Financial Analytics
- Customer profitability analytics
- Product profitability analytics
- Predictive sales analytics
- Cash flow analytics
- Value driver analytics
Pipelines, analytics, and production AI.
Data engineers, analysts, and applied ML specialists who ship systems — not slideware. Production deployments on Microsoft Fabric, Databricks, and Snowflake.
Time to first insight
~4 wks
Map the estate Monday, dashboards by month-end.
Industries served
6+
Manufacturing, banking, retail, telco, services, insurance.
Microsoft partnership
Data & AI
One of four Solutions Partner designations Near Contact holds.
Operating since
1995
30+ years of muscle behind Grupo Open.
01 · Why this exists
![[Team photo — data engineering review]](assets/images/pexels-tima-miroshnichenko-5439368.jpg)
Mexico City, MX. A data and ML engineering review — placeholder, client to confirm.
Most AI initiatives die in proof of concept. The ones that ship have data engineers behind them — not a slide deck and a vibes-based ROI.
We staff the unglamorous half of AI — ingestion, modeling, lineage, monitoring — alongside the applied ML and LLM work. That's the part that turns a demo into a system you can audit a year from now.
02 · Use cases
Six analytics practices. All in production.
We've implemented these across manufacturing, banking, retail, telco, services, and insurance — typically behind a real user-facing product, with evals and monitoring already wired up.
Fraud & Churn Analytics
- Customer churn analytics
- Fraud detection analytics
- Customer critical-touch-point analytics
Market Analytics
- Unmet-need analytics
- Demand forecasting
- Market-trend analytics
- Non-customer analytics
- Pricing & channel analytics
Customer Analytics
- Lifetime-value analytics
- Customer segmentation
- Sales-channel analytics
- Engagement analytics
- Acquisition analytics
Employee Analytics
- Capability analytics
- Capacity analytics
- Employee-churn analytics
- Recruitment-channel analytics
Operational Analytics
- Supply-chain analytics
- Core-competency analytics
- Capacity-utilization analytics
- Environmental-impact analytics
03 · Why nearshore
Pilots don't ship. Pipelines do. That's the difference between a slide and a system.
Operating since 1995. Backed by Grupo Open — managed IT, datacenter, and software services for Mexican enterprise customers for three decades.
01
Time-zone alignment, not handoff
Mexico hours overlap entirely with US working hours. Stand-ups happen in real time. No offshore "we'll catch up tomorrow" cycle.
02
Engineers, not slideware
Every engagement leads with a data engineer, not a strategy consultant. Models ship behind a real product surface — with evals, monitoring, and a retraining cadence.
03
US contract, Mexico operation
You sign with a US entity, in US jurisdiction. We absorb Mexican labor, tax, and HR complexity — including USA Corporate Insurance for general, professional, cyber, and crime liability.
04
30 years of operating muscle
Backed by Grupo Open's track record across managed help desk, datacenter, application support, and software development for Mexican enterprise customers since 1995.
— Teams who have embedded our engineers
HEINEKEN
FEMSA
WHIRLPOOL
TOYOTA TSUSHO
CHUBB
GRUMA
04 · What customers say
We had three years of stalled AI initiatives and one new CFO. They had Fabric ingesting and a forecasting model in production within a quarter. First time the model was the boring part of the project.
Anita Chen [placeholder]
Head of Data · Fortune 1000 retailer
06 · FAQ
Questions buyers ask before the call.
The same six we hear every week. If yours isn't here, the discovery call answers it in fifteen minutes.
01 Do you build ML or just data pipelines?
Both, in sequence. Most engagements start with the pipeline work — ingestion, contracts, lineage — because that's what makes the model usable. Applied ML and GenAI work follows once the data layer is honest.
We'll tell you in the mapping sprint if you don't have the data foundation to justify the ML work yet. That's a quarter of mapping engagements.
02 Which platforms are you deepest in?
Microsoft Azure and Power BI — we hold the Microsoft Data & AI Solutions Partner designation. AWS analytics services for AWS-native estates. Azure OpenAI and Amazon Bedrock for GenAI work.
If your stack is Google Cloud or fully Snowflake-native, ask — we'll be honest about which side of the team is deepest there.
03 What does "production AI" actually mean here?
A model is in production when: it's behind a real user-facing surface, it has an eval suite that runs on every change, it has drift monitoring, and there's a named owner for retraining. If those four boxes aren't ticked, we call it a pilot.
04 How do you handle data security and residency?
Engineers work in your cloud, your tenant, your VPN — not ours. Data never leaves your environment. Access is RBAC-controlled and logged. SOC 2 Type II program in place; HIPAA-ready for regulated engagements.
05 Can you take over an existing data platform mid-flight?
Yes — it's a common entry point. A two-week handover audit gets us oriented in the pipelines, lineage, and on-call rotation. We've taken over Databricks estates, post-migration Snowflake setups, and one half-built Fabric lakehouse this year.
06 How does billing work?
Mapping sprint is fixed-price. Build and deploy phases are either fixed-price (preferred) or monthly pod retainer. Operate phase is a monthly retainer at a reduced rate. All billed in USD against the US entity.
Got data and a backlog of AI promises? We can ship the next one.
A 30-minute call gets you a written mapping plan: estate audit, ranked use cases, fixed budget for the first sprint. If the data isn't ready yet, we'll say so.