How is an AI agent different from a chatbot?
A chatbot answers questions. An agent takes actions. An onboarding agent doesn't just tell the user how to invite a teammate, it watches for the invite event, and if it doesn't happen within 48 hours, it sends a follow-up, updates the CRM lifecycle stage, and pings the assigned CSM in Slack. Agents plan, call tools, remember context, and escalate to humans when policy says they should.
How do you handle data privacy and SOC 2?
Every agent action is logged (who, what, when, which tool, which model) so audit trails are ready for SOC 2, ISO 27001, or internal reviews. We support zero-data-retention model endpoints (OpenAI enterprise, Anthropic Bedrock, Azure OpenAI). PII masking happens before any prompt leaves your VPC. For strict environments we deploy Llama or Mistral inside your own AWS or GCP account so no customer data ever leaves your perimeter.
Self-hosted or cloud deployment?
Both. Default is your cloud (AWS / Azure / GCP) with our managed setup, using vendor LLMs on zero-data-retention agreements. For teams with strict data policies, full self-hosted with Llama 3.3 or Mistral Large runs inside your VPC. Hybrid also works: retrieval and orchestration on your side, generation via a cloud LLM under an ND agreement.
What is the ROI on a SaaS AI agent build?
Two levers usually pay back the build inside 4 to 9 months. First, L1 ticket deflection of 40 to 60 percent lets a CS team of 5 handle the volume of 8. Second, churn signal automation typically saves 1 to 3 percent of ARR that would otherwise leave silently. On the sales side, an SDR agent doing enrichment + first-touch replies frees 15 to 25 hours per rep per week for actual selling.
How much integration work does our team need to do?
Minimal. Most SaaS teams already have HubSpot, Salesforce, Stripe, Intercom, Slack, Linear or GitHub wired into their stack. We use their public APIs plus webhooks. For read-only signals we usually need less than a day of your engineering time. For write actions (updating CRM records, creating Jira issues, applying Stripe coupons) we scope policy limits with your team upfront so nothing runs outside guardrails.
Which LLMs do you use and can we swap them?
GPT-4.1 / GPT-5 for planning and tool selection, Claude Sonnet / Opus for long-context reasoning and drafting, Haiku or GPT-4.1-mini for cheap high-volume classification, Llama 3.3 or Mistral for self-hosted. Model choice is per-node in the graph, not global, so you can swap any step later without rebuilding. LiteLLM sits in the middle so switching a provider is a config change.
When do AI agents outperform chatbots for SaaS?
Chatbots win for pure Q&A on docs. Agents win when the workflow needs 3+ steps, spans multiple tools, or has to make a decision based on live product state. Example: a customer asks for a refund. A chatbot explains the policy. An agent reads their Stripe history, checks their usage in PostHog, applies the refund rule, updates HubSpot, drafts a reply, and if the amount exceeds your policy threshold it escalates to a human with the full context in Slack.
How much does an agent build cost?
Pilot (one agent, one workflow): ₹1.5 to 3 lakh across 3-4 weeks. Full suite (3-5 agents wired across CS, sales and product ops): ₹5 to 15 lakh across 6-10 weeks. Managed ops from ₹40,000/month covering model API costs (typically ₹15-40k depending on volume), memory store, eval runs, policy tuning, human-review console. Enterprise volume gets custom pricing.
