Automate the backbone of your business. We engineer reliable data pipelines for reconciliation, compliance reports, and cross-system synchronization—eliminating manual spreadsheets and data drift. From simplified ETL to complex financial ledgers, we ensure your numbers match.
What You'll Get
Reliable ETL/ELT pipelines using modern stack (dbt, Snowflake, Airflow)
Automated Financial Reconciliation (Stripe vs. Bank vs. ERP)
Data Quality Firewalls (blocking bad data before it hits reporting)
Compliance-ready Audit Logs for every transaction
Self-Healing Pipelines that retry automatically on API failure
Golden Record creation for Customer 360
Real-time Dashboards for operational health
Data Dictionary & Lineage documentation
How We Deliver This Service
Our consultant manages every step to ensure success:
1
Data Mapping: Tracing the lineage of every metric you care about.
2
Pipeline Architecture: Designing idempotent, replayable data flows.
3
Modeling & Transformation: Writing clean SQL/dbt to standardize raw data.
4
Testing & Validation: Running automated tests against historical baselines.
5
Orchestration: Scheduling dependencies in Airflow/Prefect.
6
Observability: Setting up alerts for freshness, volume, and schema changes.
It means we treat your data like money. We implement double-entry logic in your data warehouse, ensuring that every record is traceable, immutable, and reconciles to the penny. Essential for audits and IPO prep.
Do you use ETL or ELT?
We prefer ELT (Extract, Load, Transform). We load raw data into your warehouse (Snowflake/BigQuery) first, then use dbt to transform it. This provides a permanent record of the raw state and allows for easier debugging.
How do you handle PII/Sensitive data?
We implement hashing and tokenization at the ingestion layer. Sensitive fields (SSN, Email) are masked or stored in separate secured tables with strict Row-Level Security (RLS) policies.
Can you fix my slow dashboard queries?
Yes. Often, dashboards are slow because they query raw data. We build 'Mart' tables—pre-aggregated, optimized views that make BI tools load instantly.
How do you ensure data quality?
We use testing frameworks like Great Expectations or dbt tests. If data arrives that violates your rules (e.g., null user_id, negative revenue), the pipeline halts or alerts before polluting your reports.
Client Reviews
★★★★ 4.9
based on 92 reviews
★★★★★ 5
Inventory finally stays synced
We were constantly reconciling Shopify orders against our MySQL warehouse tables with manual CSV exports. AHK.AI built CDC from our primary DB into a queue-based processor that updates inventory and order status within minutes. They also tuned a few slow queries that were locking tables during peak traffic. The best part is observability: we now get alerts when lag increases or a vendor webhook misbehaves. Ops stopped babysitting nightly jobs.
Project: Implemented CDC + queue workers to sync orders/inventory between Shopify integrations and MySQL warehouse; added monitoring and SQL optimization.
★★★★★ 5
Reliable near real-time pipelines
Our product analytics depended on a brittle ETL that ran every 6 hours. AHK.AI redesigned it into incremental sync with stored procedures and a message queue, feeding Postgres reporting tables without hammering production. They handled some painful vendor-specific quirks in our managed database service and put proper access controls in place (least privilege roles + rotation). We can actually trust dashboards during incidents now, and support tickets dropped.
Project: Replaced batch ETL with incremental CDC/queue pipeline from production Postgres to reporting schema; added role hardening and observability.
★★★★ 4.5
Safer integrations, cleaner data
We needed lab results to land in our internal system quickly, but HIPAA constraints made us cautious. AHK.AI implemented a controlled sync workflow that moves only required fields into a secured Postgres database, with audit logging and encrypted backups. They also refactored stored procedures that were producing occasional duplicate patient rows. Not everything was instant due to upstream vendor delays, but the pipeline is stable and the compliance story is much stronger.
Project: Built secure data synchronization + backup automation for lab result ingestion into Postgres; improved stored procedures and auditing.
★★★★★ 5
Leads and listings aligned
Our CRM and listings portal kept drifting—agents would update a property and marketing wouldn’t see it until the next day. AHK.AI set up a lightweight queue-based process that syncs listing updates from MongoDB into our SQL reporting tables and triggers downstream notifications. They also optimized a gnarly search query that was timing out during open-house weekends. It’s not flashy work, but it removed a constant source of “who changed this?” drama.
Project: Queue-based sync from MongoDB listings data to SQL reporting; query optimization for listing search and update visibility.
★★★★★ 5
Reconciliation without fire drills
Month-end used to mean pulling exports from three systems and arguing over which numbers were “right.” AHK.AI implemented CDC from our transaction database into a reconciliation schema, with stored procedures to normalize fees and reversals. They added proper retry logic and idempotency in the processors, which matters a lot when queues replay messages. Audit trails and lag dashboards gave our controllers confidence, and we cut close time by two days.
Project: CDC + queue processing for transaction sync into reconciliation Postgres schema; stored procedures for normalization and observability dashboards.
★★★★ 4.5
Reporting got much smoother
We aggregate campaign data from multiple ad platforms and our old ETL was constantly breaking on schema changes. AHK.AI built a sturdier pipeline with staging tables, automated backfills, and alerting when a source field shifts. They also tuned several SQL joins that were bloating our daily client reports. Still had a couple hiccups when one vendor throttled us, but the workflow recovered automatically and the team didn’t have to babysit it.
Project: ETL pipeline development with staging/backfill automation and SQL optimization for multi-source marketing reporting.
★★★★★ 5
Shop-floor data you can trust
We needed near real-time visibility into WIP and scrap rates, but our MES exports were manual and inconsistent. AHK.AI automated the database workflows to sync production events into a central Postgres store, using queued processing to handle bursts during shift changes. They added backup automation and health checks so we’d know when replication lagged. The plant managers now rely on the dashboard instead of calling supervisors for numbers.
Project: Automated sync of MES production events into Postgres with queue-based processing; added backup automation and monitoring.
★★★★ 4
Solid engineering, some ramp-up
We brought AHK.AI in to modernize a client’s database automation that had grown organically over years. They cleaned up stored procedures, implemented better logging, and introduced a queue pattern to decouple data sync jobs. The result is far more maintainable, and query performance improved noticeably. The only downside was onboarding time: our client had sparse documentation, so the first couple weeks were discovery-heavy. Once aligned, delivery was consistent.
Project: Modernized legacy database automation: refactored stored procedures, added observability, and implemented queue-based sync for a client environment.
★★★★★ 5
Enrollment data stays current
We run an LMS plus a separate student information system, and the nightly batch sync caused constant mismatches in course access. AHK.AI implemented incremental updates with clear failure alerts and a rollback strategy, so access changes propagate quickly without risking bad data. They also tightened database permissions for staff tools and automated backups to meet internal policy. Support requests about “I can’t see my class” dropped dramatically after launch.
Project: Built incremental data synchronization between SIS and LMS databases; implemented access hardening, alerting, and backup automation.
★★★★ 4.5
Fewer delays in tracking
Shipment tracking was lagging because updates sat in a staging table until a cron job ran. AHK.AI replaced that with CDC feeding a queue, and processors that update our Postgres tracking tables in near real time. They accounted for vendor EDI oddities and added metrics for throughput and dead-letter counts, which helped us spot spikes during weather events. We still see occasional upstream data gaps, but our system reacts faster and is easier to troubleshoot.
Project: Implemented CDC + queue-based processing for shipment status updates into Postgres; added observability and handling for EDI vendor quirks.
