Generative AI on Bedrock for Mid-Market
Bedrock-based assistants, copilots, summarisation, extraction and document workflows for mid-market customer and internal use cases.
Run AWS AI in production without building an enterprise AI org.
Logiciel delivers Amazon Bedrock and Amazon SageMaker implementations for mid-market companies. Generative AI, classical ML, agents and MLOps, sized for mid-market teams and budgets, with cost and evaluation built in. We bring the engineers, the platform and the operating layer. You stay close to the business problem.
Mid-market AWS AI programmes hit predictable issues.
We give mid-market businesses production AWS AI without an enterprise programme.
We cover the AWS AI patterns that recur for mid-market businesses.
Bedrock-based assistants, copilots, summarisation, extraction and document workflows for mid-market customer and internal use cases.
Retrieval pipelines on S3, OpenSearch, Aurora and Bedrock Knowledge Bases for mid-market document repositories and knowledge bases.
Multi-step agents built with Bedrock Agents, with tool use, evaluation and human-in-the-loop controls sized for mid-market workflows.
Forecasting, churn, fraud, pricing and operational ML on SageMaker, with training pipelines and inference endpoints.
CI/CD for ML and LLMs, evaluation harnesses, drift detection, model and prompt registries.
Bedrock Guardrails, content filters, audit logs and policy alignment, right-sized for mid-market compliance posture.
Model selection, prompt and context optimisation, caching, Provisioned Throughput, batch inference and SageMaker rightsizing.
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.
AWS AI Advisory and Staff Augmentation
Senior AWS AI architects who reinforce your in-house team during specific phases.
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.
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.
Patterns from our AI engineers that have run through real mid-market deployments.
A reference pattern for production generative AI on Bedrock with retrieval, evaluation, governance and observability, sized for a mid-market business.
A practical approach to evaluating prompts, models and agent behaviours against your business rules, right-sized for mid-market.
1. 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.
2. Architecture and Model Selection
We design the Bedrock and SageMaker architecture, choose models per use case and define the evaluation approach.
3. Build and Evaluate
We build the system in code, with prompts and models versioned, and run evaluations on every change.
4. Production Rollout
We move the system into production with observability, guardrails, on-call and rollout controls sized for mid-market.
5. Operate and Improve
We run the AI system as a product with continuous evaluation, model updates, cost reviews and feedback loops.
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.
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.
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.