Skip to main content
stackloader
  • Home
  • Services
  • Portfolio
  • Technologies
  • About
Get started
stackloader
  • Home
  • Services
  • Portfolio
  • Technologies
  • About
Get started
stackloader

AI-Driven Code, Human-Centric Impact.

Product

  • Features
  • Integrations

Company

  • About
  • Blog
  • Careers
  • Contact

Legal

  • Privacy Policy
  • Terms of Service

© 2026 stackloader, Inc. All rights reserved.

Built with precision.

We use cookies

To improve your experience. Cookie policy

Data & AI

Data Engineering & ML

From raw events to actionable intelligence

We build the data pipelines, warehouses, and ML systems that turn your telemetry into a competitive advantage. Whether you need real-time analytics or a production ML model, we handle the full stack.

Explore data solutionsSee our work

What it is

The full picture

Real-time data pipelines

Event-driven architectures with Kafka and Flink that process millions of events per second with sub-100ms latency.

ML model development

End-to-end model development — from feature engineering to production deployment with A/B testing and drift monitoring.

LLM integration & RAG

Retrieval-augmented generation systems that give language models accurate, up-to-date knowledge of your domain.

Data warehouse design

Snowflake, BigQuery, and dbt implementations that make your analysts self-sufficient and your dashboards fast.

Who it's for

Right for you if…

Data teams drowning in pipeline incidents, and product teams whose AI features are held back by poor data quality or latency.

Our approach

How we work

  1. 01

    Data landscape assessment

    We map your current data flows, identify bottlenecks, and assess the quality and freshness characteristics of your key datasets.

  2. 02

    Architecture design

    We design a data architecture matched to your latency, cost, and team-size constraints — streaming, batch, or hybrid.

  3. 03

    Pipeline engineering

    Event-driven pipelines with Kafka and Flink, dbt-based transformations, and CDC replication from operational databases.

  4. 04

    ML deployment

    Model training infrastructure, A/B test routing, and drift monitoring — models that stay accurate after you stop paying attention.

Tech we use

The toolbox

Backend

Python

Database

PostgreSQLRedis

AI & ML

PyTorchOpenAI APILangChain

Sample deliverables

What you receive

  • Event streaming architecture with documented topology
  • dbt transformation layer with lineage documentation
  • ML model deployed to production with A/B test framework
  • Real-time data dashboards with SLA monitoring
  • Data quality framework with automated alerting

Related work

Projects using this service

FinFlow · Financial Technology

Rebuilding FinFlow's Real-Time Data Platform

FinFlow's legacy batch-processing pipeline couldn't keep pace with their 40 million daily transactions. We rebuilt it as an event-driven system that processes data in under 200ms — unlocking real-time fraud detection and live P&L dashboards.

<200ms

Processing latency

−94%

Pipeline incidents

Read case study

DataPulse · Business Intelligence

DataPulse: Natural Language Analytics for SaaS Teams

DataPulse wanted to let non-technical customers query their data warehouse in plain English. We built a RAG-powered analytics layer that translates natural language questions into accurate SQL — with explainability built in.

71%

30-day retention

47s

Time-to-insight

Read case study

FAQ

Common questions

We embed AI at every stage of your development cycle — not as a layer on top, but as part of how we work. That means AI-assisted code generation trained on your codebase, automated pre-review of every pull request, intelligent refactoring tools for legacy code, and LLM-powered features inside your product itself. We always start with your specific context rather than applying generic AI tooling.

We offer full-stack product engineering, AI and LLM integration, cloud infrastructure and DevOps, UX and design systems, security audits and compliance support, and blockchain and smart-contract development. Most clients engage us for a combination of these — we rarely work on just one layer because the best systems are designed cohesively across the stack.

Both. We work with seed-stage founders who need to build their first production system alongside Series B and C teams adding a specialist practice they don't have in-house. The common thread is that our clients are serious about what they're building — budget-conscious experimentation isn't a great fit for the way we work.

Explore data solutions

Ready to start?

Tell us about your project and we'll have a proposal ready within 48 hours.

Start a conversationSee case studies