Case Studies
Overview
Context
Challenge
Testimonial
Contribution
Impact
Why It Matters?
Overflow
Information Technology & Services

Fine-Tuning & Domain Adaptation Pipelines

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

Fine-Tuning & Domain Adaptation Pipelines for a Fortune 500 Information Technology & Services Company

Delivered scalable fine-tuning and domain-adaptation pipelines that significantly improved classification accuracy and reduced latency across AI workflows.
Client Context
Client Context

A Fortune 500 Information Technology & Services Company required advanced AI capabilities to improve the accuracy and performance of classification tasks central to operational decision-making.

‍

Their internal teams possessed strong data science skills but lacked deep expertise in LLM fine-tuning, domain adaptation, synthetic data generation, and pipeline engineering.

Hybrid Mind Supported →

Hybrid Mind supported the design and implementation of reusable fine-tuning pipelines, enabling the division to refine and scale models for future workloads.

Challenge

Key Challenges We Faced

The programme faced several key challenges:

‍

1. Constrained Development Environment

‍

  • SageMaker Notebook environments were unstable and limited.
  • Dependency management was difficult due to kernel inconsistency and hardware constraints.
  • Notebook-driven workflows slowed development and reduced modularity.

‍

2. Synthetic Data Requirements

‍

  • Raw operational text was unsuitable for classification tasks.  
  • Required generating synthetic datasets using Bedrock and locally hosted open‑source models.

‍

3. Compliance & Security Restrictions

‍

  • No direct use of Hugging Face models.  
  • All external models required internal scanning and approval, slowing iteration cycles.

‍

Our Contribution

Hybrid Mind Delivered

1. End-to-End Fine-Tuning Pipelines.

‍

  • Full encoder fine-tuning  
  • LoRA-based parameter-efficient tuning  
  • LLM fine-tuning for manufacturing/logistics tasks  
  • Continued pre-training on domain corpora  
  • Supervised fine-tuning to structure outputs correctly

‍

2. Reusable Platform Capability.

‍

  • Pipelines designed to be model‑agnostic for future use.  
  • Tracked with MLflow and stored in S3 for full reproducibility.

‍

3. Technical Leadership.

‍

  • Advised on transitioning from notebook-led development to modular code.  
  • Provided experiment design guidance and methodological options.  
  • Ensured collaboration across internal engineering and product teams.

‍

4. Tech Stack.

‍

AWS SageMaker, Python, PyTorch, MLflow, Bedrock.

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
  • Achieved an F1 score of 0.892 (~89%), significantly outperforming untuned LLM/RAG baselines.

‍

  • Reduced inference latency using optimised fine‑tuned models.

‍

  • Enabled rapid future fine‑tuning without repeating engineering setup.

‍

  • Standardised and accelerated experimentation workflows.

‍

  • Upgraded internal capability for domain-specific AI development.
Why It Matters?

Why It Matters?

This work underpins the company’s broader AI adoption strategy by:

‍

  • Improving model accuracy on highly specialised tasks  
  • Enabling faster, more efficient development cycles  
  • Reducing operational latency and improving decision-support workflows  
  • Strengthening internal capability to maintain and evolve LLM-powered systems

‍

It highlights Hybrid Mind’s strengths in LLM engineering, reusable asset development, and navigating complex infrastructure and compliance environments.

Сase Studies

Related Cases

Explore Cases
AI
Global Pharmaceuticals
Enterprise-Scale RAG
Estimated 30–40% reduction in time to find relevant information and generate reports.
AI
Manufacturing & Logistics
AI Platform Engineering
Increased test coverage (0% → 80%) dramatically improves system reliability.
AI
Life Sciences
AI Information Extraction / NLP
Ensured continued compliance with regulatory constraints on patient consent.
AI
Information Technology & Services
LLM Fine-Tuning, NLP, Domain Adaptation
Achieved an F1 score of 0.892 (~89%), significantly outperforming untuned LLM/RAG baselines.
AI
Global Biotech / R&D
Data Virtualisation, Platform Engineering
Significant operational efficiency through automated upgrades and restarts.
AI
Biotech
GPU Infrastructure, Observability, HPC Virtualisation
Reduced container startup from 10 seconds to under 1 second.
Explore Cases
Contact Us
Turning uncertainty into structure, and structure into growth
Contact Us