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Data strategy & engineering for agentic workflows Ray Grieselhuber Founder & CEO, DemandSphere @MKE DMC April 15, 2026

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Agenda ● Intro ● Background and motivations for this topic ● Vibe coding vs. AI-driven engineering ● Tools & skills ● Testing & security ● Project ideas ● Open weight models vs. closed models ● Final thoughts

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Introduction ● BA, Japanese & B.S. Computer Science ● Software engineering with focus on early ML, agents, big data, distributed systems, etc. ● SEO since 2006, built first enterprise platform on the market ● Founded DemandSphere (GinzaMetrics) in 2009, Tokyo ● Moved to Silicon Valley in 2010 for YC ● Work with clients all over the world

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Quick description of our platform for context

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Today we’re building a map, don’t worry too much

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Goal is to promote more fluency in building with AI

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Manager mode vs. Founder mode

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We are all in Founder mode now

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Founder mode means: ● Staying close to the user (even if it’s yourself at first) ● Build what people want ● Iterate quickly ● Keep your sleeves rolled up

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Software as a Service Or Service as a Software?

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Experience at Functionize taught me about the new SaaS

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The new SaaS (Service as a Software): ● AI is empowered by human attention ● Businesses pay for results, they don’t care about your cool AI systems if they don’t work ● Humans have to backstop when AI systems fail

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The dirty little secret in SaaS

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Silicon Valley is finally starting to figure this out

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Vibe coding vs. AI-driven engineering

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Don’t look down on vibe coding

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Vibe coding is good for building prototypes and testing ideas quickly

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AI-driven engineering is for building products

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Vibe coding should be the starting point

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Deployments and operations are what separate vibe coding and AI-driven engineering

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People and processes (manual & automated) support actual products

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Simple example: AI frontier model tracker

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Constant updates + QA (human & machine)

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Cambrian explosion of tools and ideas

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Need to anchor to business goals

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But the cost of experimentation is cheaper

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Let’s talk about some basics

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Tokens are the currency of AI and heavy context spends more tokens

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Tokens = cost + latency + attention budget

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The best AI development workflows: ● Compress ● Summarize ● Deduplicate ● Retrieve only what is needed

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It’s about curation of the context window

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(This also applies to AI search & SEO btw)

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Tools you should use & know

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Skills (also an open standard)

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Be very careful with skills you did not build (supply chain attack)

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Claude Code

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Working in a team environment

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Context files

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CLAUDE.md

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CLAUDE.md files can be hierarchical

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Per branch

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Per subfolder

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Have Claude Code generate code maps for context

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Have Claude Code generate ERD diagrams for databases

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Mermaid and Mermaid.live are great for diagramming

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The point is that maintaining context for humans is just as important as maintaining it for agents

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Simple text “standards” are always the thing to bet on

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HTML Vanilla JS JSON CSV Prompts Git (based on hashes) Markdown (.md) Mermaid

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Monitor your /context and /compact when needed

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A basic vibe coding to AI-driven engineering workflow Start in Claude.ai Vanilla prototype Claude.ai to build CLAUDE.md Move to local filesystem claude init Create repo Enforce testing policy Configure deployment environment Automate deployments Deploy regularly

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Let’s talk about building MCP servers

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MCP servers start off as local and “self-hosted” until you deploy them

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Remember the difference between vibe coding and AI-driven engineering? Deployment!

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Security & good tool construction are the hardest parts

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Be sure to create an automated test suite

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Security risks with MCPs: ● Client side ○ Giving too much access to the MCPs ○ Prompt injection ○ Data / key leakage ● Server side ○ Validating tool inputs ○ Sanitizing output ○ Rate limits ○ Access controls

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Let’s get into solving some real problems

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In search, we are trying to get into the context windows of our audience’s minds

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Context windows are everywhere

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This means we need lots of data about their behavior and motivations

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GSC Analytics data Search volume SERP data Log files LLM data Query fanouts (the list is always growing)

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The first step is deciding where this data is going to live

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Then how to get it

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APIs by themselves have challenges

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MCPs can help to make tool usage of APIs more efficient

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But MCPs are transient

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API direct to dashboards introduces latency

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API direct to agents is an improvement but granular

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You can solve a lot of problems with data warehouses

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BigQuery, Clickhouse, and Snowflake

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BigQuery is probably the best default choice

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Using BQ as a data buffer between systems

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Knowledge bases for agentic workflows

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Context files vs. LLM wikis vs. RAG

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Entity canonicalization is a really hard problem

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Final thoughts

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AI feudalism is coming

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Make the investment to own your own compute capabilities

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Learn how to work with open weight models

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Going to SEO Week? https://luma.com/p7r3nbvk

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Join us at FOUND Tokyo 2027

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Thank you!