Is client-privileged data safe with AI agents?
Yes, when the architecture is right. We run agent workloads inside your VPC or on-prem, keep vector stores in Indian data centres (AWS Mumbai, Azure India), sign zero-data-retention terms with OpenAI and Anthropic, and offer self-hosted open models like Llama or Mistral where nothing leaves your infrastructure. Privilege markers travel with every document so agents never expose privileged material in outbound drafts.
Are these agents compliant with the DPDP Act 2023?
Yes. Data fiduciary obligations sit with the firm, so we build purpose-limited processing, consent capture at intake, retention windows, and subject-rights response workflows into the agents. Cross-border transfer is off by default. We provide a DPDP compliance memo tied to your specific deployment.
Do the agents breach Bar Council of India advertising rules?
No. Client-facing agents are framed as firm workflow tools, not solicitation. Intake bots are triggered only by inbound enquiries the firm already receives. Public marketing copy is reviewed against BCI Rule 36 and the Chapter II Standards of Professional Conduct. We give partners a compliance checklist before go-live.
Can agents run on-premise instead of cloud?
Yes. For Tier 1 firms and PSU counsel we deploy the full stack on-prem. LangGraph orchestrator, self-hosted LLM (Llama 3, Mistral, or Qwen), local vector store, and audit logs on your servers. No client data ever touches the public internet.
Will agents integrate with our existing LMS?
Yes. We integrate through documented APIs where they exist, and through hardened scrapers or agent-driven RPA where they do not. Common integrations include LegalDesk, CaseFox, Manupatra, SCC Online, Indian Kanoon, eCourts, MS 365, Google Workspace, Tally, and WhatsApp Business. If you use something bespoke, we scope it in discovery.
What does an AI agent project cost?
Pilot with one agent runs ₹1.5 to ₹3 lakh over 3-4 weeks. A multi-agent build wired into your LMS, calendars, and eCourts runs ₹5 to ₹15 lakh over 8-12 weeks. Managed operations start at ₹40,000 per month. Smaller firms can start on VakeelSaathi's per-lawyer SaaS tier without a custom build.
Which model powers the agents, and can we choose?
Default is Claude Sonnet for reasoning and drafting, with GPT-4 class models as fallback. For pure retrieval and classification we use smaller open models to control cost. Firms with strict data policies can run self-hosted Llama or Mistral. Model choice is a per-agent decision, not a lock-in.
How do you stop the agents from hallucinating legal points?
Three defences. First, every research answer flows through the VakeelSaathi RAG layer where citations resolve to real judgment paragraphs. Second, drafts never auto-send. A human lawyer approves every outbound email, filing, or client message. Third, confidence thresholds trigger an explicit 'insufficient basis' response instead of a guess. We publish per-agent accuracy from live evals.
