Spell – Turn Your Enterprise Data into a Private LLM
Use your existing datasets to build, evaluate and deploy Large Language Models entirely inside your own environment.
- Use existing enterprise datasets to build domain-specific LLMs
- Run training, evaluation and serving fully on-premise
- Governance, access control and auditability built in
Representative dashboard view. Actual interface may vary.
From Raw Enterprise Data to a Private LLM
Most organizations already sit on years of tickets, documents, wikis and logs – but struggle to turn them into a reliable, secure LLM. Spell is the bridge: it transforms your existing data into curated training sets and orchestrates end-to-end model workflows inside your infrastructure.
Ingest and prepare data
Fine-tune and evaluate models
Deploy on-premise endpoints
Monitor and improve over time
Secure and govern access
Integrate with existing tools
Use the Data You Already Have
Spell connects to the systems you use every day and turns scattered information into training-ready datasets.
- Documents and knowledge bases (wikis, intranet sites, manuals)
- Ticketing and support systems (issues, requests, resolutions)
- Code repositories and engineering documentation
- Structured records from business systems and logs
- Configurable pipelines for cleaning, filtering and labeling data
- Training materials and documentation archives
- Configuration files and deployment scripts
A Full LLM Lifecycle in One Place
Spell provides a structured lifecycle for building and maintaining enterprise LLMs – from data preparation to deployment and iteration.
Ingest
Connect to internal data sources and synchronize relevant content.
Prepare
Clean, filter and transform data into training and evaluation sets.
Train
Fine-tune or adapt LLMs to your domain-specific data and tasks.
Evaluate
Measure quality, safety and relevance before models reach production.
Deploy
Expose private LLM endpoints inside your infrastructure for apps and tools.
Improve
Monitor usage, collect feedback and update models over time.
On-Premise by Design, with Governance Built In
Spell is architected for organizations that cannot send sensitive data to external AI services. All training, evaluation and inference run on your infrastructure.
Evaluate Before You Trust
A private LLM is only useful if you understand its behavior. Spell gives you evaluation tools to measure quality, safety and alignment with your policies.
Evaluation Panel
Deploy LLMs Where Your Systems Already Live
Spell exposes private LLM endpoints inside your infrastructure so your applications, tools and workflows can use them without leaving your network.
On-premise APIs for chat, completion and task-specific flows.
Integration patterns for internal portals, bots and workflows.
Support for multiple environments (dev, staging, production).
Versioned deployments and safe rollout strategies.
Monitor Usage, Drift and Model Health
After deployment, Spell helps you keep track of how your models behave over time.
2.4M
Monthly requests
45ms
Median latency
92%
User satisfaction
Spell in Your Organization
Support and Service Teams
Train assistants on your historical tickets and knowledge base to resolve issues faster and more consistently.
- Faster responses with context from your own documentation.
- Reduction in repetitive questions handled by human agents.
- Consistent answers aligned with your policies.
Engineering and IT
Build LLMs tuned to your code, infrastructure and runbooks to help teams understand complex systems more quickly.
- Explain code and architecture using your own repositories.
- Suggest fixes based on internal patterns and standards.
- Guide incident response with knowledge from past outages.
Business and Operations Teams
Create copilots that understand your processes, terminology and records.
- Answer process questions in the language of your organization.
- Summarize long documents into actionable briefs.
- Support decision-making with context from internal data.
High-Level Architecture
Data Sources
Data sources: document repositories, ticketing tools, code repos, logs and business systems.
Spell Pipeline
Spell workflow: ingestion, preparation, training, evaluation and deployment components.
Serving Layer
Serving layer: on-premise LLM endpoints and integration points for internal applications.
In Active Development
Spell is being developed in close collaboration with early adopters who need private, governed LLMs on top of their own data.
Data Ingestion and Preparation
Connectors and pipelines to turn raw enterprise data into training-ready datasets.
Training and Evaluation
Workflows for fine-tuning models and measuring quality against real scenarios.
Deployment and Integration
On-premise endpoints, SDKs and integrations into existing tools.
Continuous Improvement and Governance
Deeper monitoring, policy controls and automation for retraining.
Ready to Build a Private LLM on Your Own Data?
If your organization needs LLM capabilities but cannot send sensitive data to external services, Spell gives you a way forward. Start with a focused pilot on one dataset and expand as you gain confidence.