CONNECTHIVEAI Transformation Lab Researching Practical AI for India
Our applied research and transformation initiative: small language models, Sanskrit-first and Indian-language AI, efficient inference and agentic workflows — developed carefully, measured honestly and labelled clearly as research until the results are proven.
Practical AI, Built and Tested for India
Most AI models are designed first for English and for organisations with large AI budgets. Indian businesses work in many languages, on WhatsApp, with cost-sensitive infrastructure. The lab focuses on the questions that matter here: which tasks AI can do reliably, how to run it affordably, and how to support Indian languages — starting with Sanskrit, Hindi and Marathi.
Status: Sanskrit SLM research, Indian-language translation and multi-agent orchestration are research in progress — no model has been released, no prototype is presented as a production system and no benchmark results are claimed. AI features listed as available in our products are marked separately.
Six Areas of Applied AI Work
Each area shows its current status, so you always know what is available today and what is still research.
Available in our products Project-based Research in progress
AI Transformation and Applied Research
We help organisations move from AI curiosity to working systems: identify the workflows where AI genuinely helps, build a small proof of concept, measure it honestly and only then plan a rollout.
- Use-case discovery and feasibility review
- Proof-of-concept builds with clear success criteria
- Cost, latency and quality evaluation
- Rollout and change-management planning
Project-based
Sanskrit-First Small Language Model Research
An ongoing research direction: exploring how a compact, task-focused language model could understand, translate and summarise Sanskrit text with human linguistic review at every stage.
- Curating openly licensed Sanskrit corpora
- Devanagari and transliteration normalisation
- Sandhi- and compound-aware tokenisation experiments
- Evaluation with Sanskrit scholars in the loop
Research in progress
Indian-Language Translation and Summarization
Research into translation and summarisation pipelines for Sanskrit, Hindi and Marathi, designed to keep meaning, names and numbers intact and to flag uncertain output for human review.
- Translation and summarisation pipeline design
- Glossaries for names, terms and numbers
- Automatic and human evaluation
- Hallucination checks before output is shown
Research in progress
Efficient SLM Inference and Token Cost Reduction
Practical engineering to lower the cost of running AI: smaller task-specific models, shorter prompts, caching and routing — always measured against quality, not just token counts.
- Model routing between small and large models
- Prompt and context reduction
- Semantic caching and output limits
- Quantisation and batching for self-hosted models
Project-based
Agentic AI and Multi-Agent Orchestration
Designing multi-agent workflows with LangGraph-style orchestration, MCP tool connections and A2A-style agent communication, with human approval for any action that matters.
- Specialised agents with narrow permissions
- Shared state, memory and error recovery
- Human-in-the-loop approval steps
- Observability, cost and latency controls
Research in progress
Enterprise AI Integration
Bringing AI into the systems teams already use. Our AI-Powered CRM already includes AI features such as a chatbot, lead scoring and WhatsApp automation; integration work extends this to other business systems.
- AI features in our AI-Powered CRM and Healthcare CRM (AI in our HRMS is on the roadmap)
- Integration with existing business data
- Role-based access and audit-friendly design
- Data-handling reviews before go-live
Available in our products
Sanskrit-First Small Language Model Research
Sanskrit is one of the most precisely described languages in the world, yet it is a low-resource language for modern AI. This is an ongoing research initiative, not a released product.
Datasets
Sanskrit text is spread across digitised manuscripts, academic corpora and modern writing, with uneven quality and licensing. The first task is curating openly licensed, well-documented text and recording the source and licence of every document.
Tokenisation
Sanskrit joins words through sandhi and forms long compounds (samāsa), so a generic tokeniser can split text into many meaningless fragments. We are evaluating sandhi-aware pre-processing and tokenisers trained on Sanskrit text.
Text normalisation
The same word can appear in Devanagari or in transliteration schemes such as IAST, with inconsistent Unicode, punctuation (daṇḍa) and accent marks. Consistent normalisation comes before any training.
Translation and summarisation
Target tasks include Sanskrit-to-English and Sanskrit-to-Hindi/Marathi translation and short summaries — tasks where meaning must be preserved and output can be checked by scholars.
Low-resource modelling
Compared with English or Hindi, Sanskrit has little parallel and annotated data. This favours fine-tuning existing multilingual models over training from scratch, plus careful data augmentation.
Fine-tuning, quantisation and distillation
A compact model fine-tuned for a narrow task, then quantised or distilled, may run on modest hardware. Each step is evaluated, because compression can cost accuracy.
Retrieval augmentation
Grounding answers in retrieved source passages lets a model cite the text it relied on, which helps reviewers check accuracy.
Evaluation and human review
Automatic metrics such as chrF are only a first filter. Sanskrit scholars review samples for meaning, grammar and hallucinated content before any result is trusted.
Further reading: Building a Sanskrit Small Language Model: Research Challenges and Architecture
Indian-Language Translation and Summarization
Our research pipeline for Sanskrit, Hindi and Marathi starts with clean, normalised text, adds a glossary for names, places, numbers and domain terms, translates or summarises, and then checks the output before anyone relies on it.
Machine translation can sound fluent while being wrong. That is why every pipeline we design includes automatic checks for dropped names and numbers, confidence flags for uncertain sentences, and human review for anything published or used in decisions.
- Normalise script, Unicode and punctuation
- Apply glossaries for names and terms
- Translate or summarise with a task-tuned model
- Check numbers, names and length
- Human review before publication
Further reading: Sanskrit, Hindi and Marathi AI Translation: Building an Indian-Language NLP Pipeline
Efficient SLM Inference and Token Cost Reduction
Fewer tokens usually means a smaller bill — but not always a cheaper system. A cheaper model that needs more retries, more human correction or more servers can cost more overall. We compare options on the full picture.
Smaller task-specific models
A fine-tuned small model can handle a narrow task such as classification or extraction at a fraction of the compute of a large general model.
Prompt compression and context reduction
Shorter instructions and only the relevant context mean fewer input tokens on every request.
Semantic caching
Similar questions can reuse earlier answers instead of calling the model again — with care for freshness and privacy.
Model routing
Send easy requests to a small model and only hard ones to a larger model.
RAG-based retrieval
Retrieve a few relevant passages instead of pasting whole documents into the prompt.
Output token limits
Ask for concise, structured output; output tokens are often priced higher than input tokens.
Quantisation and batching
For self-hosted models, lower-precision weights and batched requests increase throughput per server.
Cost-per-request measurement
Track real cost per completed task, including retries, infrastructure and human review time.
A Practical Evaluation Framework
Every cost-reduction idea is tested against the same five measures on the same task before it is adopted.
| Measure | How we measure it | The question it answers |
|---|---|---|
| Accuracy / quality | Task-specific test set; human review of a sample | Does the cheaper option still meet the quality bar? |
| Latency | Median and slow-tail (p95) response time | Is it fast enough for the user waiting on it? |
| Token consumption | Input and output tokens per completed task | Where do the tokens actually go? |
| Infrastructure cost | API spend or GPU/server cost per 1,000 tasks | What does a finished task really cost? |
| Failure and retry rate | Share of requests that fail, retry or escalate | Do savings disappear in retries and manual fixes? |
Further reading: How to Reduce LLM Token Costs with Small Language Models, Caching and RAG
Agentic AI and Multi-Agent Orchestration
Agents that plan and use tools can automate multi-step work — and can also fail in new ways. These are the design principles we are researching for safe, observable multi-agent systems.
LangGraph orchestration
Model a workflow as a graph of steps with explicit state, so every branch, retry and approval is visible and testable.
Specialised agents
Give each agent one job — research, drafting, checking, filing — and only the tools that job needs.
MCP tool integrations
Use the Model Context Protocol (MCP) to connect agents to tools and data sources through a standard interface.
A2A communication
Agent-to-Agent (A2A) style protocols let agents built by different teams discover each other and exchange tasks.
Shared state and memory
Keep task state explicit and store only the memory a workflow needs, with retention limits.
Human-in-the-loop approval
Require a person to approve actions that send messages, change records or spend money.
Error recovery
Plan for tool failures, timeouts and bad outputs with retries, fallbacks and safe stops.
Observability
Trace every step, tool call, token count and decision so failures can be diagnosed.
Security and access control
Least-privilege credentials, input validation and protection against prompt injection from untrusted content.
Cost and latency management
Budgets per task, step limits, caching and routing keep agent workflows predictable.
Further reading: Multi-Agent AI with LangGraph, MCP and A2A: An Enterprise Architecture Guide
RAG and Enterprise Knowledge Systems
Retrieval-augmented generation (RAG) lets AI answer from your own documents instead of from memory — and show where each answer came from. Most of the work is in the data, not the model.
Project-based
Document preparation
Clean, de-duplicate and version source documents so the system retrieves the current policy, not the outdated version.
Chunking and indexing
Split documents into meaningful sections and index them for both keyword and semantic (embedding) search.
Permission-aware retrieval
Users only retrieve passages they are allowed to see — access control is applied before the model sees any text.
Grounded answers with sources
Answers cite the passages they rely on, and the system says so when the documents do not contain the answer.
Retrieval and answer evaluation
Test sets of real questions measure whether the right passages are found and whether answers stay faithful to them.
Freshness and governance
Re-index when documents change, keep an audit trail, and review questions the system could not answer.
Enterprise AI Integration
AI is most useful inside the tools people already use. Our products are where AI reaches day-to-day work:
- AI-Powered CRM — chatbot, lead scoring and WhatsApp automation
- AI Healthcare CRM — appointments, reminders and patient follow-up
- AI HRMS — attendance, leave and payroll, with AI assistance on our roadmap
How We Work Responsibly
- Research is labelled as research until results are independently verifiable.
- No benchmark, accuracy figure or model size is published without a reproducible method.
- Only openly licensed or properly permissioned data is used.
- Humans review AI output wherever errors could mislead or harm.
- Personal data is minimised and handled under applicable Indian law.
Our AI Research Roadmap
The order we plan to work in. Stages are goals, not completed milestones, and dates are not promised.
Foundations
Curate openly licensed Sanskrit, Hindi and Marathi text; define normalisation rules and evaluation sets with linguists.
Baselines
Measure existing multilingual models on our evaluation sets to understand where they succeed and fail.
Task fine-tuning
Fine-tune compact models for narrow translation and summarisation tasks; compare against baselines.
Efficiency
Test quantisation, distillation, caching and routing against the cost-and-quality framework.
Pilot
Run supervised pilots with human review before any wider use — and publish what we learn.
Frequently Asked Questions
Has ConnectHiveCRM released a Sanskrit language model?
No. Sanskrit-first small language model work is an ongoing research direction. We will describe results publicly only when they can be reproduced and verified.
What is a small language model (SLM)?
A language model with far fewer parameters than the largest general models, usually fine-tuned for a narrow task. SLMs can be cheaper and faster to run, but they must be evaluated carefully for quality.
Does using fewer tokens always reduce AI costs?
Not automatically. Total cost also depends on infrastructure, retries, latency requirements and the human review needed when quality drops. We measure cost per completed task, not just tokens.
Which AI features are available in your products today?
Our AI-Powered CRM lists features such as an AI chatbot, lead scoring and WhatsApp automation as available. Research topics on this page — including Sanskrit SLMs and multi-agent systems — are not product features yet.
Can you build a proof of concept for our organisation?
Yes — get in touch to discuss your use case. We start with a small, measurable proof of concept before recommending any larger investment.
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Tell us the workflow you want to improve. We will tell you honestly whether AI is a good fit, and how a small proof of concept could test it.