Lawyers were losing 4-6 hours a day to legal research.
Manual search on Manupatra or Indian Kanoon. Copy-paste citations. Miss the newer judgment because you didn't know to look for it. When you did use ChatGPT, it invented case numbers.
Not another ChatGPT wrapper. We build RAG systems that read your policies, contracts, product docs, or knowledge base. And answer questions with sources you can click through. Same tech we used to index 50,000+ Indian court judgments for VakeelSaathi.
RAG stands for Retrieval-Augmented Generation. Before the AI answers your question, it searches an index of your documents, pulls the most relevant paragraphs, and gives them to the model as context. The model can only quote what you gave it. That's why a good RAG bot doesn't hallucinate. If the fact isn't in your docs, it says "I don't know" instead of inventing something.
The plain ChatGPT you use in your browser doesn't do this. It answers from training data it was frozen on. Which is why it makes up citations, dates, and policy details when you ask about your company.
India's Legal OS. 50,000+ Indian Kanoon judgments. Sub-500ms retrieval. Zero hallucinations.
Manual search on Manupatra or Indian Kanoon. Copy-paste citations. Miss the newer judgment because you didn't know to look for it. When you did use ChatGPT, it invented case numbers.
Structured extraction of case number, citation, bench, court, subject, holdings. Semantic search over 50,000+ judgments. LLM answers with the actual case links.
Because every answer is grounded in real judgments with real citation numbers. If the case doesn't exist in the index, the bot says so. That's the point of RAG done right.
All six use RAG at the core. Each one gets a different UX layer on top.
"What's our leave policy?", "How do I raise a purchase request?", "Where's the SOP for X?". Trained on your wiki, HR docs, engineering runbooks.
Runs on Slack, MS Teams, or a private web app.
Answers L1 tickets from your product docs and past resolved conversations. Hands off to a human when confidence drops. Cuts ticket volume 40-60%.
Sits inside Zendesk, Freshdesk, Intercom, or standalone.
A conversational agent on WhatsApp Business API. Answers, takes actions, hands over to human. VakeelSaathi's WhatsApp bot is in production. So is BookMySMS's messaging layer.
Session management + template messaging handled.
Upload a 500-page contract, RFP, or regulation. Ask questions. Get answers with paragraph citations. Great for legal, compliance, and procurement teams.
PDFs, Word, Excel, scanned docs (with OCR).
On your website. Answers pricing, feature, and comparison questions from your product docs. Books demos. Qualifies leads. Feeds them to your CRM.
Web widget with your brand, embedded in one line.
A ChatGPT-style interface deployed in your VPC or on-prem. Nothing leaves your walls. Backed by Llama, Mistral, or an Azure OpenAI private endpoint.
SSO, audit logs, role-based access included.
Priced by phase. You know what you're paying for before we start.
Two calls. We look at your data, users, guardrails. Written feasibility doc.
Working RAG bot on 500-2,000 of your actual docs. You test it. Two model options compared.
Full data ingestion, integrations, UI, SSO, audit logging, evals, security review, deployment.
Monitoring, cost control, quality evals, content re-indexing, patches. API costs included in fixed slabs. From ₹40k/month.
GPT-4o / o1 · Claude 3.5 · Gemini 1.5 Pro · Llama 3.1 (self-hosted) · Mistral Large (on-prem) · Pinecone · Weaviate · Qdrant · pgvector · Chroma / FAISS · LangChain / LangGraph · LlamaIndex · FastAPI · Ragas · Langfuse · AWS Mumbai · Azure India · GCP Mumbai · On-prem GPU (VPC) · Docker / K8s
We recommend the stack in the prototype phase and lock it in with you before production build. If your infra team already runs Kubernetes, Postgres, or a specific cloud. We work inside it. No forced migrations.
Ask Deci what you'd want the bot to do, or use the contact form. We'll come back with the stack, timeline, and a fixed prototype price.