Enterprise LLM Fine-Tuning & Custom Models
Custom LLM models that respect your data and regulations. We provide LLM deployment services to fine tune large language models for your specific domain.
- Fine tuning LLM workflows on your clinical, scientific, or operational corpus.
- Reduce hallucinations with domain context, retrieval, and guardrails.
- Architect HIPAA-aware, audit-ready deployments on AWS, Azure, or your chosen platform.
- Ideal for LLM model customization and agentic workflows from day one.
Clinical QA Model
v0.3 • Production Ready↑ 34% vs baseline
↓ 67% reduction
Capabilities:
Generic Models Are Powerful. Alone, They're Not Enough.
Foundation models are remarkable, but they weren't trained on your domain or designed for your constraints.
Foundation models from OpenAI, Anthropic, and others are powerful general-purpose tools. But they come with real limitations for regulated industries:
- They don't know your specific clinical, R&D, or operational language.
- They can hallucinate confidently when pressed for domain-specific details.
- They can introduce compliance risk if used naively with PHI/PII.
In regulated industries, "just call an API" isn't a strategy. You need thoughtful patterns for data minimization, logging, approvals, and deployment.
Does this sound familiar?
We help you pick the right pattern—and then implement it in a governed way.
What "LLM Fine-Tuning & Custom Models" Means at Kriv AI
Not building foundation models from scratch. Specializing and governing them for your real-world environment.
Pattern Selection & Architecture
We guide you to the right approach for your use case: prompt engineering, retrieval-augmented generation (RAG), fine-tuning, or hybrids.
- Use case analysis and pattern selection
- Reference architecture design
- Stack-aware planning (AWS, Azure, Databricks, Snowflake)
Data & Corpus Preparation
We help you prepare, curate, and (where required) de-identify your corpus—clinical notes, SOPs, protocols, policies, research outputs.
- Corpus curation and quality assessment
- De-identification pipelines where needed
- Retrieval index and embedding setup
Model Training & Integration
We fine-tune or specialize models on your domain data. We integrate models into your apps, workflows, or agentic automations.
- Fine-tuning and prompt optimization
- Application and workflow integration
- MLOps and monitoring setup
We don't build foundation models from scratch; we specialize and govern them for your real-world environment.
Built on Leading LLM & Cloud Platforms
We work with commercial and open-source LLMs, deployed on cloud platforms where sensitive data stays in governed environments—and inference patterns match your risk appetite.
LLM
LLM
LLM
Open Source
Cloud
Cloud
Data
Data
Vector DB
Where Custom LLMs Deliver Real Value
Domain-specific models that solve real problems in regulated environments.
Clinical & Operational Q&A
LLMs that answer questions over internal clinical guidelines, SOPs, and policies—with citations and guardrails.
Pharma R&D Knowledge Assistants
Models tuned on your trial protocols, publications, and internal reports to support study design and evidence review.
Compliance & Policy Copilot
Assistants that help staff interpret internal policies and regulatory guidance, with curated citations and disclaimers.
Documentation Summarization & Drafting
LLMs that summarize visit notes, generate drafts, or pre-fill forms—with humans always in control.
Governance First, Not as an Afterthought
Every LLM we deploy is designed with compliance, auditability, and trust in mind.
Our Governance Practices
- PHI/PII minimization strategies and de-identification where possible.
- Access control and role-based permissions for model use.
- Logging of prompts, responses, and downstream actions for auditability.
- Alignment with frameworks like NIST AI RMF at a practical level.
- Clear ownership and escalation paths for model decisions.
What We Refuse to Do
- We don't build ungoverned 'shadow AI' systems.
- We don't encourage sending sensitive data to unvetted endpoints.
- We don't deploy models without clear owners and guardrails.
How a Custom LLM Project Runs
A structured approach from use case framing to production deployment.
Use Case & Risk Framing
Clarify the business goal, user journey, and risk appetite. Decide if this should be RAG, fine-tuning, or both.
Data & Architecture Design
Identify and prepare relevant corpus. Design deployment and access patterns (cloud/on-prem, private endpoints, etc.).
Build, Tune & Validate
Implement retrieval, fine-tuning, or system prompting. Test against real scenarios with domain experts. Validate quality vs risk.
Pilot & Productionization
Roll out to pilot users with monitoring and feedback loops. Integrate with MLOps / GaaS for continuous governance.
Outcomes You Can Expect from Custom LLMs
Move from playground experiments to governed, monitored, integrated model services.
Domain-Relevant Answers
Models that actually speak your clinical, scientific, or operational language—not generic responses.
Fewer Hallucinations, More Confidence
Use retrieval, fine-tuning, and guardrails to reduce nonsense and improve trust in model outputs.
Deployment You Can Defend
Architectures and documentation that your security, compliance, and legal teams can stand behind.
Ready for Agents & Automation
Models designed to feed into agentic workflows and automation from day one.
Before
Ad-hoc playground experiments
After
Governed, monitored, integrated model services
Project Shapes & Pricing
Most LLM projects begin as a scoped pilot around one or two use cases. We price based on complexity—data, integrations, governance—and team involvement, not just "API calls."
Whether you need a rapid proof-of-concept or a full production deployment, we structure engagements to deliver clear value at each phase.
Learn how we price engagementsCommon Questions
Quick answers about LLM fine-tuning and custom models.
Related Resources
Go deeper on LLMs, governance, and production deployment.
RAG vs Fine-Tuning: How to Choose for Clinical Applications
A practical guide to selecting the right LLM architecture for healthcare use cases.
View resourceHIPAA-Aware LLM Architectures: A Technical Playbook
Reference patterns for deploying LLMs in environments with PHI/PII constraints.
View resourceClinical Documentation Summarization with Human Review
How a regional health system uses LLMs to accelerate chart abstraction while maintaining quality.
View resourceHave a critical workflow that needs a smarter, safer model?
We'll help you design and deploy an LLM approach that respects your domain and your regulations. Start with a quick conversation—no commitment required.