User Interface.Users engage via AI agents. Mega-apps replaced by cheap, swappable micro-apps.
AnthropicOpenAIGoogle
Oligopoly
Reasoning.AI inference. Owned by a handful of frontier labs. Commoditizes quarterly.
AnthropicOpenAIGooglexAI
02 / Common workstream
Data readiness.
01
Inventory
Map every primary data source across the org. Who has what. Where it lives.
02
Source of truth
Anoint one canonical source per domain. Retire or consolidate the rest.
03
Restructure
Word, Excel, PPT, and email content moved to structured DB and indexed markdown.
04
MCP-ready
Every source wired up so agents can read and write through it. Data lake if it helps.
Today
Word docs scattered across Drive and SharePoint
Excel sheets emailed as the "latest version"
PowerPoints as the final form of analysis
A dozen places to find one customer record
→
Ready
Indexed markdown and structured DBs as primary
One canonical source per domain
MCP endpoints on every system that matters
Human-readable means AI-readable
03 / How we engage
Two tacts in parallel.
↓
Top-down
Tiger team automates the highest-value workflows.
Advisor + small internal Tiger team. Show what's possible. Building toward AI teammates with their own identities.
↑
Bottoms-up
Early adopters lift their own workflows.
The 5 to 10% who are already curious. Focused 1:1 enablement. Skills spread through the org. Surfaces ideas to Tiger team.
The two compound. Bottoms-up builds the muscle. Top-down builds the flagship examples.
04 / Top-down, in detail
From stakeholder interviews to AI teammates.
01
Interview stakeholders
Identify the highest-value workflows in each function. Surface the pain.
02
Redesign workflows
Often the underlying data structure too. Optimize for agent-first execution.
03
Custom Skill files
Encode workflows for employees to run locally with a Cowork-style agent at their side.
04
Managed Agents
Cloud-hosted, always-on. Triggered by email, schedule, or webhook. No human at the keyboard.
05
AI teammate
With enough training, the agent takes over the routine role end to end. Humans handle ambiguous and high-stakes cases.
05 / Where we're heading
Stages of AI Aptitude.
06 / Bottoms-up, in detail
Start with the innovators.
07 / Why us
A Tech-Native Founder-led Approach.
Large consultancies
Slow, expensive, entrenched.
Months of slide decks before any real work happens
Massive teams, layers of account managers, partners, juniors
Deliverables-as-product. The doc is the outcome.
Contracts designed to expand scope, not reduce it
Your team learns to depend on them
Magnetic Advisors
Fast, sharp, designed to exit.
Working code and live workflows in weeks, not quarters
One advisor plus a forward-deployed engineer. No layers.
Results-as-product. The doc is incidental.
Month-to-month. Aimed at making ourselves unnecessary.
Your team learns to operate without us
08 / What we bring
Founder Mindset.
Strategist
McKinsey-style consultant.
Builder
Technical software engineer.
Operator
Founder CEO taste.
Plus cross-pollination. Working across multiple companies means we import the patterns that are already working elsewhere, saving you a year of trial and error.
Magnetic Advisors
Bonus.
Extra workstreams that show up in most engagements.
Common workstream
Goodbye point-solution SaaS.
Gong
→
Custom call analyzer with MCP into your CRM. Same insights, owned by you, extensible by your agents.
~$150K / yr
Docusign
→
Direct PDF + provider API for e-signing. A 200-line internal tool replaces an enterprise contract.
~$80K / yr
Figma
→
AI-native design directly in code or via agent-driven design files. Faster iteration, lower seat costs.
~$200K / yr
PowerBI
→
Claude artifacts + MCP. Anyone on the team builds their own dashboard from live data. No analyst queue.
~$200K / yr
Typical savings: $100K to $300K annually per SaaS torn out. Across a full audit, mid-size orgs routinely recover seven figures in annual recurring spend, plus eliminate the integration tax that comes with stitching point solutions together.
Common workstream
Product team rebuild.
2x velocity at 1/2 the headcount
Typical result inside six months
The team
Everyone is a builder.
Pods of 1 PM plus 2 to 3 builders, working alongside agents. One pod per product line.
No front-end vs back-end split. Builders own the full stack with the agent at their side.
PMs ship pull requests. Designers ship pull requests. The doc is incidental.
No separate engineering org running parallel to product. One team.
External resources only as embedded individuals, never as a parallel firm.
The repo
Agent-ready from the inside out.
Feature request to full PR with one prompt. Claude or equivalent agent generates the patch, opens the PR, comments the plan.
Self-test and self-correct loop. The agent runs the suite, reads the failures, fixes its own work before any human review.
Full CI pipeline with an automatic security review agent and a best-practices review agent on every PR.
Repo configured for agent-first work: CLAUDE.md, plan-mode files, skills, MCP connectors all wired.
QA's verification logic encoded into skills so agents self-test against the same bar humans do.