About the founder
Jonathan Toland
Twenty years building the systems companies run on. One of them turned out to be dinner.
- Technology Executive
- CIO / CTO / CDO / CAIO
- Enterprise IT Strategy
- Data Platform Architecture
- Applied & Governed AI
About
I’ve spent twenty years running enterprise technology in places where being wrong is expensive — financial services, fintech, and venture-backed SaaS. My work sits where IT strategy, data architecture, and applied AI meet: the platforms a company operates on, the governed data leadership can actually trust, and increasingly the AI systems doing the work.
I’ve been a hands-on AI practitioner since 2017, when I founded an enterprise RPA Center of Excellence at a $7B publicly traded fintech. Since then I’ve built a production intelligent agent framework at a VC-backed SaaS company, and today I architect AI operating models and domain-specific AI systems at a PE-backed wealthtech firm under SEC and FINRA oversight. I write the governance as well as the code, because in a regulated business the two are the same job.
Along the way: 200+ person global teams, full IT organizations at CIO scope, 40+ M&A integrations, enterprise data platforms built from scratch on Snowflake, and multi-year technology roadmaps built alongside CFOs, CIOs, and Boards.
What I care about is the unglamorous part — making a messy system legible, governed, and boring in the best sense. That instinct is what produced DinnerTable.
Why I built DinnerTable
DinnerTable started as a prototype for a handful of real families — mine and a few close to it. Each one had the same problem and none of the existing apps solved it: a weekly plan has to work around the people at the table, not the other way around. Allergies. A kid who won’t touch anything green. The Tuesday you have twenty minutes.
The safety piece is what made it worth building properly. Roughly 5.6 million children in the U.S. have a food allergy, and for those households meal planning isn’t convenience — it’s a safety system. So DinnerTable treats it like one.
Allergens are hard-filtered by severity before a single preference is scored. A recipe that isn’t safe never enters the pool — it isn’t ranked lower, it isn’t shown with a warning, it simply isn’t there. That’s the same discipline I’d apply to a control in a regulated environment, pointed at something closer to home.
Everything else follows from the plan: the grocery list, the cook-mode steps, the feedback that makes next week’s plan better than this week’s.
Technology ecosystem
- Data & Analytics
- Snowflake, Databricks, dbt, Tableau, Power BI, Looker, Oracle Analytics
- AI & Automation
- Domain-specific AI systems, agentic frameworks, Claude Code, MCP servers, RPA, GenAI, MLOps
- Platforms
- Oracle Fusion (ERP/HCM/EPM), NetSuite, Salesforce, HubSpot, Workday, SAP
- Cloud
- AWS, Azure, OCI, Google Cloud — migration and FinOps
- Integration
- Workato, Boomi, Informatica, Fivetran, REST APIs
- Governance
- SOX, SOC 1/2, GDPR, CCPA, HIPAA, SEC/FINRA, enterprise AI governance frameworks
Get in touch
Open to conversations about DinnerTable, enterprise AI governance, or the CIO/CTO/CDO/CAIO work that sits underneath both.