Train private LLMs on your knowledge

How it works

From raw inputs to production models in four steps.

Hover a card for a preview, or click to jump straight into the guided tour.

The journey

From raw knowledge to private inference

Four chapters — tap a step or watch the tour. Each stage maps to real screens in your workspace.

Step 1 of 4

Ingest & organize

Upload PDF, PPTX, or TXT, or paste text in the builder. Edit the source, then generate Q&A or multi-turn chat rows your team can review before training.

Upload PDF, PowerPoint, or plain text
Paste or edit source text in the builder
QA, chat, or raw text · optional group scope

Workspace preview

Private LLM training, without the sprawl

One workspace to ingest documents or pasted text, shape QA and chat datasets, run training jobs, evaluate checkpoints, and roll out — with lineage and access controls so your data stays in your boundary.

Curate training data

Upload PDF, PowerPoint, or text files; paste or edit source text; then generate QA or chat-style rows to review with your team. Datasets can be scoped to a group.

Fine-tune private models

Run adapters and checkpoints on your stack — you choose what goes in, and what ships out.

Your models, your infrastructure

Weights and artifacts stay in your boundary. No need to paste sensitive docs into public APIs.

Model lineage

See which job produced which checkpoint, compare runs, and hand off models without guesswork.

Anyone can be a knowledge trainer

Groups, roles, and shared workspaces so experts contribute data and feedback without becoming ML engineers.

Evaluation & tiers

N-gram scores against your dataset references on every plan; paid tiers unlock LLM-as-judge ratings and side-by-side reference vs fine-tuned output before you ship.

Audit-ready operations

API keys, training workers, and activity trails structured for teams that need to show their work.

Interview content

Why enterprises move beyond generic LLM chat

Hallucinations, legal exposure, and one-size-fits-all responses are costly in enterprise settings. Firms and hospitals need customized models with policy controls, traceability, and domain alignment.

Reliability

Hallucinations break trust in high-stakes workflows

Confident but incorrect outputs create risk in legal, financial, and clinical contexts
Citation gaps make answers hard to verify during audits or case reviews
Teams need controlled evals and domain-grounded data before rollout

Legal & Compliance

Public endpoints can violate policy and retention requirements

Unclear data retention and third-party processing create compliance uncertainty
Sensitive prompts may cross boundaries required by contracts or regulation
Enterprise teams need scoped access, audit trails, and enforceable governance

Enterprise Customization

Firms and hospitals need domain-specific models, not generic chat

Policy, terminology, and workflow needs vary by organization and department
Generic assistants rarely match internal standards for legal or clinical operations
Private fine-tuning and controlled deployment align models to real business practice

Ready to train on your own terms?

Create an account, invite your team, and open the dataset builder — upload files, paste text, or generate training examples — no credit card required to explore the workspace.