One partner. Every layer of your data stack.
Reverse ETL, AI-driven data labeling, semantic modeling, automated cleansing, entity resolution, MDM, data mesh architecture, and governance — run as one integrated team across your entire data lifecycle, not eight separate vendor relationships.
Eight disciplines, one team
A quick look at where we plug in — from reverse ETL to governance.
Reverse ETL
Sync cleansed, governed data from your warehouse into CRMs, support tools, and ad platforms in near real time.
Learn moreAI-Driven Data Labeling
Human-in-the-loop and automated annotation pipelines for training, fine-tuning, and evaluating ML models.
Learn moreSemantic Data Modeling
Knowledge graphs and semantic layers that connect disparate data sources into a single, queryable model.
Learn moreAutomated Data Cleansing
Continuous profiling, validation, and cleansing pipelines that surface anomalies and enforce quality rules.
Learn moreHow an engagement actually runs
A look at the first three stages — discovery through implementation.
Discover
We audit your current systems of record and pain points — a real map of what's broken, not a boilerplate discovery deck.
Architect
We design the target-state architecture — semantic model, MDM strategy, governance rules — before a single pipeline gets built.
Implement
Our engineers build and ship the pipelines, matching logic, and sync jobs — production-grade from day one, not a proof of concept.
Why one partner, not eight
The short version of why the seams between tools matter more than the tools themselves.
Jnanin LLC
- One team across the full data lifecycle, from ingest to governance
- A single, consistent data model and architecture end to end
- One contract, one point of contact, one team accountable for results
Results, not just capabilities
One engagement, illustrative pending sign-off, out of several underway.
Shipment records were duplicated across six regional carriers, breaking on-time delivery reporting.
Built for your vertical's data reality
Retail
Customer identity fragmented across POS, e-commerce, and loyalty systems.
Financial Services
Regulatory reporting needs lineage that spreadsheets can't provide.
Healthcare
Patient records duplicated across EHR, billing, and lab systems.
A few questions, answered
The questions we hear most often before an engagement starts.
What is the difference between MDM and entity resolution?
Entity resolution identifies which records refer to the same real-world entity, while MDM governs the mastered record and how it is created, approved, distributed, and maintained.
What is a data mesh and do I need one?
A data mesh is an operating model where domains own and publish data products with shared governance; you may need one when a central data team has become a bottleneck, but not every organization needs the added operating complexity.
How is reverse ETL different from a normal ETL pipeline?
A normal ETL pipeline moves data into a warehouse or lake for analysis, while reverse ETL moves trusted warehouse data back into operational tools such as CRMs, support platforms, and ad systems.
Talk to a data architect
Bring us the layer that is slowing your team down. We will help you map the next move across the full lifecycle.