Let AI agents operate your SaaS product on behalf of your users
We add an AI agent layer on top of your existing SaaS. Agents understand natural language tasks, plan multi-step workflows, and call your APIs via Model Context Protocol (MCP) to actually get work done.
From features to agents
Instead of users remembering where every feature lives, agents orchestrate flows across your entire product.
No second product to build
We reuse your existing APIs, permissions, and events. MCP is the bridge between your SaaS and AI agents.
Enterprise-grade safety
Scoped tools, guardrails, and auditability so agents can’t step outside what your product should be allowed to do.
Your SaaS already solves hard problems. AI agents make it actually feel effortless.
Most SaaS products are powerful but overwhelming. AI agents sit on top and handle the navigation, coordination, and “what should I click next?” questions for your users.
Today’s reality for complex SaaS
- • New users need long onboarding just to find core flows.
- • Power users keep private “click-runbooks” to get recurring work done.
- • Important features hide behind nested menus and filters.
- • Every integration needs custom UI, webhooks, and more maintenance.
With AI agents on top
- • Users say “what” they want, agents handle the “how”.
- • Flows span multiple modules and integrations without extra UI.
- • Agents can be embedded in your app, in ChatGPT/Claude, or internal tools.
- • You differentiate as “AI-native SaaS” instead of just “has a chatbot”.
How we add AI agents to your SaaS using MCP
We don't just drop an LLM into your UI. We design a proper agent architecture around your existing APIs, events, and permission model.
Map agent responsibilities
We identify 3–5 high-value “jobs” agents should handle in your product (e.g. onboarding, reporting, admin automation).
Design MCP tools & guardrails
We group your endpoints into MCP tools with clear schemas, limits, and permission-aware design for each agent.
Implement the agents
We implement agent behaviors and prompts that use your MCP tools to plan, execute, and verify multi-step workflows.
Embed & iterate
We embed agents into your SaaS UI (and optionally ChatGPT/Claude), observe real usage, and iteratively improve.
Agents reason in natural language, call MCP tools for actions, and your existing backend enforces all business logic and permissions.
Concrete agent use cases for SaaS products
A few patterns we see again and again across B2B SaaS: onboarding copilots, reporting agents, admin automation, and support automation.
Onboarding & configuration copilot
Users describe their use case; the agent configures the product: creates projects, sets defaults, invites teammates, and wires integrations using MCP tools.
Reporting & analytics agent
Instead of custom reports UI, users ask “Show me churn by segment over the last 90 days and highlight anomalies”, and the agent orchestrates queries + visualization.
Admin & bulk operations agent
Admin users say “Deactivate trial accounts older than 60 days with no logins”, and the agent runs a safe, auditable bulk operation using your existing APIs.
Create a Q2 revenue dashboard for DACH and send a weekly summary to our sales channel.
Planning steps:
1) Query revenue data for Q2
2) Filter by DACH region
3) Generate dashboard configuration
4) Schedule weekly summary to Slack channel
Tools used: report_query, dashboard_create, notification_schedule
Done. I've created the dashboard and set up a weekly summary every Monday at 9:00.
Who this is for
We’re a fit when your SaaS product is already successful, but you want to lead in AI-native UX—not just bolt on a chatbot.
B2B SaaS with deep workflows
Multi-module platforms (billing, analytics, CRM, etc.)
Products with complex configuration & onboarding
SaaS with strong API surface already in place
Teams with internal champions for AI initiatives
Companies aiming for premium, AI-forward positioning
What we optimize for
We measure agents by real outcomes: faster task completion, higher feature adoption, and reduced manual support load.
Task completion speed
How much faster users complete core workflows when they use agents instead of manual navigation.
Feature adoption & depth
Whether users reach advanced features via agents that they would never discover via menus.
Reduced human support load
Agents handle repetitive “how do I do X?” questions by performing the action or walking users through it in-product.
Premium AI agent layer, not a generic chatbot add-on
This touches your core product, roadmap, and positioning. We treat it as a strategic capability, not a quick hack.
AI Agents for SaaS
Add AI agents on top of your SaaS product
Investment: premium, by proposal
Scoped around complexity, number of agents, and depth of integration across your product.
A typical engagement includes
- • Agent strategy & responsibility mapping
- • MCP tool & guardrail design
- • Agent behavior & prompt implementation
- • In-app embedding & metrics wiring
Optional add-ons
- • Multi-tenant & multi-region architectures
- • Dedicated AI ops & continuous tuning
- • Additional agents for new product areas
- • ChatGPT/Claude + internal tools integration
If your SaaS has deep workflows and you want to lead in AI-native UX—letting agents operate your product instead of users wrestling with UI—we're likely a strong partner.
Request a scoping callReady to give your SaaS a real AI agent layer?
Tell us what your product does today and what you wish users could say in one sentence and have done for them. We'll design agents—and the MCP tooling behind them—to make that real.