You can't vibe code a system of record
AI coding tools make an in-house marketing automation platform look like a weekend project. Then production arrives: tens of millions of records, billions of events, and pipeline that disappears every time a workflow silently fails.
Used by enterprise teams leaving
Marketo, Pardot, and HubSpot
A MAP is a system of record
It holds every contact, every touchpoint, and every signal your go-to-market runs on. The build that demos well on 500 test rows meets a different reality in production.
30M+
Contact records
A system of record for your entire go-to-market database.
1B+
Behavioral events
Every page view, product signal, and touchpoint, queryable.
Millions
Emails per month
Sent on schedule, personalized, and landing in inboxes.
24/7
Workflow uptime
Triggers that can never miss, on data that never sits still.
What you'd be signing up to build
Each of these is a product in its own right. A production marketing automation platform needs all six, running at once, forever.
Enterprise Salesforce sync
Bidirectional sync with field-level conflict resolution, API limit management, retries, and audit logs. One bad merge corrupts the records your sales team lives in.
A warehouse-scale data engine
Objects, relationships, and events from Snowflake or BigQuery, kept fresh on a schedule. Flatten it to contact fields and segmentation breaks. Keeping it live at 30 million records is a full-time data engineering job.
Deliverability at scale
IP warming, domain reputation, bounce handling, suppression lists, and consent compliance. Sending millions of emails is the easy part. Landing them in inboxes is a discipline.
A workflow engine that never sleeps
Real-time triggers across all of your data, with retries, idempotency, and observability. When a run fails at 2am, someone has to know before your pipeline does.
Agents grounded in your data
Agents are only as good as the context they can act on. Without a unified data layer underneath them, agent output at scale is guesswork.
Governance your CISO will sign
SOC 2, GDPR, role-based access, audit trails, and an on-call rotation. A system of record inherits the compliance burden of everything it touches.
Point tools fail at the seams
The other route is assembling a platform from parts: Workato recipes for sync, LeanData for routing, Chili Piper for scheduling, custom scripts against Snowflake. Every tool works on its own. The seams are where pipeline leaks.
Things break silently
A form stops posting, a sync recipe hits a rate limit, a field mapping drifts after a schema change. Nobody notices until the demo requests stop coming.
Pipeline leaks
Leads route to nobody, nurtures fire on stale data, the wrong email reaches the wrong person. Each seam drops a percentage, and the percentages compound.
Nobody owns the system
Five tools means five admin panels, five vendors, and no one who understands the whole picture. When the person who wired it together leaves, the knowledge leaves too.
Every pipeline you build is a pipeline to maintain
This is what the in-house build looks like six months in. Every dashed line is glue code your team owns, and any one of them can fail without a sound.
Salesforce 
Fivetran 
Segment 
Snowflake 
Hightouch 
Chili Piper 
Workato 
Customer.io 
SendGrid 
LeanData Can't I just connect Claude to my MAP's MCP?
It feels like the obvious move: point Claude at Marketo or HubSpot over MCP and let it run your campaigns. MCP gives the model hands. Everything underneath stays exactly as it was.
The flat data model stays flat
Claude can only see what the MAP's API exposes: a flat contact record. The warehouse signals that make automation smart never reach it, so the agent writes confident campaigns about the wrong customer.
One prompt can't move 30 million records
A MAP operates on tens of millions of records and billions of behavioral events, volumes a standard LLM call can't touch. Legacy MAP APIs rate-limit long before that scale.
No governance layer
A raw LLM with write access to your system of record ships whatever it decides. No permission scoping, no approval queue, no audit trail of what it read, reasoned, and changed.
Chat is not an execution layer
MCP answers when someone asks. Marketing runs on triggers and schedules: the 2am list hygiene job, the QA pass before every send. Nobody is sitting in a chat window at 2am.
This is why Conversion ships agents instead of a chat sidebar. They run inside the platform on triggers and schedules, permission-scoped and approval-first, with the unified data layer underneath. Every run is logged: inputs, reasoning, tool calls, and outputs. See how Conversion Agents work.
Over $1M+ in salaries alone. The math doesn't favor building.
Between the engineers to build and maintain it and the pipeline lost to silent failures, an in-house platform costs far more than it saves.
| Feature | Build it yourself | Conversion |
|---|---|---|
| Time to launch | Quarters of engineering before the first campaign ships | Live in weeks, with a 4-week average migration |
| Team required | 3 to 5 dedicated engineers, $1M+ a year in payroll alone | Your existing MOps team |
| Data scale | Your scaling problem to solve and re-solve | Tens of millions of records, billions of events |
| Email deliverability | Warm your own IPs, manage your own reputation | Built into the platform |
| AI and agents | Another system to design, build, and govern | Native agents with approval-first governance |
| When something breaks | Your on-call rotation, your lost pipeline | A vendor accountable for fixing it |
| Security and compliance | Your audit to pass, every year | SOC 2 Type II certified, GDPR compliant |
Common questions
What teams weighing an in-house build ask us most.
You can prototype it, and the prototype will work. The gap is between a demo on 500 test rows and a system of record holding 30 million contacts, processing a billion events, and sending millions of emails that must reach the right person at the right time. Sync engines, deliverability infrastructure, workflow reliability, and compliance are each multi-year engineering efforts, and a marketing automation platform needs all of them running at once, forever.
Each tool works on its own. The failures live in the seams: a recipe hits a rate limit, a routing rule fires on stale data, a field mapping drifts, and pipeline leaks without anyone noticing. You also end up with five admin panels and no single owner. Conversion runs segmentation, workflows, scoring, routing, and reporting natively on one unified data layer, so there are no seams to leak through.
The warehouse stores the truth, and it should stay your source of truth. Acting on it is the hard part: keeping objects, relationships, and events synced on a schedule, triggering workflows in real time, personalizing and delivering email at scale, and governing all of it. Conversion connects natively to Snowflake, BigQuery, Databricks, and Redshift, so your warehouse investment carries straight into activation without your engineers building or maintaining the plumbing.
No. Your current setup stays live while Conversion runs in parallel, so there is no cutover risk. Our field-deployed engineers handle the migration and QA every component, and the average migration takes 4 weeks with roughly 1 to 2 hours a week of your team's time.
Keep your engineers on your product
Conversion is the agentic marketing automation platform, built for the scale you would otherwise have to engineer yourself. See it on your own data.