Six production systems. One engineering team.
Every service Pie Data delivers ships as a working, production-ready system — not a prototype. From raw pipeline to deployed ML model, we own the engineering so you own the outcome.
Each service line, built to run in production
Data Engineering
ML Models & Deployment
Agentic AI Automation
End-to-end pipeline architecture, orchestration, and cloud data warehouse builds. We eliminate ingestion bottlenecks and guarantee data quality at scale before any model touches the data.
Supervised, unsupervised, and time-series models trained on your data and deployed to production APIs — with monitoring, retraining hooks, and documented performance baselines included.
Multi-step AI agents that close the loop between data signals and operational actions — automating decisions in sales, ops, and support workflows without human handoffs slowing the cycle.
Marketing Analytics
NLP & Generative AI
BI & Analytics Engineering
Fine-tuned language models, retrieval-augmented generation pipelines, and document intelligence systems built on your proprietary data — not generic API wrappers over commodity models.
Semantic layer design, dbt model architecture, and dashboard builds wired to governed data sources — so every metric your team sees reflects the same definitions and the same source of truth.
Attribution modeling, LTV prediction, and campaign ROI pipelines wired directly into your ad platforms. Revenue impact is measurable within the first reporting cycle after go-live.
AWS (Redshift, Glue, SageMaker) — GCP (BigQuery, Vertex AI, Dataflow) — Azure (Synapse, ADF, Azure ML)
The exact tools we deploy
Apache Airflow — Prefect — dbt — Fivetran — Kafka — Spark — Delta Lake
We specify tooling upfront so your team knows what they're inheriting. No surprise vendor lock-in, no proprietary middleware you can't maintain after the engagement ends.
PyTorch — scikit-learn — XGBoost — Hugging Face — LangChain — MLflow — Ray
Looker — Tableau — Power BI — Metabase — Superset — dbt Semantic Layer
Structured for your stage and scope
Diagnostic Sprint
Embedded Sprint Team
Full-Stack Ownership
Ongoing pipeline ownership, model maintenance, and feature development under a structured SLA. We operate as your data and AI engineering function until your in-house team is ready to take over.
A two-week scoped audit of your current data infrastructure, model readiness, and pipeline gaps. Ends with a prioritized technical roadmap and cost estimate — no retainer required.
A dedicated squad of engineers and data scientists integrated into your delivery cycle for 8–16 weeks. Scoped milestones, weekly output reviews, and production deployments on your timeline.