Event-sourced architecture
Every CRM mutation produces an immutable event with full context. Replay the database to any point in time; build derived tables from a stream, not a nightly dump.
For data engineers and analysts
Your CRM as an event-sourced source of truth, not a snowflake.
Pact's event ledger is queryable in-product, exported to your warehouse on a schedule, and structured for analytics from day one. No more reverse-engineering Salesforce's history table.
Illustrative outcomes — composite of design-partner deployments
↓ 3 weeks
from CRM-change to warehouse-mirror lag
↓ 0
schema-drift incidents per quarter
↑ 100%
of mutations recoverable from event log
↓ 60%
data-team time on CRM plumbing
What you get on day one
Six things you stop juggling.
Warehouse sync, no glue code
Native exports to Snowflake, BigQuery, Redshift, and Databricks. Schema is stable, additive, and versioned. Deletes are tombstones, not row removal — so downstream joins don't lie.
ETL pipelines, observable end-to-end
Every pipeline run has a UI: lineage, lag, row counts, retries, and the deltas that landed. Failures page the on-call channel with a one-line summary, not a stack trace. New: ingest from a 73-connector marketplace and push segments back out with reverse-ETL — same lineage, same observability.
Identity resolution, deterministic-first
Stitches identities across email, LinkedIn, phone, and tracking events using deterministic matches first and probabilistic only on the residual. Match graph is auditable per record.
dbt-friendly schema
Tables are normalized, foreign-keyed, and documented in the same repo as the API. Your dbt models point at the warehouse mirror; the source schema doesn't drift between releases.
Cost telemetry for AI workloads
Per-tenant AI usage ledger: every LLM call has model, tokens in/out, cost, and the surface that triggered it. Set caps; alert on anomalies; chargeback by team.
“We modeled Pact's event stream in dbt in an afternoon. The dimensional model fell out of the schema for free. I have not had to build a single "CRM ↔ warehouse" Airflow DAG.”
Analytics Engineer · Series-C SaaS · 60 people · illustrative scenario
For other roles
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Last reviewed: 2026-06-15
American English · claims grounded against shipped functionality
Closes DP-014 + DP-015