Data Engineering & AI | Ndakum Consulting Services

Our services

Comprehensive data & AI capabilities

From raw data to production ML — all under one roof.

↻

Data pipeline engineering

Built to keep running even when upstream schemas change without warning.

Batch & real-time pipelines
Schema validation & error handling
Automated data quality checks
Airflow & dbt orchestration

🏠

Cloud data warehouse & lakehouse

One governed source of truth your team can actually query.

Snowflake, BigQuery, Redshift
Lakehouse architecture design
Data modeling & schema design
Query performance optimization

📊

BI dashboards & reporting

Built around the decisions your team makes weekly, not vanity metrics.

Looker, Tableau, Power BI
Self-serve dashboard design
Automated report distribution
Executive & operational views

🧠

ML model development

Models built for your actual data and use case, not a generic template.

Model design & feature engineering
Classification, forecasting & NLP models
Model evaluation & validation
Explainability & bias testing

⚙

MLOps & model deployment

Models that ship into real workflows, not a notebook on someone's laptop.

CI/CD for ML models
Model monitoring & drift detection
Automated retraining pipelines
A/B testing infrastructure

⚖

Data governance & quality

Clear ownership and access controls so the audit isn't a scramble.

Data lineage tracking
Access control & row-level security
Data quality monitoring
Metadata & catalog management

⚡

Real-time data streaming

Data that's current the moment it matters, not the next morning.

Kafka & streaming pipelines
Real-time dashboard updates
Event-driven architecture
Low-latency processing

🔄

Data migration & integration

Consolidates scattered systems into one connected platform.

Legacy system migration
Third-party data integration
API & webhook ingestion
Historical data backfill

🔮

Predictive analytics

Forward-looking insight built on your own historical data.

Demand & revenue forecasting
Churn & risk prediction
Anomaly detection
Scenario & what-if modeling

Integrations

Works with your stack

Connects to the data tools you already run.

Warehouses

Cloud data platforms

SnowflakeBigQueryRedshift

BI

Reporting & dashboards

LookerTableauPower BI

Orchestration

Pipeline & workflow tools

AirflowdbtFivetran

Our process

From audit to production

A focused rollout, typically live in weeks to a few months depending on scope.

STEP 01

Data audit & strategy

We inventory your existing data sources and define what a governed platform should look like for your business.

STEP 02

Pipeline & warehouse build

We build the pipelines and warehouse architecture that will carry your reporting and ML workloads.

STEP 03

Dashboard & model development

We build the specific dashboards and models tied to real business decisions.

STEP 04

Testing & validation

We validate data accuracy and model performance before anything goes into production.

STEP 05

Ongoing monitoring & iteration

We monitor pipeline health and model performance, and iterate as your data evolves.

Trust

Built for data governance

A platform designed to hold up under scrutiny, not just under a demo.

Data lineage

Full traceability from raw source to final dashboard or model.

Access control

Row-level and column-level security where it matters.

Quality monitoring

Automated checks that catch bad data before it reaches a report.

Compliance-aware

Architecture designed to support HIPAA, SOC 2, or industry-specific needs.

Turn scattered data into AI you can ship.