Planning & forecasting
Driver-based budgets, rolling forecasts and scenario models that keep leadership ahead of the curve—not explaining it after the fact.
Strategic FP&A · Predictive analytics · AI
Strategic finance.
Forward intelligence.
Actualytics brings sophisticated FP&A, forecasting and finance transformation to every stage—from founder-led businesses building their foundation to established enterprises navigating greater complexity.
↓Probabilistic outlook80% confidence range
P10—P90
The modeled range containing 80% of likely outcomes around the central forecast.
ActualPlanOutlook
01 / Capabilities
Where finance experience
meets analytical depth.
Strategic finance for every stage of growth—from ambitious founder-led businesses to scaling teams and complex enterprises. We combine finance judgment, analytical rigor and practical implementation to improve real decisions.
Forecast science & predictive analytics
Time-series, trend and probabilistic models that turn historical patterns and business drivers into a range of plausible futures—not one fragile number.
Financial models & cash
Decision-ready models for growth, investment and liquidity—with assumptions leaders can understand, challenge and own.
Executive reporting
Board and leadership reporting that replaces data volume with a clear narrative: what changed, why it matters and what comes next.
Sales commissions
Transparent commission models and processes that connect go-to-market behavior, plan economics and reliable payout operations.
AI, systems & finance transformation
AI-enabled workflows, connected systems and cleaner processes that automate the repeatable work while keeping judgment, control and accountability with finance.
02 / Selected work
Models that make complexity
visible and actionable.
Representative, anonymized views of advisory work. Client names, system names and confidential values have been removed or normalized while preserving the analytical method.
Operating model / Revenue assurance
Billing-to-revenue intelligence
A single control view follows commercial and item data from source setup through invoicing, the finance ledger and revenue recognition—exposing where manual handoffs, timing gaps and broken lineage can distort the result.
01Contract + order
02Item setup
03Billing run
04Invoice review
05Ledger posting
06Rev rec schedule
07Recognized revenue
Data-governance bottlenecksControl layer
01Product and SKU lineageContract → invoice → ledger item
02Effective-date integrityActivation → billing → service period
03System handoffsManual uploads and mapping exceptions
04Revenue bridgeBillings → deferred revenue → P&L
Operational event→Invoice→Contract liability→Recognition schedule→Revenue
Bot economics modelScenario B
Volume1.42xnormalized index
Unit cost−18%modeled change
Capacity+31%available output
BaseScaleOptimizeTarget
CapacityUnit economics
Financial model / Automation economics
Bot capacity and unit economics
A driver-based model links demand, bot utilization, operating capacity and cost-to-serve—so leaders can test scale, investment and margin scenarios before committing resources.
Illustrative presentation only. Values are normalized and do not represent any specific company.
03 / Founder
Jose Guzman, MBAFinance leadership today.
Finance leadership today.
Twenty years behind it.
Today, Jose works at the intersection of finance, data and technology—as a director-level FP&A leader, executive partner and trainer supporting complex, high-performing organizations.
He brings more than 20 years of combined professional experience across banking, corporate finance, market-share analysis, trust operations, forecasting, financial modeling, M&A integration, cash management and executive reporting. His career spans multiple industries and markets, including foundational experience in his native Colombia and extensive leadership experience in the United States.
After earning an MBA from Texas A&M’s globally recognized Mays Business School, Jose continued building expertise with leading U.S. companies. He founded Actualytics to unite that operating experience with advanced analytics, responsible AI and practical finance transformation.
View LinkedIn profile ↗Experience20+ professional yearsFinance leadership across industries and markets
EducationTexas A&M MBAMays Business School
AnalyticsCertified analystMicroStrategy analytics credential
KnowledgeTrainer and mentorMaking technical finance practical
“AI is the execution layer. Finance leaders are the architects.”
Data foundations→Financial logic→Control points→AI execution→Better decisions
04 / How we work
From financial signal
to decisive action.
Practical enough for the next operating review. Durable enough to strengthen the finance function long after the engagement.
Diagnose
Find the decisions that matter, the friction slowing them down and the data that can be trusted.
Design
Build the model, metric set and operating rhythm around how your business really works.
Embed
Put the answer into use—documented, repeatable and owned by the people who run the business.
05 / The difference
“The best finance teams don’t just report the result. They make the next result more intentional.”
Clarity
One decision-ready view of performance and its drivers.
Foresight
Living forecasts that reveal tradeoffs before they become surprises.
Momentum
Processes and systems that help finance lead, not chase.
Start a conversation
Make your numbers
mean more.
Bring the forecast, reporting or finance challenge that keeps resurfacing. We’ll start with the decision it needs to support.