Pipelines, warehouses, and observability with lineage and monitoring designed in, delivered in phases you can stop between.
What we build
Ingestion, a warehouse sized to the company, lineage, and monitoring. Each phase has a named outcome and a stopping point where the work still stands on its own.
Incremental, idempotent loads from ERP, CRM, event streams, and files, with schema contracts at every ingestion boundary.
How we prove it Named tables pass freshness and volume tests on a schedule before the phase is accepted.
PostgreSQL or a managed warehouse for most companies of 50 to 500 people. A lakehouse only when your own numbers force the question.
How we prove it The readiness audit shows the sizing case in your data, not in a vendor deck.
Every number in a report or an agent retrieval traces back to its source system, so you can trust what an agent reads.
How we prove it Lineage is traceable end to end for a named metric as written acceptance evidence.
Freshness and volume tests on critical tables, with alerting on test failure rather than on dashboard silence.
How we prove it Alerting is proven by a deliberate induced failure before handover.
Use cases
Every one of these starts with a named consumer for the data. A warehouse with no consumer is a monthly bill.
GL, budgets, and WIP from an ERP land in one reporting layer, so period close produces a report instead of a spreadsheet hunt.
Finance · Multi-entity · ERPNextClean, fresh, access-controlled tables an agent can read without a human exporting a CSV first.
AI pilots · RAG · SupportDaily pulls from accounting systems feed rule and AI checks that flag categorization errors and unreconciled items early.
Accounting firms · BookkeepingScrape or pull from many sites and APIs, normalize, deduplicate, and enrich records into one system of record.
Recruiting · Research · SalesA parallel analytics layer over operational databases, so reporting never slows the live application.
Logistics · SaaS · OperationsClassification first, then masking and access controls in the pipeline, then a data map you can forward to counsel.
Healthcare · Finance · SLEDTests, alerting, and idempotent loads added to pipelines we did not build, when repair beats rebuild.
Inherited stacks · Mid-flight pilotsNews, market, or lead signals collected continuously, deduplicated, scored, and stored where the team already works.
Sales · BD · Market researchClient stories
Each one replaced a spreadsheet, a manual scan, or a periodic review with a pipeline that runs on a schedule and tells someone when it breaks.
GL entries, budgets, WIP, and employee records pulled from ERPNext into a Postgres layer that produces P&L reports per cost center.
Read story
QuickBooks Online books for every client pulled daily and checked for categorization errors and unreconciled items.
Read story
Persistent LinkedIn scraping into MongoDB Atlas with validation and a live analytics dashboard on top.
Read story
Six sources scraped, normalized, enriched, and deduplicated into one system of record with a filterable portal.
Read story
Three newswires merged into one pipeline, near-duplicates caught by AI, each story scored 0 to 100 and stored in Notion.
Read storyHow we ship
Every phase leaves with the same four things defined before the build starts: a score, a stopping point, acceptance evidence, and a data map.
The audit produces a scored map across 6 dimensions, each with the specific finding behind the score. You get the map whether or not you build with us.
Data work is where budgets disappear if nobody closes the scope. We phase it.
"The pipeline runs" is not acceptance evidence. These are.
The data map is a deliverable, not an internal document. If you cannot forward it, it is not finished.
What you own
WHAT WE WILL NOT BUILD
For SLED scope under NAICS 518210, we build governed analytics layers as your subcontractor, with PII handled to standard, never facing the agency.
NDA-first, subcontract-only. We work behind the prime, under your brand. We do not pursue prime contracts and we never face the agency.
Parallel analytics layer. ELT runs without altering operational databases, so live applications stay fast.
Lineage you can show. Transparent data lineage, tested pipelines, and strict handling of personally identifiable information.
FAQ
Straight answers about data work and what it will and will not fix. We answer the hard ones first.
Ask the hard onePhase 1 is scoped to unblock 1 named use case. If the audit says that takes longer than a quarter, you hear it in the audit, before you commit to delivery.
Your cloud accounts, your billing. Access from Lahore, scoped and logged, revoked at exit.
The audit answers that specifically, including the case where data is not your blocker and something else is. That answer is worth the audit on its own.
Yes, and sometimes the recommendation is repair rather than rebuild, which is the less profitable answer for us.
Usually a warehouse at your size. We size to the company, not to the vendor deck.
Start the conversation
Tell us the AI use case that is stalled. We will tell you whether data is the reason.
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