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Industry: B2B SaaS & Product

AI agents for B2B SaaS that take actions, not just answer

Onboarding agents that watch activation events. Ticket triage agents that draft L1 replies. Churn intervention agents wired into Stripe and PostHog. SDR agents that enrich inbound leads and book meetings. Built by the team running 6+ SaaS products of our own in production.

40-60%
L1 ticket deflection
20-30%
CS capacity freed
6+
Own SaaS products live
Human
Review on every action
The SaaS Ops Problem

Support costs climb. Churn signals get missed. CSMs juggle 40 accounts. SDRs burn out on manual research.

Every growing SaaS team hits the same wall around Series A. Support headcount scales sub-linearly with users, but ticket volume grows linearly. Your CSMs are supposed to catch churn signals across 40 accounts each, but nobody actually reads every Mixpanel dashboard on Monday morning. Your SDRs spend 60 percent of their week on LinkedIn enrichment and reply drafting, not talking to prospects.

Chatbots don't fix this. Agents do. An agent watches the signals continuously, drafts the action a human would take, and either executes inside policy or escalates with full context. Your team stops doing repetitive glue work and spends time on the 20 percent that actually needs a human.

The right question is not "should we add AI". Most SaaS teams already did that with a chatbot on the marketing site. The real question is where in your ops pipeline is a human doing the same 30 minutes of work 40 times a week. That is where agents pay back inside two quarters, not two years.

Proof of work

We ship agents into our own 6+ SaaS products

Dcomply (compliance OS), Fluxeta (creator OS), VakeelSaathi (legal OS), RealZent (brokerage OS), SignupDesk (event registration), Dpublish (digital publishing). Every one of these has agents running in production for onboarding nudges, ticket triage, or churn watch. The same architecture, memory patterns and eval loops we deploy for you.

See our products
Our Approach

Agents that live inside your existing SaaS stack

Every SaaS company has a stack. HubSpot or Salesforce for CRM. Stripe for billing. Intercom or Zendesk for support. Slack for internal comms. Linear or GitHub for engineering. PostHog or Segment for product analytics. We wire agents into that graph, not build a parallel system your team has to babysit.

1. Map the workflow

Which decision does the agent replace? Who does it today, using which tools, with what policy? We turn that into a graph before writing any code.

2. Human-in-the-loop first

Every agent starts in draft-only mode. Your team reviews and approves each action for the first 2-4 weeks. Autonomy expands only after eval scores hold above 90 percent on real cases.

3. Guardrails, not vibes

Policy limits on refund amounts, discount ceilings, escalation triggers, PII scrubbing. Written down. Enforced in code. Every action logged for SOC 2 review.

Agent Patterns for SaaS

Eight agents we ship into B2B SaaS teams

Customer Onboarding Agent

Guides new users through the activation flow. Watches for key events (invite sent, first integration, first report). Sends nudges if a step stalls. Escalates to the assigned CSM if activation stops for more than 48 hours.

Higher activation rate

Ticket Triage & Draft-Reply Agent

Reads every inbound Intercom or Zendesk ticket. Categorizes by product area. Routes to the right queue. Drafts an L1 response with source citations for your CSM to approve or edit in one click.

40-60% L1 deflection

Churn Intervention Agent

Watches Stripe billing signals, PostHog usage drop-offs, support ticket sentiment. Scores every account weekly. Drafts CSM outreach with a proposed concession (discount, extended trial, feature unlock) inside policy limits.

1-3% ARR saved

Account Health Insights Agent

Every Monday, sends CSMs a one-page brief per account. Usage trend, top-3 friction points from tickets, expansion signals, red flags. Cuts weekly account-review prep from 3 hours to 15 minutes.

10x faster QBR prep

SDR / Lead Qualification Agent

Enriches inbound leads from HubSpot or Salesforce (company size, funding, tech stack, buying signals). Drafts a personalized first-touch reply. Books meetings on the rep calendar for qualified leads. Feeds full context to the CRM.

15-25 hrs/week per rep

Contract & Renewal Agent

Tracks renewal dates 90/60/30 days out. Reads customer sentiment across tickets, usage and NPS. Drafts renewal scenarios (flat, upsell, contraction). Flags at-risk renewals to CS leadership with proposed talk tracks.

Zero missed renewals

Bug Triage & Release-Notes Agent

Reads incoming bug reports from Intercom, GitHub, Linear. Groups by likely root cause. Assigns severity. Drafts a customer-facing changelog when the fix ships. Your engineers stop writing the same three release notes every week.

Cleaner backlog

Feature Usage Analytics Agent

Auto-summarizes which customer cohorts adopt which features. Runs weekly. Feeds product managers a plain-English digest: what's sticking, what's not, which segments to target for the next release.

Faster product decisions

Curious which agent would move the needle first?

15-min call. Tell us your CS + sales stack, current pain areas, team size. We come back with a ranked list of 3 agents by ROI and a fixed pilot price in 48 hours.

Book Free 15-min Call
Tech Stack

What we build on

Boring, battle-tested pieces that compose into an agent your team can actually operate six months from now. No black boxes, no vendor lock-in on the orchestration layer.

Orchestration

LangGraph CrewAI Temporal LiteLLM router

Models

OpenAI GPT-5 / 4.1 Anthropic Claude Sonnet / Opus Llama 3.3 (self-hosted) Mistral Large

Memory & Retrieval

Postgres + pgvector Qdrant / Weaviate Redis for session state

SaaS Integrations

HubSpot API Salesforce API Stripe API Intercom / Zendesk Slack SDK Linear / GitHub PostHog / Segment

Evals & Observability

Langfuse OpenTelemetry traces Custom rubric evals Regression test sets

Deployment

AWS / GCP / Azure ECS / Cloud Run / AKS Bedrock / Azure OpenAI Full self-hosted option
Internal Proof

VakeelSaathi: agents in production at scale

VakeelSaathi is our own legal-tech SaaS. Advocates use it to research case law, draft petitions, and manage matters. At scale (50k+ documents indexed, thousands of queries daily) we hit the exact ops wall every growing SaaS hits.

We deployed the same agent stack we sell. A triage agent reads every inbound query and routes to the right retrieval graph. A draft-reply agent handles the L1 support queue with human review. A weekly account-health agent flags advocates whose usage dropped so a human can reach out before they churn.

Result: 40 to 60 percent L1 ticket deflection. 20 to 30 percent CS capacity freed up for high-value account work. Cost per resolution dropped ~70 percent versus the pre-agent baseline. More importantly, the CSM team stopped context-switching every 15 minutes and started shipping structured expansion plays for the top-quartile accounts.

The build ran on the same LangGraph + pgvector + Claude Sonnet stack we deploy for external SaaS clients. Human-review console was live from day one. Full autonomy on categorization + routing was granted after two weeks of eval scores staying above 92 percent. Draft-reply autonomy is still gated by a CSM approval click on every message, by design.

Read the case study
Numbers from production

What we measured over 90 days

  • 40-60% of L1 tickets resolved without human touch
  • 20-30% CSM time recovered for expansion + strategic accounts
  • ~70% reduction in cost per ticket resolution
  • < 2 sec median agent response time on triage
  • 100% of actions logged with full audit trail
Process & Pricing

Pilot in 3-4 weeks. Full suite in 6-10 weeks.

Week 1
Discovery

Look at your stack, workflows, top 3 ops pain areas. Written feasibility with agent ranking by ROI.

Free
Weeks 2-4
Pilot Agent

One agent (usually onboarding, triage or churn watch) live in draft-only mode. Your team reviews every action.

₹1.5-3 L
Weeks 5-10
Full Agent Suite

3-5 agents wired across CS, sales and product ops. Shared memory. Human-review console. Eval dashboards. Deployment on your cloud.

₹5-15 L
Ongoing
Managed Agent Ops

Model costs, memory store, eval runs, policy tuning, escalation review, monthly capability upgrades. Flat monthly, no per-action fees.

From ₹40k/mo
FAQ

Questions SaaS founders ask on every call

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.

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.

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.

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.

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.

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.

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.

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.
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Related capabilities & proof

Stop paying humans to do glue work

15-min call. Tell us your CS + sales stack, top ops pain, team size. We come back with 3 ranked agent bets and a fixed pilot price in 48 hours.

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