Case Studies
Overview
Context
Challenge
Testimonial
Contribution
Impact
Why It Matters?
Overflow
Manufacturing & Logistics

Modular AI Platform Engineering

Overview
Context
Challenge
Testimonial
Contribution
Impact
Why It Matters?
Overflow
Overview & Metadata

Modular AI Platform Engineering for a FTSE 100 Company’s Manufacturing and Logistics Division

Designed and delivered a modular, reusable AI platform enabling rapid development of new use cases, cutting turnaround times from weeks to days.
Client Context
Client Context

A FTSE 100 company’s manufacturing and logistics division set out to build a unified, reusable AI platform to prevent teams from duplicating efforts across multiple projects.

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Hybrid Mind Supported →

Hybrid Mind supplied technical leadership and engineering services to architect the solution, define the tech stack, build core services, and guide UI, DevOps, and cloud teams through implementation.

Challenge

Key Challenges We Faced

The programme faced several key challenges:

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1. Fragmented Efforts Across Teams

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- Multiple groups were independently building APIs, vector DBs, prompt managers, and model integration layers.

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2. Enterprise Tooling Constraints

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- Tools such as LangSmith required procurement, security review, approval, and budget signoff.

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3. Infrastructure Complexity

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- Platform needed to support Postgres, DynamoDB, OpenSearch, Bedrock, ECS microservices,and multiple knowledge bases.

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4. Need for Modular Architecture

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- Required a plug‑and‑play system that could support many different use cases without rewriting core services.

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Our Contribution

Hybrid Mind Delivered

1. Architecture & Tech Stack Definition

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  • Designedend‑to‑end architecture (application, API, infrastructure layers).
  • Selectedstack: AWS ECS, S3, DynamoDB, Postgres, OpenSearch, Bedrock, FastAPI, React.

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2. Backend Engineering & Core Features

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  • Semantic caching
  • Text‑to‑SQL
  • Text‑to‑Cypher
  • Async FastAPI services
  • Authentication & authorisation
  • Profile and prompt management
  • Knowledge base integrations

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3. Automation,Testing & Development Standards  

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  • Created mock data frameworks and introduced unit tests (0% → 80%coverage).
  • Standardised coding, reviews,branching, and JIRA–GitHub integration.
  • Leveraged AI-assisted tooling to reduce development cycles from three weeks to a few days.

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4. Cross‑Team Enablement

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SupportedUI, DevOps, cloud engineering, and other AI module teams with patterns and guidance.

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Impact

Hybrid Mind works with enterprises to unlock real value from AI, from readiness to measurable impact

We combine strategic insight with hands-on technical execution to help enterprises turn AI potential into measurable, lasting business impact
  • New use cases can now be onboarded in under one week.  

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  • Model switching and modular reuse significantly accelerate delivery.  

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  • Increased test coverage (0% → 80%) dramatically improves system reliability.  

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  • Eliminated duplicated engineering efforts across multiple groups.  

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  • New abilities unlocked: text‑to‑SQL, text‑to‑Cypher, semantic caching,structured prompt workflows, profile‑based execution paths.  

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  • Strong client recognition for engineering excellence, leading to continued engagement.
Why It Matters?

Why It Matters?

This platform is a key enabler of AI‑driven R&D.

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It allows users to retrieve files, URLs, datasets, graphs, and application outputs simply by asking questions.

The modular platform:  

  • Future‑proofs internal AI development  
  • Reduces duplication and technical debt
  • Accelerates digital transformation initiatives  
  • Enables reusable enterprise‑scale AI engineering  
  • Strengthens organisational capability

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