Train reasoning once
Keep the capable base model frozen. Translation training happens in the target-language layer, so language adaptation does not rewrite the model's reasoning behavior.
REASONING-FIRST LANGUAGE AI
LANGBRAINS AI
Reasoning Intelligence · Native Language
Intelligence, expressed through language.
Vajram pairs reasoning- and tool-capable models with SLIM to deliver native-language answers without retraining the source intelligence.
Vajram carries a model's committed answer states directly into a native-language decoder—preserving reasoning and tool context without a conventional translation cascade.
Source-Latent Inline Memory architectureReasoning-aware
Native-language output
Tool-call context
No completed-answer re-forward
Exact-value protection
Frozen source model
Reasoning-aware
Native-language output
Tool-call context
No completed-answer re-forward
Exact-value protection
Frozen source model
BENEFITS
One model. More languages.
Separate reasoning capability from language delivery, then adapt the output layer for the language people actually use.
Remove a translation pass
SLIM consumes hidden states already produced during generation. The completed English answer is not sent through the source model or a separate translation encoder again.
Extend language by language
Add a tokenizer and decoder for each target language while retaining the same source intelligence—starting with native Kannada in Vajram KN-1.
FEATURES
Built around the answer
A focused architecture for preserving capable model behavior while delivering native-language output.
Cutting-Edge AI
Pair modern reasoning and tool-capable models with dedicated target-language decoders through SLIM's inline memory boundary.
Reasoning-Aware Output
Translate only the committed response while keeping the answer conditioned on the source model's reasoning and conversation context.
Tool-Ready Architecture
Keep internal reasoning and tool traffic out of the translated answer while carrying tool-informed context into the final response.
AI-Powered Support
Build native-language assistants on a provider-neutral chat interface with streaming responses and local conversation history in the preview.
CAPABILITIES
What we're building
A focused language-intelligence platform, with every capability marked by its current product stage.
CORE PLATFORM
Reasoning-Model Translation
Connect capable source models to target-language decoders so their committed answers can be delivered in Kannada—and extended to additional languages with dedicated language layers.
PREVIEW
Kannada Chat Assistants
Create streaming conversational experiences that preserve source-model reasoning and tool context while returning user-facing Kannada answers.
IN DEVELOPMENT
Grounded Kannada Content
Generate explanations, summaries, and structured content in Kannada from the same source intelligence, with exact-value protection for numbers and technical spans.
FUTURE ROADMAP
Indic Speech Interfaces
Text-to-speech and speech-to-text support for Indic languages is planned as a future interface layer; it is not part of the current preview.
ARCHITECTURE
Simple & Scalable
Keep intelligence and language adaptation separate, with explicit evaluation at every boundary.
01
Choose the source intelligence
Start with a capable reasoning model. Its core stays frozen, so target-language training does not alter how it reasons or uses tools.
02
Adapt the language layer
Train a tokenizer, bridge, and decoder for the target language using aligned data—Kannada first for Vajram KN-1.
03
Evaluate and improve
Measure translation quality, grounding, exact-value fidelity, latency, memory use, and multi-turn behavior before packaging a release.
BENCHMARKS
Measured SLIM v6 results
Inline typed latent copy compared with a conventional translation cascade on the same local benchmark.
lower median translation-added latency
173.82 ms vs 276.96 ms
less peak allocated VRAM
1.756 GiB vs 2.962 GiB
less estimated translation work
55.46 vs 225.45 median GFLOPs
Kannada decode throughput
128.08 vs 59.70 tokens/second
Development benchmark: 10 fixed prompts, batch size 1, greedy decoding, one warm-up, AMD Radeon RX 7900 XT. These figures are architecture measurements, not production latency guarantees. The v6 report calls for larger ordinary and copy-heavy sets before release qualification.
ACCESS
Pricing is coming later
Vajram KN-1 is still in preview. Public hosting and commercial plans will be announced only after deployment and release testing are ready.
COMING SOON
No paid plan is available yet
The current website is a product preview. You can explore the chat experience while hosted Vajram access remains in development.
FOUNDER
Built with a singular focus
LangBrainsAI is currently founder-led. Team and social links will be added as the project grows.
LANGBRAINS AI
Abhijith KJ
Founder & CEO
Building Vajram KN-1 and the SLIM architecture for reasoning-aware, native-language AI—starting with Kannada.
FAQ
A few useful answers
The essentials for the current Vajram KN-1 preview.
Vajram KN-1 is LangBrainsAI's Kannada-focused language model project. It combines a capable reasoning and tool-using source model with SLIM, a dedicated architecture for producing native Kannada answers.