Custom LLM Fine-Tuning Services
Generic models guess when your domain has exact answers. LLM fine-tuning services retrain a base model on a company's own records, so it responds with real precision. Kodexo Labs adapted models on 160,000 technical records for Diesel Laptops, cutting their lookup time 85%.
Send us a brief
TRUSTED BY ENTERPRISES
Your support team answers the same niche questions daily, and off-the-shelf models still miss the context. We train models on your own data, so each one learns your terminology, catalog, and rare cases.
Core Capabilities
Supervised fine-tuning on your labeled prompt and response pairs
Preference alignment with RLHF and DPO for reliable outputs
LoRA and QLoRA adapters for faster, lower-compute training runs
Dataset curation, labeling, and synthetic data from your records
Held-out evaluation harnesses that measure accuracy before release
Production serving inside your own cloud with drift monitoring
IN THE NEWS
AI products
AI Development Company
Expert team
Client retention
What Our Custom Fine-Tuning Practice Covers
Every project starts with your data, not a generic template. We match the method to your accuracy targets, latency limits, and compute ceilings. Some clients need full retraining, others need a light adapter. We handle both.
Supervised fine-tuning (SFT)
We instruction-tune a base model on your labeled prompt and response pairs, so it answers in your format, tone, and domain every time.
We turn raw tickets into clean prompt and response pairs for each key task.
We lock the model onto your own reply format across every live user query.
Not Sure Which Method Fits Your Data?
Not sure whether full retraining or a light adapter fits your data? Bring us your accuracy targets and your records, and we will map the fastest path to a model that performs.
Results From Real Deployments
Diesel Laptops
Mechanics were digging through manuals to match parts, losing time on every job. We fine-tuned base models against 160,000 technical records, covering catalog and edge cases. Lookup time fell 85%, and the model runs inside their own AWS VPC for full data residency. This Inc. 5000 firm now answers part queries in just seconds.
85%
Faster Lookup
160,000
Records Searched
AWS VPC
Self-Hosted
SmartMedHx
Every clinician here burned nearly an hour daily writing visit notes by hand. We built a HIPAA-compliant system that listens to each patient interview and drafts the note for review. Today 42+ providers rely on it, and it has already processed 493 patient interviews. The patent-pending model keeps doctors focused on care, not paperwork.
42+
Providers
493
Patient Interviews
HIPAA
Compliant
Extensiv
At Extensiv, every data question meant a ticket to engineering and a long wait. We built an agentic system on LangGraph that reads plain-English questions and answers them straight from the live operational database. It hits 90%+ accuracy across 207 tables and 4 databases. This Inc. 5000, Hg-backed company now self-serves those answers instantly.
207
Tables
4
Databases
90%+
Accuracy
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- HIPAA-safe model tuningClinical vocabulary adaptationPatient-note domain modelsSelf-hosted model deployment
Where Does Custom Model Fine-Tuning Deliver the Clearest Returns?
We have shipped domain-adapted models across 25+ industries since 2021, and the pattern holds everywhere. Each vertical carries its own vocabulary, edge cases, and record formats. Below are eight where domain adaptation pays off fastest.
Ready to Fine-Tune a Model Trained on Your Own Data?
Tell us about your records, your edge cases, and where accuracy breaks down today. We will map a training approach to your domain and show you where measurable gains appear first.
How Does Our Fine-Tuning Pipeline Support Strict Regulatory Frameworks?
We build our training pipeline and deployment architecture to support the frameworks regulated teams answer to. When data cannot leave your walls, we deploy models inside your own AWS VPC, so records never transit a third party. Checkpoint audits track every training run.
SOC TYPE 2
HIPAA
GDPR
PCI-DSS
ISO 42001
NIST AI RMF
COPPA
ISO 27001
CCPA
FERPA
SOC TYPE 2
HIPAA
GDPR
PCI-DSS
ISO 42001
NIST AI RMF
COPPA
ISO 27001
CCPA
FERPA
Why Regulated Teams Choose Kodexo Labs to Fine-Tune and Ship Models to Production
Fine-Tuned Models That Ship
A demo that works once proves little. Our agentic system for Extensiv holds 90%+ accuracy over 207 tables and 4 databases. We test against production load before we ship, not a staged walkthrough.
Scored Before It Ships
A model can look fluent and still drift. We score each one on held-out benchmarks, tracking perplexity, ROUGE, BLEU, and F1. Regressions show up in our test runs, not in a support ticket.
Self-Hosted Inside Your VPC
Some data cannot sit on servers you do not own. For Diesel Laptops, we ran a model tuned on 160,000 technical records inside their own AWS VPC. Nothing ever left their own infrastructure.
LoRA Adapters by Default
A full retrain is rarely the right first move. We default to LoRA and QLoRA adapters, which update a small slice of a model's weights. Faster cycles, less compute, the same domain gains.
Ready to Scope Your Fine-Tuning Project?
Not sure which training method fits your data, your accuracy targets, or your compliance rules? Bring us the problem and we will map a training approach in one working session. No prep required, just your goals.
Awards and Recognition
Independent review platforms rank our work against thousands of firms worldwide. These placements reflect verified client feedback and delivery record, not paid positioning. Each badge below comes from earned standing.
Overcoming Common Custom LLM Fine-Tuning Challenges
Fine-tuning a large language model is rarely a straight line. Teams hit predictable obstacles that erode accuracy, waste compute, and delay launch. We have run enough training jobs to name these traps, and we build every engagement to sidestep them.
The Fine-Tuning Stack We Run Daily
These are the training frameworks, base models, and serving tools we reach for on model-training engagements weekly.
How We Deliver Fine-Tuning in Weekly Sprints
Discovery & Data Audit
We map your data sources, current model gaps, and target accuracy. Together we agree on what success looks like and which tasks the adapted model must handle. This shared baseline anchors every decision that follows.
Data Prep & Method Selection
We curate and clean the dataset, then choose the method. SFT, LoRA, QLoRA, RLHF, or DPO all get weighed against your data volume and compute constraints, so the approach fits your reality. You approve the plan before we commit compute.
Fine-Tuning & Experimentation
We run training jobs and compare candidate checkpoints side by side. Hyperparameters get adjusted between sprints, and every experiment is tracked so we can trace exactly which change moved which metric. Nothing is left to guesswork.
Evaluation & Benchmarking
We score every checkpoint against held-out data using named eval harnesses, not gut feeling. Domain accuracy and general capability are measured together, so a win in one area never hides a quiet regression elsewhere.
Deployment & Monitoring
We ship the model to production, add drift monitoring, and hand a clean runbook to your team. You keep full control of the weights, and we stay available for retraining as your data shifts.
Related Insights
Reactive vs. Proactive AI Agents: What’s the Difference?
August 2025 · By Kodexo Labs
Explore the differences between reactive and proactive AI agents, their decision-making processes, business applications, and implementation strategies. Learn how reactive AI excels in real-time responses (e.g., customer service chatbots) and proactive AI drives long-term value through predictive analytics (e.g., predictive maintenance), with hybrid approaches delivering 40% better performance.
AI in Adaptive Learning: Benefits, Challenges, and Best Practices for 2024
November 2024 · By Kodexo Labs
A practical guide to AI in adaptive learning, covering benefits, challenges, platforms, ROI, and best practices for personalized education in 2024.
How the Future of AI Agents Will Power Businesses and Industries
October 2025 · By Kodexo Labs
Discover how AI agents are transforming business operations and industries in 2025 through autonomous decision-making, enhanced customer experiences, and optimized workflows. This guide explores agentic AI applications, implementation strategies, and industry-specific impacts for finance, healthcare, manufacturing, and retail.
Frequently Asked Questions
Retraining a base model on your data to sharpen domain performance.