Outcome before technology
We start with the business result you are accountable for. The platform conversation comes second, and sometimes concludes that you already have what you need.
About
Senior architecture judgment, without the layers in between.
For Each Group LLC is an independent consultancy founded on a simple premise: the person who assesses your environment should be the person who designs the target state and reviews the work that gets you there.
Founder & Principal Architect
Rodney Elmore
Rodney Elmore is the founder and principal architect of For Each Group LLC, where he advises enterprises on cloud platforms, enterprise data and analytics architecture, and the design and productionization of AI systems.
His work sits deliberately at the intersection of strategy and delivery. Roadmaps that no one can build from are as unhelpful as code written without a target architecture, so engagements are structured to produce both: decisions leadership can defend, and artifacts engineers can execute. That means reference architectures, decision records, data contracts, and working reference implementations — not a deck that expires the week it is presented.
Rodney founded For Each Group to work differently from the large-integrator model. Engagements are kept few and deep, so clients get continuity of thought rather than a rotating bench. Recommendations are vendor-neutral, grounded in the estate a client actually has rather than a reference architecture written for someone else. And every engagement is designed to reduce dependence on the consultancy over time — the clearest signal of success is an internal team that no longer needs the help.
Outside of client delivery, he focuses on the operating side of architecture: how standards get adopted rather than ignored, how review forums stay useful instead of ceremonial, and how organizations build the internal capability to make good architectural decisions without external input.
Operating principles
How we make decisions
Four commitments that shape every engagement, and that you are welcome to hold us to.
Write it down
Tradeoffs, alternatives considered, and the reasoning behind each decision are documented. Institutional memory should not depend on who is still in the room.
Design for the operators
An architecture your team cannot run is a liability. Operability, cost transparency, and supportability are design constraints, not afterthoughts.
Build ourselves out of a job
Enablement is part of delivery. We measure success by how quickly your internal team can carry the platform forward alone.
Industries served
Where our work fits
We work with organizations where data volume, regulatory scrutiny, or operational complexity make architecture decisions consequential.
Financial Services & Insurance
Regulated data platforms, risk and finance reporting, model governance, and AI use cases that must withstand examination.
Healthcare & Life Sciences
Protected health information handling, interoperability, clinical and operational analytics, and research data platforms.
Manufacturing & Industrial
Operational technology and IT convergence, supply chain visibility, quality analytics, and predictive maintenance.
Retail & Consumer
Customer data unification, demand and inventory analytics, personalization, and forecasting at seasonal scale.
Professional & Business Services
Practice and engagement analytics, knowledge retrieval assistants, and automation of document-heavy workflows.
Public Sector & Education
Cloud modernization under procurement and compliance constraints, records management, and transparent reporting.
Azure
AWS
Microsoft Fabric
Power BI
Databricks
Snowflake
Synapse
Data governance
Generative AI
MLOps
Measurable outcomes
What we hold ourselves to
Every engagement defines its success measures up front, in writing, before work begins. These are the dimensions we typically commit to.
Time to insight
Reduce the lead time from question asked to trustworthy answer delivered.
Platform cost
Lower run-rate through right-sizing, workload isolation, and eliminated duplication.
Release cadence
Move data and model changes from quarterly events to routine, automated releases.
Model uptime
Keep production AI and ML services monitored, supported, and retrainable on demand.
Audit readiness
Lineage, access controls, and decision records available on request rather than reconstructed.
Report consolidation
Collapse duplicated datasets into certified, governed semantic models teams actually reuse.
Incident reduction
Fewer data quality and pipeline incidents through contracts, testing, and monitoring.
Team capability
Internal architects and engineers able to extend the platform without external help.
Add your own engagement figures and case studies here before launch.
Let’s talk about your architecture.
Whether you need a second opinion on a decision already made or a partner for the next eighteen months, the first conversation costs nothing.