Case Studies

— production platforms, measurable outcomes

Representative deliveries across streaming, lakehouse modernization, data trust programs, AI/RAG, and automation (n8n). Each case is written as a practical blueprint: constraints → decisions → validation → operations → results.

Discuss a comparable project Request an assessment

Reliability gains

fewer incidents, higher freshness, improved SLAs

Performance & cost

faster queries, lower spend, predictable scaling

AI quality controls

eval gates, guardrails, traceability

We show the "how" — architecture, ops, and governance

Each case focuses on decisions, operating model, and what made it safe to run in production.

Constraints and risks documented upfront

Target architecture and ADR discipline

Validation, observability, and operational readiness

Outcomes measured by agreed metrics

Why work with us

Six pillars that define how we deliver production systems

Cases map to business outcomes

Measurable results tied to real business value.

latency
freshness
incident rate
cost
delivery velocity

Architecture decisions are explicit

Clear documentation of every major decision.

contracts
boundaries
scaling choices
trade-offs

Operations are included

Not just build — run is part of every case.

SLOs
dashboards
runbooks
incident workflow

Data trust is engineered

Quality and governance are first-class.

DQ checks
lineage
ownership
governance

LLM in production is measurable

AI systems with real quality metrics.

eval harness
guardrails
privacy controls

Automation reduces ongoing cost

n8n workflows that pay for themselves.

n8n workflows
approvals
reporting
MTTR reduction

Our delivery process

A proven approach to building production systems

Step 1 — Identify comparable constraints

Match your situation to relevant case patterns

Step 2 — Map to reference architecture

Apply proven blueprints to your context

Step 3 — Validate feasibility and rollout approach

Plan safe implementation with clear milestones

Step 4 — Execute with measurable milestones

Deliver with outcomes you can verify

Request a platform blueprint

Platform-focused engineers

Data Reliability Engineer

Builds DQ gates, monitors, lineage, and incident workflow.

Core skills

DQ checks, observability, governance, incident response

Production focus

incident reduction, SLA enforcement

View profile

Principal Data Architect

Leads architecture decisions and operating models for multi-team platforms.

Core skills

reference architectures, governance, cost and reliability trade-offs

Production focus

scalable patterns, alignment, execution plans

View profile

Engagement models

Flexible approaches to match your needs

Architecture Workshop (case-based)

Best for

learning from comparable cases

Includes:

pattern mapping + architecture review + roadmap

Delivery Squad

Best for

full implementation

Includes:

engineers + governance + SLOs + continuous improvement

Stability & Cost Sprint

Best for

quick wins and incident reduction

Includes:

assessment + fixes + runbooks + monitoring

Representative case studies

Production deliveries with measurable outcomes

Case 01 — Streaming Platform Stabilization

Problem

Consumer lag spikes + missing events + unclear replay strategy

Delivered

Event contracts, DLQ/quarantine, replay tooling, SLO dashboards, load tests

Read case study

Case 02 — Lakehouse Modernization (Iceberg/Delta)

Problem

Slow queries + high storage cost + unreliable partitions

Delivered

Table redesign, compaction strategy, dbt tests, cost governance

Read case study

Case 03 — Data Trust Program

Problem

Recurring broken dashboards and silent pipeline failures

Delivered

DQ rules, ownership model, incident workflow, monitors, lineage patterns

Read case study

Case 04 — RAG Assistant for Internal Knowledge

Problem

Slow support resolution + inconsistent answers + policy constraints

Delivered

Retrieval pipeline, eval harness, guardrails, monitoring, access controls

Read case study

Case 05 — Real-time Analytics for Ops Decisions

Problem

Dashboards lagging hours behind, no freshness SLA

Delivered

Streaming aggregation + serving layer + freshness SLA + alerting

Read case study

Case 06 — n8n Automation for Data/AI Ops

Problem

Manual reporting, inconsistent approvals, slow incident response

Delivered

Audited workflows, approval gates, exec reporting, incident automation

Read case study

Frequently asked questions

FOUNDRY

Production AI & Data Platforms — streaming, lakehouse, and LLM systems built for real operations.

Navigation

Legal

Production-grade AI & Data engineering