Turn Your Data Into a
Real-Time Decision Engine
We design, build, and scale modern data platforms, AI systems, and analytics workflows—so your teams can move faster, make better decisions, and unlock real business value.
Trusted by leading brands across industries
From Data Chaos to Production Systems
Most team don’t need more tools — they need systems that connect data, decisiones, and action.
Align to the
Business Problem
We start with the decision — not the data.
What needs to improve?
Revenue, risk, operations, customer experience.
Architect the
Right System
We design across the full stack — data pipelines, models, APIs, and interfaces — so everything works together in production.
Build & Ship
to Production
Our teams embed directly with yours to deliver production-grade systems — fast, scalable, and built for real usage.
This is how we move teams from dashboards and pilots to systems that actually run the business.
WHERE WE WIN
Two high-value motions. Both in production.
We’ve built our practice around the two AI and automation use cases with the clearest enterprise ROI and the sharpest talent scarcity. This is where we go deep.
From AI Pilot to Production System
Most enterprise GenAI initiatives are stuck in prototype mode — impressive demos, no production path. Distillery builds what comes next: grounded retrieval systems, production LLMOps pipelines, agentic applications that operate across real enterprise data. We handle the hard part: retrieval quality, data grounding, model evaluation, governance, and the integration work that turns a model into a reliable business application.
RAG architectures grounded in enterprise data
Agentic AI applications and multi-agent orchestration
LLMOps pipelines: evaluation, monitoring, fine-tuning
AI-native copilots and document intelligence systems
Production GenAI on Azure OpenAI and Azure AI Search
Support-cost reduction and workflow augmentation systems
Pilots stuck in sandbox · Retrieval quality problems · No path to production · Scarce LLMOps talent · Lack of data grounding
AI Agents That Actually Work in Your Workflows
Traditional RPA is hitting its limits. Rule-based bots break when processes change, exceptions pile up, and manual handoffs eat margin. The market is entering a replacement cycle — and AI agents are ready to replace brittle automation with governed, intelligent orchestration. Distillery builds AI agent systems that integrate across enterprise platforms, handle complex decision logic, and keep humans in the loop where it matters.
AI agent orchestration across business workflows
Intelligent document and data processing pipelines
Human-in-the-loop automation with governed escalation
Cross-system integration using Azure AI Foundry and Logic Apps
Back-office automation for finance, ops, and shared services
Agent monitoring, observability, and failure handling
Pilots stuck in sandbox · Retrieval quality problems · No path to production · Scarce LLMOps talent · Lack of data grounding
WHAT MAKES IT WORK
The full-stack engineering depth
behind every engagement
Production AI and agentic automation don’t run on air. They run on clean data, governed pipelines, solid integration, and engineers who’ve done this before.
Batch and real-time pipeline design, data lake and warehouse architecture, ingestion, transformation, and orchestration across the modern data stack.
Semantic Modeling & Enterprise Data Access
Governed metric layers, dimensional modeling, AtScale and dbt implementation, consistent data definitions across teams and tools.
API & Systems
Integration
Enterprise system integration, REST/GraphQL APIs, event-driven architectures, and the middleware work that AI agents actually need to function.
Azure-native delivery across AI Foundry, Azure OpenAI, Synapse, Fabric, and Data Factory. We speak Microsoft fluently and move fast inside the Azure ecosystem.
Data quality frameworks, lineage, monitoring, access control, and compliance-ready delivery for regulated industries.
AI Application
Engineering
Full-stack AI application development — from backend ML infrastructure to the product surfaces that make AI useful to end users.
PROOF POINTS
Enterprise delivery. Real proof
AI systems designed to integrate cleanly with your data, workflows, and infrastructure.
E-Commerce / Retail
Data foundation for scale at eBay
Distillery’s engineers have supported enterprise-scale data platform work for one of the world’s largest e-commerce marketplaces — embedding senior engineers into complex, high-volume data environments.
Health & Wellness / Retail
Analytics and data engineering at Thrive Market
Delivered data engineering and analytics capabilities for a high-growth DTC brand navigating complex data environments and a need for faster, more reliable reporting.
Platform Fluency
Semantic Layer
Expertise via AtScale
Databricks-Adjacent
Lakehouse Delivery
Azure OpenAI and AI
Foundry Implementations
Snowflake Data Cloud
& Warehousing
dbt and Modern
Analytics Engineering
DELIVERY MODEL
We build what strategy firms design.
We accelerate what internal teams can’t staff
Distillery isn’t a consultancy with a point of view and no builders. We’re not a staffing firm with no institutional knowledge. We’re something more specific: a technical execution partner that embeds senior engineers into real programs and builds production systems.
After the strategy firm
McKinsey, Deloitte, Slalom, or BCG defined the roadmap. Distillery builds it.
Alongside internal teams
Your engineering team is stretched. Distillery augments with senior specialists in 10–14 days.
As the primary delivery partner
You need a production AI or automation program built. We own the execution.
Supporting platform migrations
Your SI is migrating infrastructure. Distillery handles the data modeling, semantic layer, and AI readiness work they don’t.
10–14 days from signed statement of work to embedded engineers.
Industry average: 3–6 months.
EXPANSION CAPABILITIES
Broader data modernization
once the foundation is in place
Predictive Analytics
& ML
Once Distillery is in the account and the data foundations are clean, predictive use cases are fast to frame: churn, demand forecasting, asset health, and risk scoring. We bring the engineers who’ve done it in production.
BI Modernization & Executive Analytics
Dashboard sprawl, inconsistent metrics, and semantic debt are expensive. We modernize BI environments, build governed self-service layers, and clean up the semantic models that Power BI and other tools depend on.
ENGAGEMENT MODEL
Fast to start. Embedded to deliver
01
Technical Discovery
We dig into your stack, your use case, and your constraints. No generic assessments.
02
Solution Framing
We define the build scope, talent requirements, and delivery approach.
03
Team Design
We assemble the right engineers for the problem — roles, seniority, domain fit.
04
Fast Onboarding
Engineers embedded and productive in 10–14 days.
05
Delivery
Production-focused, milestone-driven, with your team inside the work — not watching from the outside.
Engagements typically run 6–18 months. We’re not designed for one-week scoping sprints — we build production systems that require real implementation depth.
Planning a production AI or automation initiative?
Let’s get specific.
Tell us what you’re building. We’ll tell you what the team looks like, how fast we can start, and what it costs to do it right.