AWS AI and ML Services on Bedrock and SageMaker for Mid-Market

Why Logiciel

Why Mid-Market AWS AI Programmes Stall.

Why Logiciel · 01

Pilots are built around a model rather than a business decision.

Why Logiciel · 02

Bedrock model selection becomes a one-off choice that is never revisited.

Why Logiciel · 03

Prompts and retrieval are tuned on demo data.

Why Logiciel · 04

Inference costs grow faster than the number of users.

Why Logiciel · 05

There is no evaluation harness, so regressions appear in customer feedback.

Why Logiciel · 06

Security and audit teams enter late and reshape the architecture.

What you get

What You Get When You Work With Logiciel on Mid-Market AWS AI.

01

A clear separation between Bedrock for generative AI and SageMaker for ML.

02

RAG architectures built on existing AWS data sources, not parallel to them.

03

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

04

LLMOps and MLOps pipelines sized for a mid-market engineering team.

05

Inference cost controls with model routing, caching, Provisioned Throughput and small-model fallbacks.

06

A managed operating layer with monitoring, on-call and continuous improvement.

What we build

Mid-Market AWS AI Solutions Built for Production.

01

Generative AI on Bedrock for Mid-Market

What it meansBedrock-based assistants, copilots, summarisation, extraction and document workflows for mid-market customer and internal use cases.

02

RAG and Knowledge Architectures on AWS

What it meansRetrieval pipelines on S3, OpenSearch, Aurora and Bedrock Knowledge Bases for mid-market document repositories and knowledge bases.

03

Agentic AI on AWS for Mid-Market

What it meansMulti-step agents built with Bedrock Agents, with tool use, evaluation and human-in-the-loop controls sized for mid-market workflows.

04

Classical ML on SageMaker for Mid-Market

What it meansForecasting, churn, fraud, pricing and operational ML on SageMaker, with training pipelines and inference endpoints.

05

MLOps and LLMOps Sized for Mid-Market

What it meansCI/CD for ML and LLMs, evaluation harnesses, drift detection, model and prompt registries.

06

AI Governance Sized for Mid-Market

What it meansBedrock Guardrails, content filters, audit logs and policy alignment, right-sized for mid-market compliance posture.

07

AI Cost Optimisation on AWS for Mid-Market

What it meansModel selection, prompt and context optimisation, caching, Provisioned Throughput, batch inference and SageMaker rightsizing.

Engagement

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

01

Dedicated AWS AI Squad for Mid-Market

A long-running team of AI engineers, MLOps specialists and product engineers sized for a mid-market business.

Engagement

02

AWS AI Advisory and Staff Augmentation

Senior AWS AI architects who reinforce your in-house team during specific phases.

Engagement

03

Outcome-Based AWS AI Use Cases

Fixed-scope engagements for a defined use case, for example a customer support copilot on Bedrock or a forecasting model on SageMaker.

Engagement

Under the hood

Mid-Market AWS AI Services We Deliver.

Mid-Market Generative AI Strategy on AWS

Use case selection, risk assessment, model strategy and a phased roadmap sized for a mid-market business.

Bedrock Implementation for Mid-Market

Bedrock-based assistants, agents, knowledge bases and guardrails for mid-market workflows.

SageMaker Implementation for Mid-Market

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

RAG Architecture on AWS for Mid-Market

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

Agentic AI on AWS for Mid-Market

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

LLMOps and MLOps on AWS for Mid-Market

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

AI Governance on AWS for Mid-Market

Bedrock Guardrails, content filters, audit logging and policy alignment, right-sized for mid-market.

AI Cost Optimisation on AWS for Mid-Market

Model selection, caching, Provisioned Throughput, batch inference and SageMaker cost control.

Insights

AWS AI/ML Services (Bedrock + SageMaker) for Mid-Market Insights & Frameworks.

Patterns from our AI engineers that have run through real mid-market deployments.

01

Mid-Market Generative AI Reference Architecture on AWS

A reference pattern for production generative AI on Bedrock with retrieval, evaluation, governance and observability, sized for a mid-market business.

↳ Insights

02

Mid-Market AI Evaluation Framework

A practical approach to evaluating prompts, models and agent behaviours against your business rules, right-sized for mid-market.

↳ Insights

How we work

Our AWS AI/ML Services (Bedrock + SageMaker) for Mid-Market Framework.

01

Use Case Discovery and Risk Review

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

02

Architecture and Model Selection

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

03

Build and Evaluate

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

04

Production Rollout

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

05

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

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.

What does AWS AI/ML Services (Bedrock + SageMaker) for Mid-Market include?

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

How long does AWS AI/ML Services (Bedrock + SageMaker) for Mid-Market 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) for Mid-Market 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) for Mid-Market?

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) for Mid-Market 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) for Mid-Market?

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) for Mid-Market?

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) for Mid-Market?

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

Let's build

Accelerate AWS AI/ML Services (Bedrock + SageMaker) for Mid-Market.

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

Plan your mid-market AWS AI rollout

AWS AI/ML Services (Bedrock + SageMaker) for Mid-Market.

See Logiciel in action.

Why Mid-Market AWS AI Programmes Stall.

Pilots are built around a model rather than a business decision.

Bedrock model selection becomes a one-off choice that is never revisited.

Prompts and retrieval are tuned on demo data.

Inference costs grow faster than the number of users.

There is no evaluation harness, so regressions appear in customer feedback.

Security and audit teams enter late and reshape the architecture.

What You Get When You Work With Logiciel on Mid-Market AWS AI.

Mid-Market AWS AI Solutions Built for Production.

Generative AI on Bedrock for Mid-Market

RAG and Knowledge Architectures on AWS

Agentic AI on AWS for Mid-Market

Classical ML on SageMaker for Mid-Market

MLOps and LLMOps Sized for Mid-Market

AI Governance Sized for Mid-Market

AI Cost Optimisation on AWS for Mid-Market

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

Dedicated AWS AI Squad for Mid-Market

AWS AI Advisory and Staff Augmentation

Outcome-Based AWS AI Use Cases

Mid-Market AWS AI Services We Deliver.

AWS AI/ML Services (Bedrock + SageMaker) for Mid-Market Insights & Frameworks.

Mid-Market Generative AI Reference Architecture on AWS

Mid-Market AI Evaluation Framework

Our AWS AI/ML Services (Bedrock + SageMaker) for Mid-Market 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) for Mid-Market.