AI & Data Engineering That Delivers Business Outcomes
From model training to production deployment, we build intelligent systems that actually ship. Data pipelines, LLM integrations, and ML infrastructure that scales with your ambition.
Your data and AI initiatives shouldn't stall at the prototype stage. We engineer production-grade intelligent systems — from real-time data pipelines to custom LLM applications — that create measurable business value, not just impressive demos.
Book a CallWhy AI & data projects fail to deliver ROI
Your AI prototype never made it to production
The Jupyter notebook demo looked great. Six months later, it's still not in your product. The gap between a data science experiment and a reliable production feature is where most AI companies lose momentum — and investor confidence.
Your data infrastructure can't support your AI ambitions
Your models need clean, real-time data. Your pipelines deliver stale, incomplete, duplicated data. Every time your data team tries to build something new, they spend 80% of their time fighting infrastructure instead of creating value.
You're burning through cloud credits with no optimization
GPU costs are spiraling. Model inference is expensive. Your training runs waste compute on bad hyperparameters. Without proper ML infrastructure, you're paying 3x what you should for every prediction your model makes.
Your team can build models but can't ship products
You have brilliant data scientists, but nobody who can build the API, the monitoring, the feature store, or the retraining pipeline. The "last mile" of AI — from model to product — is where your team gets stuck.
What we build for AI & data companies
End-to-end engineering for companies where data and AI are the product, not just a feature.
Production ML Systems
Model serving infrastructure, A/B testing frameworks, automated retraining pipelines, and monitoring systems. We take your models from notebooks to production with proper MLOps practices.
Data Pipeline Architecture
Reliable, scalable data infrastructure using Airflow, Spark, dbt, and streaming technologies. Real-time and batch pipelines that deliver clean data to every team that needs it.
LLM Application Development
Custom LLM applications with RAG, fine-tuning, and prompt engineering. Built for accuracy, cost-efficiency, and user adoption — not just impressive demos.
Analytics & BI Infrastructure
Data warehouses, semantic layers, and self-service analytics platforms. We build the infrastructure that lets your entire organization make data-driven decisions.
Feature Stores & ML Infrastructure
Centralized feature computation, storage, and serving for your ML models. Consistent features between training and inference, with versioning and monitoring built in.
Data Quality & Governance
Automated data validation, lineage tracking, access controls, and compliance frameworks. Trust your data so your stakeholders can trust your insights.
Our AI & data stack
ML/AI
Data Processing
Orchestration
Storage
Streaming
Infrastructure
How we deliver for AI & data companies
Architecture Assessment
We audit your current data and ML infrastructure. We identify bottlenecks, reliability issues, and missed optimization opportunities. You get a prioritized roadmap with clear ROI estimates.
Foundation Build
We establish reliable data pipelines, proper orchestration, and core ML infrastructure. This foundation eliminates the "firefighting" and lets your team focus on high-value work.
Production Hardening
We build monitoring, alerting, cost optimization, and automated recovery. Your AI systems become reliable production services, not fragile experiments.
Scale & Transfer
We optimize for growth, document everything, and train your team. The infrastructure becomes yours to extend and evolve independently.
Why Pletava
Full-stack AI engineers, not just data scientists
Our engineers build the API, the infrastructure, the frontend, AND the ML pipeline. No handoff gaps. No "that's not my job." One team that owns the entire stack.
Production mindset from day one
We don't build experiments. We build production systems with monitoring, fallbacks, cost controls, and SLAs. Every feature we ship is built to run reliably at scale.
Cost-optimized AI infrastructure
We've reduced cloud costs by 40–60% for AI companies through proper GPU utilization, model optimization, caching strategies, and smart infrastructure choices.
Frequently Asked Questions
Can't find what you're looking for? Book a call and we'll answer everything.
Book a CallCan you work with our existing data science team?
That's our preferred model. We complement your data scientists with production engineering skills — building the infrastructure, APIs, and deployment systems they need to ship their models.
What if we're starting from scratch with AI?
We help AI-first startups establish their entire data and ML infrastructure from the ground up. We've done it multiple times and know how to build foundations that scale.
How do you handle data security and privacy?
We implement encryption, access controls, PII handling, and audit trails from day one. We've worked with GDPR, HIPAA, and SOC 2 requirements across multiple AI companies.
What's the typical engagement timeline?
Quick infrastructure wins in 2–4 weeks. A solid production foundation in 2–3 months. Ongoing scaling and optimization as your product grows. We scope precisely during discovery.
Your AI should be in production, not in a notebook.
Talk to engineers who bridge the gap between data science and production.
Thrilled to meet you!
Let's talk possibilities