Build a Skill for your exact workflow.
Most AI projects fail on requirements, not technology. Digital Block FX brings certified business-analysis discipline to every custom Skill we build, so the workflow you automate is the right one, specified correctly, and governed from day one.
Business-analysis-led AI implementation
AI Readiness & Process Assessment
Before you buy tools or build agents, know where AI actually pays off. We assess your processes, data, and team readiness, map the highest-value automation candidates, and hand you a prioritized roadmap you can act on in 30–60 days.
Learn about assessments →Business Analysis & Requirements
The step most AI projects skip. Process mapping, task decomposition, requirements, and acceptance criteria — delivered by a Certified Business Analysis Professional (CBAP) so your automation is built on what your business actually does, not what a demo assumed.
Learn about business analysis →Agent Implementation
From specification to production. We implement AI skills and agents on the Endeavor platform by Rotational Labs — with defined permissions, testing, human oversight, and audit trails built in, so every agent is accountable to a named owner.
Learn about implementation →Assess. Specify. Implement. Govern.
1. Assess
Find the workflows where AI creates real value.
2. Specify
Document the process, the requirements, and what “correct” means.
3. Implement
Build and deploy agents against the specification.
4. Govern
Monitor, audit, and improve them in production.
Requirements discipline. Platform partnership.
Digital Block FX is led by Ryan Low, a Certified Business Analysis Professional (CBAP®) certified by the International Institute of Business Analysis (IIBA), and an independent business development partner of Rotational Labs, makers of the Endeavor AI platform for mid-market enterprises.
What every engagement is built on
Process mapping & task decomposition
Break workflows down to the task level and identify which steps are ready for automation.
Requirements & acceptance criteria
Define what each agent must do, must never do, and how success is measured — before anything is built.
Agent specification & permissions
Every agent gets a written specification: purpose, scope, permissions, escalation rules, and a named human owner.
Workflow skills development
Reusable AI skills that encode how your business does its work, so quality compounds instead of resetting with every project.
Testing & validation
Evaluate agent output against acceptance criteria before and after deployment, so quality problems surface in testing, not in front of customers.
Governance & monitoring
Audit trails, human review, rollback paths, and ongoing performance monitoring for every deployed agent.
Analyst first. Implementer second. In that order.
Ryan Low has spent his career turning complex operations into working systems — from Bitcoin mining infrastructure and data center deployment to enterprise software and applied AI. Today that experience is focused on one thing: helping businesses implement AI agents with the rigor of professional business analysis.