retailer Used Agents to Automate 40% of Customer Ops

Frequently asked questions.

Accelerate AWS AI/ML Services (Bedrock \+ SageMaker).