Arjun Mehta
COO @ Quorivex Systems
We wanted to automate a few workflows but weren't sure where to start. Lampros helped us cut through the noise and get something useful into production quickly.
LLM Fine-Tuning & Custom Model Adaptation Development
Building LLM fine tuning and custom model adaptation systems that keep outputs reliable, consistent, and aligned with real product workflows in production
Why Fine-Tuned LLM Systems Fail at Scale
Unstructured Data Curation Pipelines
Messy data leads to inconsistent outputs and unreliable results
Incorrect Fine Tuning LLM Strategies
Poor tuning reduces accuracy and breaks how the model responds
Weak Evaluation and Benchmarking
Without real testing, results look correct but fail in actual use
Uncontrolled Training and Cost
Repeated training increases cost without improving outcomes
Model and Adapter Sprawl
Multiple models and versions create confusion and reduce control
Production Failure Under Real Usage
Edge cases break outputs and disrupt user workflows
From Broken Fine-Tuning to Reliable Production Systems
Solving these challenges with a system-first LLM fine tuning and custom model adaptation approach built for real usage
Create clean and structured data that improves output consistency
Align model behavior with real product workflows and use cases
Test outputs against real scenarios to reduce failures in production
Reduce cost by running focused and efficient training cycles
Keep models and versions organized as systems scale
Maintain stable performance with monitoring and control systems
Designing LLM Fine-Tuning Systems That Hold Up in Production
Approach focused on controlling model behavior in production, not just training performance
Start with data shaping to ensure outputs stay consistent from the beginning
Select fine tuning llm and custom model adaptation based on real failure risks
Validate performance against real workflows before deployment
Build monitoring and control systems early to prevent failures after launch
Hands-On With the Tools Powering Onchain Systems
AI & Machine Learning
AI development stacks including LLMs, RAG systems, and MLOps pipelines implemented in production.
OpenAI
Anthropic
LangChain
LlamaIndex
Pinecone
Hugging Face
PyTorch
MLflow
Web & Cloud Systems
Languages we build, optimize, and maintain in production.
Java
Node.js
Unity
Python
Ruby
PHP
Rust
C/C++
Docker
Kubernetes
Mobile & Product Interfaces
Mobile applications engineered for reliability and user experience.
iOS
Android
Flutter
React Native
Xamarin
Swift
Blockchain Infrastructure
Onchain infrastructure architected for security and scalability.
Ethereum
Arbitrum
Optimism
Base
Solidity
Foundry
Hardhat
OpenZeppelin
The Graph
Alchemy
Your AI-Native
A focused engineering partner for teams that value speed and architectural discipline.
AI-First Development Partner
Move Faster. Build Smarter.
AI-enhanced workflows automate testing, optimize infra, and accelerate shipping, without compromising security or stability.
Speed to Market
Ship With Confidence.
Structured sprint execution and senior-led ownership move features from roadmap to production with fewer delays and rework.
Outcome-Led Ownership
Beyond Ticket Completion.
Engineering decisions align with product goals, system health, and measurable outcomes, not just task completion.
Strategic Partnership
Built For Long-Term Scale.
Architecture and implementation choices are made with future scale, performance, and maintainability in mind from the start.
Other Blogs
FAQs
How do you make sure the fine-tuned model doesn't break in production?
We treat production reliability as a design constraint, not an afterthought. That means structured data pipelines aligned to real usage, evaluation frameworks built around actual user workflows, and monitoring systems that track model behavior continuously, not just at deployment. We don't hand off a model; we hand off a system.
How do you prevent the model from losing general reasoning after fine-tuning?
Catastrophic forgetting is a real risk with aggressive fine-tuning. We use controlled adaptation methods, including PEFT and LoRA, that update only the parameters relevant to your domain, and we validate outputs across both domain-specific and general reasoning tasks before signing off on any training run.
How do you measure whether LLM fine-tuning is actually improving our product?
We measure improvement using product-level metrics, not model scores. Track response accuracy, consistency, task completion rate, and failure frequency across real user workflows. And compare these metrics before and after LLM fine-tuning to identify changes in output quality and reliability. Continuously evaluate performance under real usage to ensure improvements hold beyond testing.
Can you handle multiple models or customizations across different customers or use cases?
We define success using your product, not generic benchmarks. Before training begins, we establish baseline metrics tied to your workflows: response accuracy, output consistency, task completion rate, and failure frequency. We track these before and after fine-tuning so improvement is measurable, not assumed.
Can you manage fine-tuning across multiple use cases, models, or customer tenants?
Yes. We build systems designed for multi-model and multi-tenant environments, managing model versions, LoRA adapters, and tenant-specific customizations in a structured way that scales without creating operational debt.
