Access Anywhere
Every interface.
One model.
Native desktop apps, CLI, Python, and TypeScript — access your custom model however your team builds.
The complete LLM platform — fine-tune 100+ open-source models, build Knowledge Bases, align with RLHF, configure Guardrails, evaluate in the Playground, run Agents, and deploy Endpoints with deep Analytics.
Without fine-tuning, every model you deploy is a stranger to your business.
Public models were trained on the internet — not your business. 87% of inference drifts from domain truth without fine-tuning.
LLMs forget everything outside a context window. 1M+ tokens of institutional knowledge lost between sessions.
Your SOPs, products, and internal data live in your systems. 0% of your proprietary knowledge exists in the base weights.
23% hallucination rate on domain queries. Inconsistency and latency at scale. Base models are research artifacts, not infrastructure.
100+ open-source models.
Pick any base model. We handle quantization, adapter merging, and format conversion.
LoRA & QLoRA
Configurable rank, epochs, learning rate. No PyTorch.
Safety by default.
PII, profanity, regex rules, custom classifiers — enforced at inference.
One command.
Managed endpoint, private VPC, or export to GGUF / ONNX.
Align with human preference.
Annotate preference pairs. We train a reward model and run PPO automatically.
Here's some info about that.
Sure! The capital of France is Paris, known for the Eiffel Tower.
✓ preferredYour code has a bug.
On line 12, `arr[i]` should be `arr[i-1]` — off-by-one error.
✓ preferredCompare base vs. fine-tuned.
Run the same prompt through both models side-by-side before you ship.
The clause relates to liability...
Section 4.2 limits liability to direct damages, capped at 3× fees. Action needed: review indemnification clause before signing.
Run fine-tuned agents.
Deploy your model as an autonomous agent with tool use, memory, and daily run limits.
Measure what matters.
Run benchmark evals and compare your fine-tuned model against the base before shipping.
Your weights.
Your infra.
On-premise mode, private VPC deployment, zero telemetry on training data. Total isolation.
Langtrain combines the zero-ops convenience of Hosted APIs with the data privacy and weight ownership of building it yourself.
See how teams are using Langtrain to turn generic open-source models into specialized domain experts that drive actual business value.
Base models hallucinate refund policies and give vague, generic answers.
Fine-tune Llama 3 on 14K support tickets, add PII + profanity guardrails, deploy as a managed endpoint.
92% auto-resolution rate, zero hallucinated policies, avg. < 100ms response. Guardrails blocked 1,200 PII leaks in the first week.
Generic Copilot doesn't understand your proprietary monorepo architecture.
Fine-tune DeepSeek Coder on your GitHub repos, use the Playground to compare base vs. tuned before shipping.
40% fewer PR revision cycles. Engineers validated the improvement with side-by-side Playground runs before rollout.
Fine-tuned model is accurate but its tone and formatting don't meet your standards.
Annotate 2K preference pairs in the RLHF module. Langtrain trains the reward model and runs PPO automatically.
User satisfaction scores jumped 38%. Model now consistently follows house style without prompt engineering hacks.
No visibility into how your deployed models perform across different user cohorts over time.
Enable LangVision analytics on your endpoints to track latency, hallucination rate, and prompt/output quality.
Caught a dataset drift issue in week 3 before it reached 5% of users. Triggered retraining automatically.
Native desktop apps, CLI, Python, and TypeScript — access your custom model however your team builds.
Turn open-source models into production-grade domain intelligence — without giving up your weights, your data, or your autonomy.
