You have data but no decisions.
You accumulate sales, customer, operations, and production information for years, but decisions are still made on intuition or with reports that arrive too late. Value is latent in data nobody is analyzing rigorously.
// services / ai and big data
Data turned into decisions.
We turn your company's information volume into competitive advantage. We implement machine learning, NLP, computer vision, and business intelligence with measurable impact on cost and decision-making. Predictive models for demand and risk, enterprise chatbots, automated document analysis, real-time executive dashboards, and end-to-end data pipelines. 100% source code ownership, full documentation, and extended support under contractual SLA.
// when
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When investing in AI and Big Data makes sense.
Not always. Before quoting, we validate that available data, quality, and use case justify the investment. These are the typical signals that AI or advanced analytics solve more than they cost.
Your support team is saturated.
Support answers the same 50 questions every day. Sales loses leads from not responding fast. A well-designed chatbot can absorb 60-80% of volume without sacrificing service quality.
Expensive, error-prone visual processes.
Manual QC on production lines, invoice and document OCR, 24/7 surveillance with operators. Computer vision can cut errors 90% and costs 70% on repetitive processes.
Gut-feel forecasts that cost millions.
Excess inventory, lost sales, reactive maintenance. Predictive demand models improve forecasts 20-40% and pay for themselves in 6-12 months with working-capital savings.
Excel dashboards that break every month.
Executive reporting depends on one analyst who pastes data from five sources into Excel every Monday. When they take vacation, it breaks. Automated pipelines and BI dashboards remove that bottleneck.
// sub-services
What we build within this discipline.
Five types of solutions that cover most AI and Big Data projects. Two or three are almost always combined within the same project.
Predictive Machine Learning
Predictive models for demand, risk, maintenance, and customer retention. Real-time fraud prediction. Typical ROI: 20-40% improvement in forecasts.
NLP and chatbots
Multilingual enterprise chatbots, social sentiment analysis, automated document classification, and information extraction from long-form text.
Computer Vision
Automated quality control, intelligent OCR, real-time video analytics, face and object recognition for industrial processes.
Business Intelligence
Real-time executive dashboards, automated reporting, multidimensional analytics. We connect your sources with visualizations that tell your business story.
Data pipelines and MLOps
End-to-end data engineering: ETL/ELT, data lakes, real-time streaming, MLOps to deploy and monitor models in production.
// stack
Stack for AI and Big Data.
For AI and data projects we pick mature frameworks with active communities and long-term support. Open source whenever possible, cloud-managed when it delivers real velocity.
// ml frameworks
- TensorFlow
- PyTorch
- scikit-learn
- Keras
- XGBoost
// big data
- Apache Spark
- Kafka
- Airflow
- dbt
// data storage
- Snowflake
- BigQuery
- Redshift
- Delta Lake
// visualization
- Power BI
- Tableau
- Looker
- Metabase
// cloud ML
- AWS SageMaker
- Azure ML
- Vertex AI
- MLflow
// how we work
From data to model in production.
Four visible phases. We work in 2-week sprints with functional demos each sprint. Zero surprises.
- 01 Data assessment Quality, availability, and governance audit. Use case justified in ROI terms. 1–2 wk
- 02 Data engineering ETL/ELT pipelines, cleansing, feature engineering, data lake/warehouse. 2–4 wk
- 03 Training and validation Iterative modeling, clear metrics, validation with real data, baseline comparison. 4–12 wk
- 04 Deployment and MLOps Model in production, drift monitoring, automated retraining, dashboards. 2 wk + ongoing
// guarantees
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What's included by contract.
Four guarantees formalized in every proposal. Not marketing words, contract clauses.
Code ownership.
Source code and trained models are 100% client-owned. No hidden licenses, no proprietary dependencies, no vendor lock-in. Full Git repository delivered at closeout.
Full documentation.
Architecture, pipelines, models, metrics, deployment runbooks, and user manuals. Available in Spanish and English. Up-to-date through the last day of the project.
Training included.
Sessions for end users, analysts, and technical teams. On-site or remote with recorded material for later onboarding of new staff.
Extended support.
Maintenance plans with contractually-defined SLAs. Drift monitoring, scheduled retraining, and monthly performance reports.
// related cases
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AI and data solutions already running in production.
Predictive analytics in logistics
Demand prediction and route optimization models for a transport fleet. Operating-cost reduction and improved capacity forecasts.
+30% forecast accuracy
─18% route cost
Enterprise chatbot for insurance
Multilingual chatbot for policy and claims support, integrated with CRM and internal systems. 24/7 coverage with automated human escalation.
─70% support time
24/7 coverage
// frequently asked
The questions we hear most about AI and Big Data.
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How long does it take to deploy a machine learning model in my company?
A typical ML project goes through 4 phases (data assessment, engineering, training, deployment) and takes between 8 and 20 weeks. Simple predictive models can be launched in 6-8 weeks; enterprise computer vision or NLP systems typically require 16-24 weeks.
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Power BI or a custom dashboard?
Power BI works very well for standard reporting with clean data and analytical users. A custom dashboard makes sense when: (1) data comes from many proprietary sources, (2) you need specific embedded business logic, (3) non-technical users must take actions from the view, (4) per-user licensing cost at scale is prohibitive.
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What is the typical ROI of implementing AI in a company?
It depends on the case: predictive sales analytics typically improves forecasts by 20-40%, chatbots reduce support time by 70%, computer vision in industrial QC reduces errors by 90%. Typical payback in well-defined projects ranges from 6 to 18 months.
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Is my data ready for AI?
Few companies are at the start. That's why we always begin with a data maturity assessment (1-2 weeks) that maps quality, availability, and governance. From there we design what data engineering is a prerequisite before the models.
// let's talk
Ready to turn your data into decisions?
30 minutes, no commitment. Tell us what you need to solve and we'll tell you exactly what to build, how much it costs, and how long it takes. Detailed proposal within 3-5 business days after the first call.