Enterprise LLM fine-tuning & custom models
Custom language models that respect your data and your regulations.
We fine-tune and govern large language models for your clinical, scientific, or operational domain, reducing hallucinations with retrieval and guardrails, and architecting HIPAA-aware, audit-ready deployments on AWS, Azure, or your chosen platform. Built to plug into agentic workflows and MLOps from day one.
For healthcare, life sciences & regulated industries
v0.3 · production-ready
Precision vs baseline
↑ 34%
Hallucinations
↓ 67%
Capabilities
The reality
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.
They don't speak your domain
Foundation models from OpenAI, Anthropic, and others are powerful general-purpose tools, but they don't know your specific clinical, R&D, or operational language.
They hallucinate confidently
Pressed for domain-specific detail, generic models will produce fluent, plausible, and wrong answers, exactly the failure mode a regulated environment can't absorb.
Naive use creates compliance risk
"Just call an API" isn't a strategy when PHI/PII is involved. You need deliberate patterns for data minimization, logging, approvals, and deployment.
Does this sound familiar?
- Our LLM prototypes give great demos but break on real data.
- We can't explain or reproduce model behavior consistently.
- Compliance and security blocks our AI experiments.
- We don't know if we should fine-tune, do RAG, or both.
- Generic models don't understand our clinical/scientific language.
- We're worried about sending sensitive data to third-party APIs.
We help you pick the right pattern, and then implement it in a governed way.
What it means here
We don't build foundation models. We specialize and govern them.
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, then integrate them into your apps, workflows, or agentic automations.
- Fine-tuning and prompt optimization
- Application and workflow integration
- MLOps and monitoring setup
The stack
Built on leading LLM & cloud platforms.
We work with commercial and open-source LLMs, deployed where sensitive data stays in governed environments. Pick a layer, see what we plug into and how we keep it defensible.
Commercial foundation models
Hosted frontier models for the fastest path to value, wrapped in retrieval, guardrails, and logging so inference patterns match your risk appetite.
- OpenAI
- Anthropic
- Azure OpenAI
Where it pays off
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
Governance first.
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 the 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 it runs
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 whether this should be RAG, fine-tuning, or both.
Data & architecture design
Identify and prepare the relevant corpus. Design deployment and access patterns, cloud or on-prem, private endpoints, and more.
Build, tune & validate
Implement retrieval, fine-tuning, or system prompting. Test against real scenarios with domain experts. Validate quality against risk.
Pilot & productionization
Roll out to pilot users with monitoring and feedback loops. Integrate with MLOps / GaaS for continuous governance.
What you get
Outcomes you can expect from custom LLMs.
Move from playground experiments to governed, monitored, integrated model services.
↑ 34%
precision lift over baseline on a domain-tuned clinical QA model
↓ 67%
fewer hallucinations with retrieval, tuning, and guardrails
0
client data used to train models for anyone else, ever
Representative domain-tuning engagement, measured against the pre-tuning baseline.
- Domain-relevant answers, models that actually speak your clinical, scientific, or operational language, not generic responses.
- Fewer hallucinations, more confidence, retrieval, fine-tuning, and guardrails reduce nonsense and improve trust in model outputs.
- Deployment you can defend, architectures and documentation 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
Engagements
Project shapes & pricing.
We price 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 engagementsScoped pilot
Most LLM projects begin here, one or two use cases, proven end-to-end in a governed environment.
Rapid proof-of-concept
A fast, focused build to validate the pattern and the value before broader investment.
Production deployment
Full integration with MLOps, monitoring, and continuous governance for live use.
Straight answers
Common questions about LLM fine-tuning
Do you store or reuse our prompts and data?
Data handling depends on the architecture we design together. By default, we minimize sensitive data exposure. For fine-tuning, data may be used for training but remains in your governed environment. For RAG, data typically stays in your vector stores. We never use your data for training models for other clients.
Do you recommend fine-tuning or RAG for us?
It depends on your use case, data, and constraints. RAG is often faster to deploy and easier to update. Fine-tuning can improve domain fluency but requires more data and governance overhead. Many clients use a hybrid approach. We help you choose based on risk, cost, and performance, not one-size-fits-all.
Can we keep all data inside our own cloud?
Yes. We can architect solutions where data never leaves your governed cloud. This includes private endpoints, on-premises inference, and self-hosted models. We design for your security requirements.
Which models do you prefer: OpenAI, Anthropic, or open source?
We're model-agnostic. We choose based on your requirements: performance needs, cost constraints, data residency, and integration patterns. Commercial models offer convenience; open-source models offer control. We help you make the right trade-offs.
Can these models be safely used with PHI/PII?
PHI/PII use is handled cautiously. We employ encryption, role-based access, de-identification where possible, and audit logging. For some use cases, we design architectures that avoid PHI exposure entirely. We never recommend sending sensitive data to unvetted endpoints.
Go deeper
Related resources.
Go deeper on LLMs, governance, and production deployment.
Start here
Have a critical workflow that needs a smarter, safer model?
Bring your clinical, scientific, or operational use case to a 30-minute working session, we'll help you design and deploy an LLM approach that respects your domain and your regulations. No commitment required.
+1-732-433-5564 · info@kriv.ai · East Brunswick, NJ
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Flagship engagement, 2025, 2,000+ associates · 122 locations. From kickoff to independently productive engineers in 3 weeks.