Data Automation
Make data movement boring: reliable, monitored, and reversible when exceptions occur.
Automation without clean data is choreography on quicksand.
PrimeAxiom implements ETL/ELT, event-driven sync, and AI-assisted validation so records converge across systems. We design for drift: when vendors rename, SKUs split, or sales edits bypass process, exceptions surface before they pollute reporting.
Why this department matters
Every downstream system inherits upstream sins. A duplicate account in CRM becomes a split shipment, a wrong forecast, and a bad commission.
AI features are only as stable as their inputs. Retrieval and agents amplify inconsistencies if identity and timestamps are fuzzy.
Manual CSV uploads do not scale—they hide who changed what, when, or why.
Common pain points
Duplicate and fragmented identity
The same customer exists as slight variants across CRM, billing, and support. Matching rules are tribal knowledge in a macro.
Nightly batch jobs that nobody owns
When a job fails, teams notice in the afternoon stand-up—after decisions were made on stale numbers.
Reporting teams reconcile instead of analyzing
Finance and ops build parallel spreadsheets because metric definitions diverge by department.
Poor lineage and auditability
Regulators and partners ask where a number came from; the honest answer is “a few joins and hope.”
What we automate
Ingestion and change capture
CDC from databases, webhooks from SaaS, scheduled exports with checksums, and file drops with schema validation.
Normalization and enrichment
Standardize addresses, company names, and tax IDs; append firmographic or risk data where useful.
Matching and merge rules
Deterministic keys first; probabilistic scoring second; human queues for edge cases with audit trails.
Sync and conflict resolution
Define system-of-record per field; version merges; replay failed events without duplicating rows.
Data quality monitors
Expectation tests on null rates, distributions, and referential integrity—alerts before dashboards lie.
Curated marts and semantic layers
Expose governed dimensions and metrics to BI tools so “ARR” means one thing in every board slide.
Typical workflow / system flow
Example chain from trigger to reporting—your exact shape depends on stack and policy, but the control pattern stays consistent.
Trigger
Record change in source system or scheduled batch window.
Extract
API pull, CDC stream, or secure file arrival.
Transform
Cleanse, map to canonical model, apply business rules.
Load
Upsert to warehouse/lake; publish events to subscribers.
Validate
DQ checks; quarantine bad rows with reason codes.
Serve
BI, ops apps, and AI features read from governed endpoints.
Systems & integrations
- Warehouses: Snowflake, BigQuery, Redshift, Postgres.
- ELT: dbt patterns, Airbyte/Fivetran-class connectors where appropriate.
- CRM/ERP: Salesforce, HubSpot, NetSuite, Dynamics—via API and bulk patterns.
- Streaming: Kafka/PubSub when event volume justifies near-real-time.
- Catalog/lineage: OpenLineage-compatible metadata where teams need traceability.
AI intelligence layer
AI is not a replacement for your ERP—it is an accelerator for extraction, classification, prioritization, and surfacing exceptions before they become rework.
- Classification: label inbound records (lead type, product family) from sparse text.
- Deduplication: embeddings plus rules for fuzzy entity resolution.
- Anomaly detection: sudden spikes in returns, discounts, or usage that indicate upstream errors.
- Mapping assistance: suggest field mappings when onboarding a new SaaS tool.
- Summarization: human-readable change logs for data stewards.
Outcomes clients care about
Trusted metrics at leadership cadence
One definition, refreshed on schedule, with known freshness.
Less time cleaning, more time deciding
Stewards focus on exceptions—not every row.
Safer AI rollouts
Models and RAG indexes draw from governed corpora.
Lower integration TCO
Replace brittle scripts with monitored pipelines and clear ownership.
Example use cases
CRM ↔ ERP customer and order sync
Bidirectional updates with clear ownership for addresses, credit holds, and ship-to changes.
Product catalog harmonization
Align SKUs across e-commerce, POS, and WMS with alias tables and controlled splits.
Revenue analytics mart
Conform subscriptions, invoices, and usage into a single ARR view with cohort cuts.
Support ticket enrichment
Join tickets to account health, NPS, and churn risk for prioritization—not just FIFO.
Partner data exchange
Secure pipelines with validation and ACK workflows for B2B onboarding files.
FAQs
- Do we need a warehouse first?
- Not always. We scope the smallest durable store for your problem—sometimes Postgres plus jobs—then grow into a warehouse when analytical load demands it.
- How do you handle schema changes in SaaS APIs?
- Versioned connectors, contract tests, and alerts when vendors deprecate fields. Breakage becomes visible in hours, not at month-end.
- Who owns data quality?
- Business owners define rules; automation enforces them; exceptions route to named stewards. We document RACI as part of delivery.
- Can you work with our existing dbt project?
- Yes—we integrate with your repo conventions and CI so analytics engineering stays in control.
See how this fits your stack
Request a workflow review: we map bottlenecks, integrations, and a phased plan—no generic pitch deck.