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AWS AI/ML Services

Move AI from pilot to production on AWS without losing the budget.

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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Why AI Programmes on AWS Stall After the First Demo

Almost every enterprise has a working demo. Few have a production AI system.

  • Demos use clean data. Production has the rest of the 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

We give product, data and platform teams an AI system they can actually operate.

  • 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.

AWS AI and ML Solutions Built for Enterprise Production

We cover the full path from data to production AI, on AWS.

Generative AI on Bedrock

Bedrock-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

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

Agentic AI on AWS

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

Machine Learning on SageMaker

Classical ML, deep learning, training pipelines, model registry and inference endpoints on SageMaker.

MLOps and LLMOps

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

AI Governance and Guardrails

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

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.

AI Advisory and Staff Augmentation

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

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.

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.

Bedrock Implementation

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

SageMaker Implementation

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

RAG Architecture Engineering

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

Agentic AI Engineering

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

LLMOps and MLOps

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

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

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

AI Evaluation Framework

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

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.

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

Frequently Asked Questions

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

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

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

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

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

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

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

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