A language model that knows your business.
Fine-tune a foundation model on your domain knowledge, data, and terminology. The result is a model that writes and reasons like your subject matter experts.
Domain-specific training. 6 to 12 week delivery. Enterprise deployment included.
When Fine-Tuning Is the Right Choice
Most AI applications start with a general-purpose model. Fine-tuning makes sense when the task is domain-specific, when accuracy on proprietary terminology matters, or when the default model behavior does not match what your users expect.
Domain Specificity
Your field uses technical language, internal acronyms, or specialized knowledge that general models get wrong.
Accuracy Requirements
The output must meet a higher bar than a general model can achieve with prompting alone.
Cost and Latency
A fine-tuned smaller model can match a larger model quality at lower cost and faster response time.
Our Fine-Tuning Process
Use Case Definition
Define what the model will do, what good output looks like, and how we will measure success.
Data Curation
Collect, clean, and format training data from your documents, records, and domain experts.
Model Selection
Choose the right base model for your task: GPT, Llama, Mistral, or a domain-specialized foundation model.
Training and Evaluation
Fine-tune on your data. Evaluate against your success criteria. Iterate until the bar is met.
Deployment
Deploy to your infrastructure: cloud, on-premises, or via API. Integrate with your existing applications.
What We Fine-Tune For
Document Classification
Route documents, emails, and records into the right category automatically.
Named Entity Extraction
Extract structured data from unstructured text: names, codes, dates, and custom entities.
Q&A on Internal Knowledge
Answer employee or customer questions from your internal documents and knowledge base.
Content Generation
Generate on-brand copy, reports, and summaries in your company's exact voice.
Code Generation
Build a model that writes code in your internal frameworks and coding standards.
Summarization
Condense lengthy documents into structured summaries in the format your team needs.
Supported Models
Open Source
Llama, Mistral, Falcon, and other open-weight models. Deploy anywhere with no per-token licensing.
Commercial
GPT-4o, Claude, and Gemini fine-tuning via official APIs. Managed infrastructure included.
Small Language Models
SLMs for edge deployment or cost-sensitive use cases where a smaller, faster model is the right fit.
Real outcomes from real clients
Not projections. Actual results from businesses that transformed with Captivix.
Employee Advocacy & MarTech
Xmplifi's AI-Driven Platform Achieves 75% Boost in User Engagement
A leading employee advocacy platform was revolutionized with AI-driven personalization, cloud migration, and modern tech stack upgrade by Captivix.
“When we partnered with Captivix for our digital transformation, we knew we were setting high expectations. They not only met these expectations—they surpassed them. The due diligence and custom development work offered by Captivix has been nothing short of revolutionary for Xmplifi.”
In their words
When we partnered with Captivix for our digital transformation, we knew we were setting high expectations. They not only met these expectations—they surpassed them. The due diligence and custom development work offered by Captivix has been nothing short of revolutionary for Xmplifi.
Captivix exemplified the highest level of professionalism. They helped me find the best path forward.
The Captivix team played a crucial role in optimizing Navistar's pricing systems. His innovative approach to IT and pricing automation streamlined operations and significantly enhanced our sales and profitability.
I would suggest that Captivix's is the same and we found this to be one of its biggest strengths. At no point during the process did we feel isolated. Our relationship has been and continues to be, one of collaborative effort.
Common questions about fine-tuning
How much data do we need?
It depends on the task. Classification tasks can work with a few hundred examples. Complex generation tasks may need thousands. We assess your data during scoping and tell you honestly whether fine-tuning is viable.
How do we protect our proprietary data during training?
Training happens in your cloud environment or ours under NDA. Your data never leaves the agreed boundary, and the resulting model weights belong to you.
Can we fine-tune on top of OpenAI models?
Yes. OpenAI offers a fine-tuning API for GPT-4o and other models. We use it when it is the right fit. We also work with open-weight models when you need more control over the training environment.
How long does training take?
Typically 6 to 12 weeks from scope sign-off to a deployed model. Data preparation is often the longest phase.
How do we know when the model is good enough?
We define success metrics before training starts. Common metrics include accuracy on a held-out test set, human evaluation scores, and task completion rate. We do not ship until the agreed bar is met.
Ready to build a model on your domain?
Fine-tuning starts with a data and use case assessment. Book a call and we will tell you whether your project is a good fit.