Generative AI
Development
We build LLM applications on your own data and run them in your environment. Not demos - systems built from the first sprint to be reliable, auditable and ready to scale.
What we build
Three GenAI capabilities. One integrated stack.
RAG Pipelines
Retrieval-Augmented Generation that answers from your own documents, databases, wikis and APIs. We build the vector store, chunking approach and ranking logic so answers come back accurate and cited.
LLM Fine-tuning
We adapt models to your domain using LoRA, QLoRA or full fine-tuning - trained on your terminology, tone and decision logic, so the model sounds like your best expert instead of a generic assistant.
Enterprise Chatbots
Multi-turn conversational AI with memory, intent routing and a connection to your CRM, ERP or helpdesk. Works as a customer-facing assistant or an internal knowledge tool.
Use cases
Where GenAI earns its place in the workflow.
Document extraction
Read contracts, reports and regulatory filings. Pull out structured data, flag anything unusual, and send only the edge cases to a human.
Knowledge assistants
Internal search and Q&A over your policies, SOPs and product docs. Employees get an accurate answer in seconds, with a link back to the source.
Content generation
Product descriptions, reports and proposals written in your voice at scale, ready for a final human pass before they go out.
Code assistance
Developer tools fine-tuned on your own codebase - code completion, review help, test generation and documentation.
Data analysis
Ask a question in plain language, get SQL and an answer back - no data analyst needed for every one-off query.
Multilingual support
AI that works in Hindi, Tamil, Marathi and 30+ other languages, so Indian enterprises can actually reach their regional users.
Technology
Model-agnostic. Best-fit by use case.
We are not tied to one model provider. We choose and combine models based on the accuracy you need, your budget, data residency rules and how fast the response has to come back.
How we work
From data to something live.
Data & use-case audit
We look at your data quality, volume and structure, then pick a short list of GenAI applications and rank them by how feasible and how useful they are.
Architecture design
We pick the model, embedding strategy, retrieval approach and deployment setup that actually fits your use case and your existing infrastructure.
Build & evaluate
We build in cycles and test as we go - RAGAS metrics, human review and adversarial testing, before anything is promoted to production.
Deploy & improve
We deploy with monitoring, prompt versioning and retraining triggers in place, and a feedback loop that keeps output quality improving over time.
Common questions
Generative AI, answered directly.
How long does it take to build a generative AI system?
Most RAG pipelines and chatbots go from data audit to a working pilot in 6-10 weeks, depending on data quality and how many systems we need to connect to. Fine-tuned models add 2-4 weeks for training and evaluation before deployment.
Which AI models do you use?
We're model-agnostic - GPT-4o, Claude 3.5, Gemini Pro, Mistral, Llama 3 and Phi-3, chosen per use case based on accuracy needs, budget, data residency rules and response speed. Most of our production systems combine more than one model rather than relying on a single provider.
Do you fine-tune models or use RAG - what's the difference for us?
RAG connects a model to your existing documents and data at query time, so it can cite sources and stay current without retraining. Fine-tuning adapts the model itself to your terminology and decision logic. Most clients start with RAG and add fine-tuning once they know exactly which behavior needs to change.
Where does our data go - is it used to train your models?
No. We deploy in your environment or your cloud account, and your data is never used to train third-party models. RAG pipelines and fine-tuned models both stay inside infrastructure you control.
How do you prevent the AI from making things up?
Every system goes through RAGAS evaluation, human review and adversarial testing before it reaches production, and RAG pipelines are built to cite the source document behind every answer. We keep monitoring and retraining triggers running after launch, not just at handoff.
Can this work in Hindi or other Indian languages?
Yes - our chatbots and knowledge assistants support Hindi, Tamil, Marathi and 30+ other languages, which matters if your users or customers aren't primarily English-first.
Start building
Have data sitting around
that AI could actually use?
Book a discovery call. We will look at your data, find the GenAI opportunity worth building first, and give you a concrete roadmap.