Bad data infrastructure doesn't announce itself. It just quietly breaks everything downstream.
We build the pipelines, lakehouses, and orchestration layers that companies need before AI, dashboards, or any of the things built on top of data can actually work.
Data Pipelines & ETL/ELT
Data Lakehouse Architecture
Cloud Data Migration
If any of this sounds familiar, you're who we build for.
Real language from real clients — not agency copy. These are the conversations that usually start an engagement.
We have data in six systems and no one trusts any of them.
Our data pipeline breaks whenever a source changes something upstream.
We want to move to AI but the data isn't clean enough to do anything reliable with.
We're still on-premise and the migration feels too risky to attempt.
Concrete deliverables. Nothing abstract.
Everything below is testable, demoable, and yours on handover — not a vague statement of work.
Data Pipelines & ETL/ELT
Extract, transform, and load — built to handle schema drift, failed sources, and high volumes without someone watching it. Failures surface with alerts, not after-the-fact damage.
Data Lakehouse Architecture
A single governed store on Azure (Databricks / Fabric) or AWS (S3 + Glue + Athena) with Delta Lake ACID transactions. ML-ready, query-optimized, access-controlled.
Cloud Data Migration
On-premise or siloed systems moved to Azure, AWS, or GCP. Row counts and checksums validate every move. Nothing gets to production until parity is confirmed.
Pipeline Orchestration & Monitoring
Airflow or Azure Data Factory orchestration with SLA-level alerting. If a pipeline misses a run at 2AM, you hear about it before your users do.
Data Governance & Quality Engineering
Catalogs, lineage tracking, access controls, and automated quality gates. Required for SOC 2, HIPAA, or any data room that needs to hold up under scrutiny.
ML-Ready Feature Engineering
Versioned feature stores integrated with your model pipeline. Models train on consistent, documented features — not ad-hoc queries that change between runs.
How an engagement actually runs.
The same accountable rhythm every time — adapted to what this service needs.
Data Audit
We map your sources, volumes, and failure points before writing a line of code. You get a clear picture of what's broken and why — not a sales pitch dressed up as a discovery call.
Architecture First
Pipeline design, schema drift handling, and governance decisions made before build starts. Retrofitting these into an existing architecture is expensive. We don't do it that way.
Build, Orchestrate, Validate
Pipelines built with orchestration wired in, quality checks at every layer, and row-level validation before data lands in production.
Govern and Hand Over
Data catalog, lineage documentation, alerting runbooks, and access controls. Your team runs this on their own after handover. That's the goal.
Recognized tools. No mystery frameworks.
The stack a CTO can vet on sight — chosen for your constraints, not our convenience.
Where this fits — so you can self-identify.
If one of these is basically your situation, this is the right page to be on.
E-Commerce
Unified order, inventory, and customer pipelines feeding real-time revenue dashboards — and ML-ready behavioral features for the recommendation layer you're building next.
FinTech & Payments
Transaction pipelines with fraud-detection feature stores, automated regulatory reporting, and an ACID-compliant lakehouse that holds up to an audit.
Healthcare & SaaS
HIPAA-compliant infrastructure, patient data ETL across fragmented source systems, and clinical datasets ready for model training without manual cleaning before each run.
Logistics & Supply Chain
Real-time shipment tracking pipelines, route optimization feature engineering, and fleet dashboards that pull from one source instead of three spreadsheets and a gut feeling.
We've shipped this kind of work.
AI-Powered SaaS · MVP Delivered
SocialSense
One AI dashboard for every social account
An AI-powered social media management platform — unified inbox, multilingual sentiment analysis, AI caption generation, and RAG-based automated responses, on a scalable microservice architecture.
75.6%
Multilingual sentiment accuracy
1
Unified AI dashboard
10+
Core workflows shipped
The questions clients are thinking but afraid to ask.
Answered honestly. If yours isn't here, ask it on the call.
Almost always yes. AI models are only as good as the data they're trained on. We've seen companies spend six figures on ML that failed because the data foundation wasn't there.
A single well-scoped pipeline: 2–4 weeks. A full lakehouse migration: 8–16 weeks depending on data volume and source complexity. You'll get a specific estimate after a data audit.
Yes — a managed support tier with SLA-backed uptime, schema-drift monitoring, and monthly performance reviews.
Yes. We optimize and extend existing Snowflake, BigQuery, or Redshift — or migrate off them if they're the wrong tool for your scale.
Ready to scope your Data Solutions project?
Tell us what you're building. We'll tell you how long it takes and what it costs — for free, in plain English.
No agency jargon. No surprise invoices. Just engineers who give you a straight answer.