Build domain-specific AI systems using BharatLM as the foundation — adapted to your industry's knowledge, terminology, workflows, tools and business requirements.
The objective isn't to make a model know everything. It's to make the model highly useful for what matters to your organization.
Broad knowledge, generic behavior
Industry knowledge, controlled behavior
BharatLM is designed as a model foundation that can be specialized for targeted knowledge domains and application workflows — not a finished chatbot, but the base you build from.
The BharatLM foundation — an adaptable model base for domain AI.
Industry-specific documents and approved datasets for your field.
Fine-tuning, instruction tuning or domain adaptation — as appropriate.
RAG and retrieval over your company knowledge and documents.
APIs, databases, calculators and enterprise systems the AI can use.
A business AI assistant, agent or workflow your teams actually use.
Every capability is engineered for your use case — "can be adapted", "can be specialized", "can be deployed". Nothing is automatic without the right architecture and data.
Six reasons organizations move from general-purpose AI to AI that is specialized for their field.
Focus the model around the terminology and knowledge that matter to the domain.
Control which knowledge sources and business rules influence responses.
Smaller active architectures can support efficient inference and serving.
Standardize model behavior for repeatable business workflows.
Connect AI directly to enterprise tools and systems.
Optimize for specific tasks instead of trying to solve everything.
BharatLM is built for efficient domain-specific deployments — lower inference footprint, lower serving cost, and deeper specialization. Every capability below is available in the current release.
Sparse expert routing — only selected experts are active per token. 6B total parameters, 1.6B active (top-2 of 8 experts).
1M-token context window using LongRoPE2 positional encoding for long-document and multi-turn workloads.
Support for 22+ Indian languages and scripts with Indian-language optimization built into the tokenizer and generation pipeline.
Function calling, tool schemas, parallel tool calls and agentic loops for production application integration.
Grammar-constrained JSON output with schema enforcement — output your software can trust and consume.
PRM, MCTS and test-time compute mechanisms for multi-step reasoning and complex problem solving.
1,000+ tok/sec on A100 hardware with a speed stack (diffusion, adaptive denoising, MoD, Flash Attention, PagedAttention, AWQ).
100% Indian ownership and IP, with a model built from zero and open weights.
The architecture combines Discrete Diffusion, Mixture-of-Experts, Multi-head Latent Attention and Infini-Attention. Click a stage to see what happens there.
The user request or system prompt enters the pipeline — a question, a document, or a task from an application.
Not every domain problem needs a new base model. The right architecture may combine model adaptation, retrieval, tools and business logic.
Approved industry documents and datasets.
Shape behavior, style and format for the domain.
Where appropriate — not always the right answer.
Ground answers in current, citable knowledge.
Domain language the model actually understands.
Constraints and policies the model respects.
APIs and systems the AI can act through.
Domain benchmark that proves it works.
A retrieval pipeline ingests your source systems, processes and indexes them, and grounds BharatLM so answers come from your business — not the open internet.
Retrieval-augmented generation grounds every answer in your data.
Find relevant context by meaning, not just keywords.
Chunk, embed and index documents for fast retrieval.
Bring PDFs, policies and manuals into the pipeline.
Access-controlled retrieval where implemented for your systems.
BharatLM includes OpenAI-compatible function calling with JSON-schema tool definitions and up to 8 parallel tool calls — so it can read and act through your systems.
Applications need reliable, machine-readable output. BharatLM includes grammar-constrained JSON with schema enforcement — schema-constrained outputs can be implemented and validated for your schemas.
Useful to a human. Hard for software to act on reliably.
{
"customer_intent": "replacement_or_refund",
"priority": "high",
"recommended_action": "check_order_status",
"next_step": "offer_replacement"
}
Deterministic fields your application can branch on.
BharatLM uses a ReAct + Toolformer-style agent loop with parallel tool execution — the model reasons, uses tools, observes results and iterates toward a structured response.
Picks the right tool for each step of the task.
Runs tools, including parallel calls where safe.
Feeds results back into the reasoning loop.
Re-plans as new information arrives.
Ends with output your software can consume.
The tokenizer uses a 64K vocabulary designed for Devanagari, math and 22+ Indian scripts, with Hindi tokenization substantially more efficient than the cited LLaMA comparison. An Indic-morpheme-aware diffusion noise schedule optimizes Indian-language generation.
Scoped to the languages your users actually need.
Devanagari and 22+ Indian scripts in the tokenizer design.
64K vocabulary, efficient Hindi tokenization.
Local terminology and regional-language applications.
Languages listed are supported in the current release. Production support is scoped to the languages your deployment requires.
BharatLM ships with an OpenAI-compatible API with streaming, authentication and India-based processing. We design the deployment around your data, security and control requirements.
Fast integration through an OpenAI-compatible API — streaming, authentication and India-based processing. The quickest path from pilot to production.
Dedicated infrastructure for your organization — isolated model serving inside your own cloud account, with your security and compliance controls.
For organizations requiring controlled infrastructure — the model and serving stack run inside your own data center, with your data never leaving your boundary.
For selected use cases where model size and hardware requirements permit — compact deployments embedded in devices or local applications.
For organizations with strict data, privacy or infrastructure requirements, BharatLM can be positioned as a foundation for controlled AI deployments.
Greater control over where and how the model runs.
Options to keep data within your boundary.
Less reliance on external model providers.
Governance designed around your policies.
Specialized to your industry's context.
We do not make automatic legal or compliance guarantees. Compliance claims (for example DPDP) are made only after legal and technical validation for your specific deployment.
Potential implementation areas — each would be scoped, evaluated and built for your specific context. These are use cases, not existing deployments.
Potential applications only — no diagnostic accuracy or regulatory approval is claimed.
Model output is not regulated financial advice.
A workflow tool — not a substitute for legal advice or certification.
Recommended procedures go through human approval before execution.
The domain most directly supported by BharatLM — Indian academic tools including NCERT lookup, PYQ search, syllabus checking, exam calendar and study planning.
These are implementation opportunities — government adoption is scoped per engagement.
Potential applications — claims decisions remain with humans and your underwriting rules.
Need an e-commerce AI assistant? E-commerce Development
A flagship reference architecture — an Insurance Claims AI Assistant. Watch the pipeline run end-to-end, or click any stage to inspect it.
A customer files a claim — by phone, portal or email. The system captures the claim and the documents that support it.
This is an illustrative reference architecture — not a live deployment. Alternative examples (Healthcare, Legal, Manufacturing, Education) follow the same pattern.
Enterprise credibility comes from measurement. We build a domain benchmark on your real data and run a continuous improvement loop. BharatBench measures speed, quality and consistency metrics.
A conceptual benchmark framework for your domain — with room for future custom evaluation suites built on your data and success criteria.
Illustrative scores — your benchmark is measured on your data.People stay in control of the actions that matter.
Measured on your real tasks, continuously.
Controlled, governed data pipelines.
Scoped where implemented for your systems.
Logged, traceable AI decisions.
Quality, drift and cost watched in production.
A complete production architecture — the model is one layer in a system with orchestration, knowledge, tools, memory and controls.
BharatLM connects to the systems you already run — through clean APIs and connectors — so the AI works where your business already works.
Eight steps from discovery to a continuously improving production system.
Understand the business problem and the domain.
Assess current data, workflows and systems.
Identify AI use cases and success criteria.
Clean, structure and index domain information.
Fine-tune, adapt or retrieve — as appropriate.
Connect tools, APIs and business systems.
Test domain accuracy, safety and performance.
Launch, monitor and continuously improve.
Patterns we adapt to your real systems — conceptual reference implementations, not real client deployments.
These are conceptual reference architectures — patterns we adapt to your systems, not real client deployments.
Three levels of value — from the model foundation to a complete business AI system. Every engagement is a custom quote.
The BharatLM foundation, configured for your domain.
Specialized with your knowledge, tools and evaluation.
Integrated, governed and running in production.
Understand what domain AI can do for your business — and where to start.
One focused use case on your real data — measured against your success criteria.
A production-grade domain AI system — integrated, evaluated and governed.
Controlled deployment for organizations with strict data and infrastructure requirements.
Domain AI is not just a model — it is data, software, integration and governance. We build all of it.
Foundation models, specialization and inference — engineered, not assembled.
We adapt the model to your industry's terminology, rules and workflows.
Production-grade applications, APIs and integrations around the model.
Tool-using agents that plan, act and iterate — with guardrails.
Schema-constrained outputs your software can trust and consume.
Indic-aware tokenization and language support built into the foundation.
Cloud, private cloud, on-premise — deployed where your data needs it.
Monitoring, evaluation and improvement after launch — not just at launch.
Straight answers about what BharatLM is, what it can do, and how we build with it.
BharatLM is Kirikaa Digital's proprietary AI model foundation. It is designed as an adaptable base that can be specialized for a specific industry or business domain — combined with your knowledge, tools and workflows — to build domain-specific AI systems. It is a foundation, not a finished chatbot.
A domain-specific LLM is an AI model that has been adapted — through domain data, instruction tuning, retrieval (RAG), tools and evaluation — to work well in one specific field such as healthcare, finance or legal. Instead of knowing a little about everything, it is highly useful for what matters in that domain.
A general-purpose LLM has broad knowledge and generic behavior. A BharatLM-powered domain AI is built around your industry's terminology, your company's knowledge, your business rules and your tools — with domain-specific evaluation. The objective is usefulness in your domain, not general knowledge.
Yes. That is the core design intent. We specialize the foundation around your domain data, terminology and workflows — using the right mix of model adaptation, retrieval-augmented generation, tools and business logic for your use case.
Depending on the problem, we may fine-tune or instruction-tune the model on approved domain data, and/or ground it with retrieval over your company knowledge. We choose the approach that fits your data, goals and budget — fine-tuning alone is not always the right answer.
Often both. RAG grounds the model in your current documents and keeps knowledge up to date; fine-tuning shapes behavior, style and format. Many production domain AI systems combine model adaptation, retrieval, tools and business logic.
Yes. Through clean API and database integrations, the model can read from your databases and systems as tools — with scoped permissions, policy checks and human approval gates where the action is sensitive.
Yes. BharatLM includes OpenAI-compatible function calling with JSON-schema tool definitions and parallel tool calls, so the model can invoke your CRM, ERP, calculators, search and internal APIs as part of a workflow.
Yes. BharatLM uses a ReAct + Toolformer-style agent loop with parallel tool execution — the model reasons, selects tools, observes results and iterates toward a structured final response, with human approval where appropriate.
Yes. BharatLM includes grammar-constrained JSON output with schema enforcement — so applications can consume model output reliably. Schema-constrained outputs can be implemented and validated for your specific schemas.
Yes — Indian language capability is a core differentiator. The tokenizer uses a 64K vocabulary designed for Devanagari, math and 22+ Indian scripts, with Indian-language optimization built in. Language support is scoped to the languages your users actually need.
Yes. BharatLM can be positioned for controlled deployments — cloud API, private cloud or on-premise — so your data and inference stay within your boundary. We design the deployment around your data, security and control requirements.
Yes, for organizations requiring controlled infrastructure. We assess the hardware, model size and latency trade-offs, and design the serving stack to run where you need it — including embedded or edge options where model size and hardware permit.
Yes — as a potential implementation. A healthcare domain AI can combine hospital knowledge, policies and systems with BharatLM to build assistants for knowledge search, document summarization and SOP support. We do not claim diagnostic accuracy or regulatory approval.
Yes — as a potential implementation. A legal domain AI can combine your documents with retrieval and BharatLM to support contract analysis, clause extraction and legal knowledge search. It is a workflow tool, not a substitute for legal advice.
Yes — as a potential implementation. A finance domain AI can support policy assistance, internal knowledge, document analysis and risk workflow support. Model output is not regulated financial advice.
Yes — as a potential implementation. A manufacturing domain AI can combine SOPs, maintenance knowledge and production data with BharatLM to support SOP assistance, quality documentation and incident analysis — with human approval for recommended procedures.
Yes — as an implementation opportunity. A public-sector domain AI can support citizen service, scheme and policy knowledge, document processing and multilingual public-service assistance. These are implementation opportunities, not existing deployments.
Yes. We embed domain AI into the software your teams already use — web, mobile, CRM, ERP and internal tools — through an OpenAI-compatible API layer, so the intelligence appears where the work happens.
Yes. We build a domain benchmark on your real data — measuring accuracy, relevance, hallucination, citation quality, tool selection, JSON validity, language quality, safety, latency and cost — and run a continuous evaluation loop. BharatBench measures speed, quality and consistency metrics.
We measure hallucination on your domain test set and reduce it with grounded retrieval (RAG), citations, structured outputs and guardrails. We do not claim zero hallucinations — we measure the rate, set acceptable thresholds and add human review where the stakes are high.
A focused pilot can be built in weeks; a production domain AI system with integrations, evaluation and governance takes longer. We agree an illustrative timeline during discovery based on scope, data access and integration complexity — timelines are estimates, not guarantees.
Pricing is a custom quote. It depends on the domain, the data and systems involved, the capabilities required, integration effort, deployment model, and whether you start with a pilot or go straight to production. We scope each engagement individually.
Yes — a Domain AI Pilot is one of our engagement models. We build one focused use case on your real data, evaluate it against your success criteria, and give you a measured go / no-go recommendation before production investment.
Yes. Production domain AI needs monitoring, evaluation and improvement. We run a loop of test, error analysis, improvement and re-evaluation, and offer ongoing support to keep the system accurate and aligned as your data and business change.
Tell us about your domain, your data and the problem you want to solve. We'll show you what a BharatLM-powered domain AI system would look like for your business.
We learn your domain and data.
One use case, measured on your data.
Integrated, governed, improving.