Make consequential platform decisions with less risk

Principal-level architecture consulting for companies modernizing valuable software, stabilizing integrations, adopting AI, or adding sustained technical leadership. Start with a bounded engagement and expand only when the evidence supports it.

Based in Fresno, serving Central California businesses and remote teams across the US. Learn more about regional architecture consulting.

Start with the business problem

Choose the decision you need to make

The right engagement depends on the risk and decision in front of the business, not a technology label. These are the three most common starting points.

A major platform decision needs scrutiny

Reduce risk before committing budget

Get an evidence-based recommendation on whether to stabilize, migrate, build, buy, phase, or stop.

See the Architecture Review →

Critical systems disagree or fail between boundaries

Make integrations recoverable

Define ownership, identifiers, failure behavior, and operational workflows across commerce and enterprise systems.

See the Integration Audit →

A repeated workflow may be worth automating

Validate the AI opportunity first

Test the business case, data boundaries, controls, and failure path before choosing a model or building a pilot.

See the AI Assessment →

Bounded starting points

Start with a focused engagement

Not every platform problem should begin with a large implementation. These bounded engagements provide the evidence, architecture, and next-step recommendation needed before committing more budget.

See how this judgment applies in practice: a database migration without downtime and a commerce platform built around capabilities.

Diagnose and decide

Build evidence before committing to a larger technical investment.

Platform Architecture Review

Reduce risk before a consequential architecture, modernization, migration, vendor, or build-versus-buy decision.

Best fit: A team needs independent technical judgment before committing more budget to a platform decision.

Includes

Stakeholder and technical discovery
Relevant architecture, code, infrastructure, and data-flow review
Material risk and failure-mode assessment
Prioritized written findings and follow-up review

Outcome: A clear recommendation to stabilize, migrate, build, buy, phase, or stop.

Request an Architecture Review

Final scope depends on the evidence available, systems involved, stakeholders, and operational risk.

Commerce Integration Audit

Find reliability, reconciliation, ownership, and recovery gaps across commerce and enterprise systems.

Best fit: Orders, payments, fulfillment, inventory, loyalty, finance, or internal systems disagree or fail between boundaries.

Includes

End-to-end workflow and system-boundary review
Source-of-truth, identifier, and data-mapping analysis
Retry, idempotency, replay, and reconciliation review
Prioritized remediation and operational-visibility plan

Outcome: A practical plan for making critical integrations more reliable, observable, and recoverable.

Request an Integration Audit

Final scope depends on the number of systems, representative failures, access, and operational risk.

Controlled AI Workflow Assessment

Determine whether a workflow is valuable, safe, and technically viable before building it.

Best fit: A repeated workflow is consuming time, but the business case, controls, or implementation path is not yet clear.

Includes

Current workflow and bottleneck map
Data, permissions, and integration review
Human-review and failure-path design
Written pilot, product, redesign, defer, or no-build recommendation

Outcome: An evidence-based recommendation to automate, redesign, defer, use an existing product, or run a bounded pilot.

Assess an AI Workflow

Final scope depends on the workflow, available examples, systems involved, and access requirements.

Implement a bounded outcome

Deliver one validated workflow with clear controls, ownership, and handoff.

AI Automation Pilot

Implement one validated, measurable AI workflow with production controls and a clear handoff.

Best fit: The workflow, owner, examples, success criteria, and review path are already defined.

Includes

Bounded workflow and integration design
Validated structured outputs and human approval
Authentication, permissions, audit trail, and exception handling
Deployment, documentation, and handoff

Outcome: One production-ready workflow pilot connected to the client’s existing systems.

Discuss an Automation Pilot

Pilots cover one validated workflow with agreed success criteria, controls, and handoff. Complex integrations and business-critical implementations are scoped separately.

Add ongoing technical leadership

Keep principal-level judgment close to delivery and consequential decisions.

Fractional Principal Engineer

Add ongoing principal-level technical judgment without hiring another full-time executive or permanent principal engineer.

Best fit: A lean engineering team needs sustained technical leadership close to delivery and consequential decisions.

Includes

Architecture, design, and technical-strategy reviews
Roadmap, reliability, scalability, and vendor decisions
Critical implementation guidance and engineering standards
Mentoring, hiring support, and stakeholder communication

Outcome: Clearer technical direction, better-sequenced investments, and principal-level support for the team.

Discuss Fractional Support

Advisory and hands-on delivery have different responsibilities. Review cadence, availability, response expectations, implementation involvement, and operational ownership are agreed before work begins.

Engagements are scoped around the decision, systems involved, and delivery responsibility. After an initial fit discussion, I propose the scope, deliverables, and fee before work begins.

Broader consulting work

Capabilities that support the engagement

A focused engagement can lead to hands-on architecture and implementation support when the evidence justifies a broader scope.

Platform Architecture & Modernization

Evolve valuable systems through explicit capability boundaries, sequenced migrations, and operationally safe rollout plans.

Explore modernization →

Commerce & Enterprise Integrations

Connect commerce, ERP, CRM, payment, fulfillment, and internal systems with clear ownership and recoverable failure behavior.

Explore integration architecture →

APIs, Distributed Systems & Cloud Reliability

Build stable contracts, explicit failure handling, safer delivery, and infrastructure that small teams can operate with confidence.

Explore platform engineering →

Controlled AI Workflows

Use AI where the workflow, data boundaries, permissions, human review, failure paths, and measurable value are explicit.

Explore controlled AI adoption →

Fractional Principal Engineering

Add sustained principal-level judgment close to delivery without hiring another full-time executive or permanent principal engineer.

Explore fractional support →

Not sure which engagement fits? Describe the business capability, platform risk, and systems involved. Antonio will recommend the smallest responsible next step.

Discuss Your Platform Challenge

Review Selected Case Studies