Too many experiments
AI tools are being tried across the business, but no one owns the operating model.
AI advisory and implementation
The Tomorrow Machine.
Put tomorrow to work. I help founder-led businesses turn AI agents, automation ideas, and manual workflows into reliable systems for operations, reporting, forecasting, sales, and decision-making.
One high-value workflow. A working system in 30 days. Measurable results.
You work directly with Jason Merkoski.
- workflow
- 1
- build window
- 30 days
- result
- measured
SignalSystemOutcome
MIT theoretical mathematicsKindle employee #10 and launch leader25+ years building new technology into businessesCTO across healthcare AI, SaaS, data, and automation
Why The Tomorrow Machine exists
Most businesses do not need more AI ideas. They need the right one built properly.
Many teams already have ChatGPT accounts, automation subscriptions, scattered experiments, and half-finished prototypes. What they usually do not have is a clear way to choose the right opportunity, connect the right data, redesign the workflow, measure the result, and keep the system reliable after the first demo.
Too much manual work
Reporting, follow-up, forecasting, and coordination still depend on spreadsheets, copying, and memory.
Too little follow-through
Promising ideas stall between the first conversation and a system the business can rely on.
The Tomorrow Machine turns one expensive, decision-heavy workflow into a system your business can actually use.
Have a workflow that feels expensive, repetitive, or hard to trust? Send the rough version.
Start a conversationWhat I build
From idea to operation.
Executive reporting
Automated weekly and monthly reporting that pulls from real business data and explains what changed.
Forecasting and decision support
Systems that help leaders reason about demand, revenue, risk, operations, and next actions.
AI-assisted operations
AI agents and workflow automation that coordinate information across people, software, and recurring business processes.
Sales and follow-up
Practical systems for lead research, qualification, outreach support, follow-up, and pipeline visibility.
Internal knowledge
Secure assistants that help teams find answers across documents, databases, policies, and operating history.
AI reliability
Evaluation, monitoring, human review, security, data quality, and cost controls for AI systems that already exist but are not dependable.
The best first idea is usually not the flashiest. It is the one with clear value, usable data, and a committed owner.
Tell me where the manual work, reporting gaps, or decision bottlenecks are showing up.
Start a conversationServices
Choose the right idea. Build the smallest useful system. Expand from results.
BEST FIRST STEP
AI Workflow Review
A focused review of up to three workflows to identify the strongest first AI system, agent, or automation to build.
- Leadership interview
- Workflow and data review
- Opportunity and risk assessment
- ROI ranking
- Recommended architecture
- 30-day implementation plan
- Success metrics
- Executive readout
Fixed scope. Typically $1,500–$3,000.
Discuss a Workflow Review30-Day AI Build
One high-value idea turned into a working, monitored business system.
- Workflow redesign
- Data and software integration
- AI prompts, models, tools, or agents
- Human review and escalation
- Acceptance testing
- Monitoring and cost visibility
- Documentation and training
- Outcome measurement
Most builds begin at $10,000.
Discuss a 30-Day BuildFractional AI Operator
Ongoing senior ownership for businesses that need an AI leader but not a full internal department.
- AI roadmap and prioritization
- Architecture and vendor decisions
- Supervision of developers and vendors
- Reliability and cost reviews
- Governance and data-risk review
- Continuous workflow improvement
- Executive reporting
Monthly engagements are scoped around the operating need.
Discuss Fractional SupportNot sure which service fits? The form is enough to start the conversation.
Start a conversationHow ideas get built
Choose carefully. Build concretely. Keep the system honest.
Find the leverage
Identify the workflow where better information, faster execution, or fewer manual steps would matter most.
Shape the idea
Define the business outcome, baseline, success metric, human owner, available data, and conditions under which the system should be trusted.
Build the system
Connect the data, redesign the workflow, implement the AI or automation layer, and make the result usable inside normal operations.
Operate and improve
Monitor quality, cost, failures, drift, and business outcomes. Improve the system when the evidence supports it.
An AI idea matters only when it becomes a useful part of the business.
If the idea is fuzzy, start there. The first job is deciding what is worth building.
Start a conversationSelected work
Systems built where mistakes matter.
Quantitative decision and data systems
Built forecasting, analytics, and decision systems with feature engineering, backtesting, model tracking, monitoring, alerts, and automated reporting.
- Time-series signal analysis
- Model and prompt lifecycle management
- Backtesting and utility-based evaluation
- Operational alerts and telemetry
- Executive and daily reporting
Healthcare AI operations
Led AI-powered healthcare platforms using LLM workflows, voice AI, transcription, summarization, patient engagement, and human escalation in high-trust environments.
- Virtual nurse and patient engagement systems
- Voice and workflow automation
- HIPAA-conscious transcription and summarization
- Human-in-the-loop review
- Production ownership and safeguards
SaaS, marketplace, and business automation
Led product and engineering for SaaS, advertising, creator marketplace, and business automation systems that converted data into customer-facing features and operating insight.
- Customer-facing analytics
- Big data and ML pipelines
- Creator and advertising marketplaces
- Lead research and qualification
- Dashboards and management reporting
Kindle and frontier product launches
Joined Kindle as employee #10 and worked across content, formats, publishing tools, launch operations, mobile connectivity, and data-driven product decisions.
- Kindle 1, Kindle 2, and Kindle DX launch work
- Ebook formats and publishing tools
- Large-scale content ingestion
- Executive product reviews
- Applied futurist product leadership
If you see a pattern from your business in these examples, describe it in a few lines.
Start a conversationAbout
Senior AI judgment. Hands-on implementation.
The Tomorrow Machine is my AI advisory and implementation practice. I’m Jason Merkoski, an MIT-trained mathematician, AI product and engineering leader, and experienced CTO. I joined Amazon Kindle as employee #10, helped launch the first Kindle devices and publishing systems, and have spent 25+ years turning frontier technology into products, platforms, and operating systems.
My recent work includes AI agents for independent medical practices, voice AI, LLM workflows, HIPAA-conscious transcription and summarization, patient engagement, financial and operational analytics, scalable ML/data pipelines, and SaaS systems that connect technical execution to business outcomes.
I have spent much of my career in environments where a convincing demo is not enough. The system has to use the right data, behave reliably, surface uncertainty, support human judgment, and continue working after launch.
I created The Tomorrow Machine for founder-led businesses that are ready to use AI seriously but do not need a large consultancy or a permanent internal AI department.
The name reflects the work: making tomorrow practical by turning worthwhile AI opportunities into systems that operate in the real world.
- MIT BS in theoretical mathematics with creative writing
- Kindle employee #10 with launch, content, format, and publishing-platform leadership
- Multiple CTO and product/engineering leadership roles across healthcare AI, SaaS, adtech, creator marketplaces, fintech, and automation
- Hands-on AI-native builder across agents, LLM workflows, voice AI, ML/data pipelines, TypeScript/React, Python, and MLOps
- Author of Burning the Page and longtime applied futurist
- Hands-on from executive problem definition through production implementation
Work starts directly with Jason, not a sales handoff.
Start a conversationFAQ
The practical questions usually come first.
I do both. The first step is choosing the right problem and defining what success means. From there, I can design and build the system directly, supervise other developers, or work with the team you already have.
The best fit is usually a founder-led or owner-operated business with roughly 10–100 employees, valuable operational data, recurring manual work, and no internal AI leader.
No. Most clients begin with a workflow that is expensive, slow, repetitive, or difficult to manage. The Workflow Review determines whether AI is useful, what should be built, and what should remain human.
No. Data readiness is part of the assessment. Sometimes the best first project is an AI system. Sometimes it is fixing the workflow or data foundation that makes a future system possible.
The goal is usually to remove repetitive work, improve consistency, and give people better information. Every engagement is designed around a clear human owner, review path, and escalation process.
The technology depends on the business problem. I work across modern AI models, databases, APIs, automation tools, cloud services, reporting systems, and custom software. The stack follows the workflow, not the other way around.
A Workflow Review can usually begin with a leadership interview and access to the relevant process, data, and systems. A 30-day build begins once the scope, owner, and success metric are agreed.
Yes, where the project, systems, and agreements support it. Sensitive work begins with explicit decisions about access, storage, vendors, logging, and human review.
Still deciding whether AI is useful here? Bring the workflow and the constraint.
Start a conversationContact
Bring me the workflow you think AI should fix.
We will identify the strongest idea, define what success means, and determine the smallest useful system worth building.
Or email Jason directly at hello@tomorrowmachine.ai.
No sales team. No generic pitch deck. You will speak directly with Jason.
U.S. Mountain and Pacific working hours make live collaboration easy for U.S. teams.