AI for SaaS companies that takes ticket triage, churn signals and security reviews off your operations team

We build AI agents for your support, customer success and RevOps teams, inside the help desk, CRM and product data you already run. A person on your team approves every reply and every account action before it goes out.

Book a Discovery Call Start with an AI Audit

This page covers the operations behind your product: support, customer success, RevOps and security reviews. Jason, our COO, runs the call and scopes the first workflow with you.

Agent run: support ticket triageSample

Ticket receivedSSO login fails for 12 users since this morning's release
Done
Account context pulledPlan, seats, open tickets, renewal in 41 days
Done
Known issue matchedLinked to an open bug, fix planned for the next release
Done
Reply draftedWorkaround from your help docs, with the bug status
Done
Support rep approvalDraft and its sources wait for a person before sending
Waiting
Sent, account owner alertedRenewal inside 60 days, so the CSM gets a note and the thread
After approval

What the agent can seeOnly the fields this ticket needs. Everything else stays in your systems.

Ticket thread: Visible
Plan, seats, renewal date: Visible
Feature usage, 30 days: Visible
Contact email: m.k••••@
Data your customer stores in your product: Not retrieved
Payment details: Not retrieved

Illustrative run. Fields, routing rules and thresholds are set per workflow during scoping. The agent never sends a reply, a credit or a discount on its own.

Clients include

AI for SaaS operations, from the support queue to the renewal call

Start with work that repeats every week, follows rules your team has written down, and already gets a second look from someone before it reaches a customer. These six workflows meet all three. Not sure your data is ready? The AI readiness checklist scores it in ten questions.

Support

Ticket triage and drafted replies

The agent reads each new ticket, tags product area and severity, pulls the account's plan and history, matches known bugs, and drafts the reply from your help docs. A support rep edits or approves it before it is sent.

What a support agent can resolve on its own

Customer success

Churn signals and renewal prep

Each week the agent joins product usage with CRM and ticket history, lists accounts whose logins, seats or key features dropped, and builds a renewal brief for every contract inside your window. Your CSM decides what to do with each one.

How the agent joins usage and CRM data

Onboarding

Onboarding and implementation tasks

When a deal closes, the agent reads the order form and handoff notes, opens the implementation plan, chases missing configuration from the customer, and flags any account that stalls before first value.

How agents connect to your CRM

RevOps

CRM hygiene and pipeline checks

The agent finds deals with no next step, close dates in the past, duplicate accounts and contacts with no owner, then proposes each fix to the record owner. Your RevOps lead approves bulk changes before they are written.

Built on self-hosted n8n

Security reviews

Security questionnaire answers from your trust library

The agent drafts each answer from your approved policies, past questionnaires and trust center, cites the source document beside it, and marks any question it cannot answer from that library. Your security owner signs off before it goes back.

How approval gates work

Docs

Release notes and help docs upkeep

From merged tickets and changelog entries, the agent drafts customer-facing release notes and lists every help article that now describes the old behavior. Your product or docs owner edits and publishes.

Keeping a knowledge base fit for agents

The retention and security review work behind every renewal

43.6%

average annual growth of SaaS businesses with net retention above 100%, against 13.1% for those below 60%

Source: ChartMogul, SaaS Retention Report (November 2023), data from more than 2,100 SaaS businesses.

ChartMogul measured what retention is worth, and Vanta's survey counted the hours that compliance work takes. Each figure maps to the agent that takes on part of the work.

More than half of SaaS businesses retained less in 2022 than in 2021

The churn-signal agent reads usage, tickets and CRM every week, so your CSMs see a drop in logins or seats while there is still a quarter left to act on it.

41.1% of businesses with ARPA over $500 a month keep net retention above 100%

Only 2.7% do below $10 a month. When each account is worth that much, every renewal deserves a brief, and the agent builds it from the account's own history.

Over 11 weeks a year on manual security compliance tasks

By the survey's estimate, security teams could save 3 to 5 hours a week by automating work such as answering security questionnaires. The questionnaire agent drafts from your trust library, and your security owner approves.

65% say customers, investors and suppliers want more proof of compliance than before

Every drafted answer cites the approved document it came from, so your reviewer checks a source instead of rewriting from memory. The automation ROI calculator prices the hours.

Sources: ChartMogul, SaaS Retention Report (November 2023); Vanta, State of Trust Report 2024, conducted by Sapio Research with 2,500 business and IT leaders in the US, UK and Australia (October 2024).

Your customers' data stays inside the boundary your contracts set

Your own customers will ask what the agent read, where it ran and who approved what it sent. We build those answers into the system before it goes live, and we describe the mechanism rather than claim a certification.

Your environment

SourcesHelp desk, CRM, product analytics, trust libraryRead through the APIs they already expose
Context layerOne account at a timeOnly the fields each task needs, contact details masked
AgentSelf-hosted n8nRuns the workflow and its rules in your cloud
ApprovalNamed ownerSupport rep, CSM or security owner, before anything is sent

run 8812 · ticket triage · 4 sources read · product data not retrieved · reply drafted · approved by support rep · sent from help desk · CSM alerted

Outside the boundary

A model provider receives scoped fields only, under your own agreement with that provider, or is replaced by a model you host.

Never allowed

An agent sending a reply, credit or discount on its own
Customer data used to train models
A questionnaire answer with no approved source

Approval gates

A named owner on every customer-facing action

Replies, renewal outreach, CRM bulk edits and questionnaire answers wait for the person who owns them. Each workflow has an owner and an escalation path written down before it goes live.

See enterprise AI governance

Separate permissions

The agent that talks cannot act

The agent that replies to a customer has no permission to create tickets or change an account. A separate triage agent reads the finished conversation and files the ticket, so each action runs under its own permissions and shows in the run log.

See our enterprise AI chatbot builds

Your cloud

Built and logged in your environment

We deploy on self-hosted n8n in your cloud, behind your own auth, with permissions inherited from the systems the agent reads. Every run is logged with the records it read, what it changed and who approved it.

Self-hosted n8n for enterprise teams

How a SaaS operations AI build runs

The same team carries your workflow from the first call to the system running in production, and every build is tested in staging before it goes live. See how we work

Jason, COO
AI Audit, if you need one
Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap. The fee is credited toward your first build.
Jason, COO
Scope
One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.
- The ticket types, accounts or questionnaires in scope
- The actions that always go to a person
- The timeline, before you sign
Maciej, CTO
Build
Built against your real data, in your environment, as production software from the first week.
- Staging first, on a sandbox help desk and CRM
- Your reps use it on real tickets before go-live
- Self-hosted n8n in your cloud
Jason, COO
Integrate
Into production behind your auth, your governance and your monitoring, with your team trained on it.
- Separate development, pre-production and production environments
- Runs behind your own auth
- Your support and success teams trained on the system, with corporate AI training if more of the company needs it
Maciej, CTO
Run and improve
Watched, fixed and extended in weekly sprints by the engineers who built it.
- Error alerts to the engineers who built it
- Weekly sprints that you direct
- The next workflow, scoped with you

What your support and success teams get from a build with us

We build the agent around your playbooks and run it in your environment.

	What you get
You receive	Working agents inside your help desk, CRM and product analytics
Where customer data lives	In your cloud, on self-hosted infrastructure
Fit to your rules	Built around your support macros, success playbooks and trust library
Customer-facing actions	Approved by the person who owns them, and logged
Timeline	Scoped per workflow and agreed before you sign
After launch	The engineers who built it monitor and extend it

What SaaS operators ask about AI agents

Ask us something else

How can AI agents help customer success teams?

By doing the reading before each decision. An agent can join product usage with CRM and ticket history every week, list the accounts whose usage dropped, and draft a renewal brief with the open issues and the contract terms. Your CSM decides whether to call, offer help or escalate, and nothing goes to the customer without them.

How does AI ticket triage work?

The agent reads each ticket as it arrives, tags product area, severity and language, pulls the account's plan and renewal date, and checks open bugs for a match. It routes the ticket to the right queue and drafts a reply from your help docs. A second agent can check each draft against your rules before the rep sees it, the same pattern behind our enterprise AI chatbot builds. A support rep approves or edits the draft, and urgent tickets from accounts close to renewal also reach the account owner.

Will AI replace customer success managers?

In the systems we build, no. The agent takes the preparation, such as account research, usage summaries and renewal briefs, so your CSMs spend their hours with customers. Which tasks move to the agent is written into the scope before we build.

Can AI answer security questionnaires accurately?

It can draft them accurately when it answers only from documents you have approved: your policies, past questionnaires and trust center. Each answer cites its source, questions the library cannot answer are marked for your security owner, and that person signs off before anything is returned. We do not claim certifications on your behalf, and the agent does not invent controls.

How do you keep one customer's data out of another customer's answer?

A context layer scopes every task to one account, so the agent working a ticket reads that account's records and nothing else. Data your customers store inside your product is not retrieved unless a workflow needs it and you approve that field. Every run is logged with the records it read.

Which SaaS tools can the agents work with?

Anything with an API or an export: your help desk, CRM, product analytics, billing system, issue tracker and document store. The agent reads each one live through its API or on a schedule from its export. The AI Audit confirms what each of your systems allows before anything is quoted.

Do you build AI features into our product?

The builds on this page run your internal operations: support, success, RevOps, security reviews and docs. If you are deciding whether to build an AI feature for your customers, our build vs buy guide lays out the decision, and the discovery call is the place to ask whether we are the right team for it.

What does a SaaS operations build cost, and how long does it take?

Each workflow is priced and its timeline agreed in a written scope before you sign. The AI agent cost estimator shows what the model calls for a workflow cost a month. If you want proof first, an AI pilot runs 2 to 6 weeks on your own data and costs $35,000 to $75,000, with the fee credited toward the build. The AI Audit runs 4 to 8 weeks and costs $10,000 to $100,000 depending on scope, and its fee is credited toward your first build too. If another team built automations you already run, we can take them over after a technical assessment, under AI managed services.

Scope your first SaaS workflow before the call

Scope

AI use-case canvas

One page to scope a support or renewal process before anyone builds it.

Open the use-case canvas

Compare

25 AI use cases by department

What each one takes to ship, including the customer service and sales operations cases.

Read the use cases

Prove

AI pilot

A working proof of concept on your own tickets or accounts, before you commit to the full build.

See the AI pilot

Related industries

Investors

AI for venture capital

Agents that screen inbound decks, check diligence files, collect founder updates and draft LP letters, with a partner approving every message.

See AI for venture capital

Investors

AI in private equity

Agents that screen deals, review data rooms with page citations and monitor portfolio KPI packs.

See AI in private equity

Commerce

AI for retail

Agents that prepare product data, stock exceptions, supplier onboarding and returns, with a person approving each change.

See AI for retail

People

AI for HR

Agents that parse resumes into your ATS, schedule interviews, run onboarding checklists and match timesheets, with a recruiter deciding on every candidate.

See AI for HR

Put your first workflow on the board

Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.

Owen, Maciej, Jason, Oskar and Ben