O
Oxtane
AI Audit & Workflow Advisory
The easiest way to start with AI consulting: the AI audit

Find the repetitive work
draining your business

Oxtane helps teams start with AI in a practical way. We map recurring workflows, identify the biggest time and cost sinks, and show where AI can create the clearest near-term leverage.

45-minute workflow call$1,000 flat feePrioritized action planImplementation available after
Core offer
AI Audit
Simple entry point
Price
$1,000
Flat fee per audit
Delivery time
~2 hours
plus a 45-minute discovery call
Outcome
Action plan
AI tools, workflows, and priority order
You’re not paying for a giant transformation project on day one. You’re buying a structured assessment, clear bottleneck ranking, and a practical view of where AI can help first.
Offer

A clear first step into AI

Start with one focused audit. Get clarity on what is worth fixing first, where AI can help, and what should wait.

Workflow mapping
We make your recurring processes visible so the real opportunities are easier to spot and discuss.
Bottleneck ranking
We identify which workflows are creating the most drag on time, budget, and operator attention.
Practical AI recommendations
You get concrete priorities, tools, and workflow changes that are realistic to test and implement.
Process

How the AI audit works

A short engagement designed to turn operational friction into a clear plan of action.

Before the call, clients fill out a short 5-question intake. The conversation stays focused and practical, with follow-up only where it adds real clarity.

01
Understand the workflow
In a 45-minute call, we review the recurring processes inside your business and where things feel slow, messy, or expensive.
02
Spot the real bottlenecks
We narrow the problem down to the workflows that are creating the most drag on execution.
03
Define the best next move
You leave with a practical recommendation on what to change first, what to test, and where AI is most likely to help.
Deliverables

What you walk away with

A structured assessment and a clear next move, not a massive retainer commitment.

✓Workflow map of the business areas you want reviewed
✓Top 3 bottlenecks with clear ranking
✓Best first wedge: the one workflow to tackle first
✓Recommended AI priorities, tools, and workflow changes
✓AI Audit Memo with next action and pilot plan
Good fit

Who this is for

Best for teams with recurring workflows, constrained time, and a real appetite for operational leverage.

•Founder-led teams with too much repetitive ops
•Media, research, and content organizations
•Lean teams trying to scale without bloating headcount
•Crypto and AI-native teams that want practical automation, not buzzwords
Recent focus

Closer to what I’m actually working on now

AI systems, operator workflows, research pipelines, and crypto-native execution.

AI agent workflows
Designing agent-driven research, review, and execution loops that save operator time without creating chaos.
Research and content ops
Turning messy recurring analysis, reporting, and synthesis work into systems that produce faster output with better consistency.
Crypto-native automation
Applying practical AI and workflow design to crypto teams, infra, diligence, compliance, and go-to-market operations.
Daily AI Alpha

Research radar and operator-grade AI intel

This is the layer I want to keep expanding: curated AI signals, applied research summaries, and practical notes on what matters for real operators.

What shows up here
Memory signal
MemMachine posts a strong LoCoMo result for personalized agent memory
A new memory-system paper reports 0.9169 on LoCoMo with gpt-4.1-mini and positions itself above several open memory-framework baselines.
arXiv 2604.04853 · 1 week agoOpen source
Evaluation signal
Beyond Task Completion argues agentic systems need broader evaluation
The paper frames non-determinism, tool choice, and memory retrieval variability as first-class evaluation problems for agent systems.
arXiv 2512.12791v2 · 3 weeks agoOpen source
Architecture signal
Graph-native cognitive memory explores formal belief revision for agents
Kumiho proposes a graph-native memory architecture that tries to unify versioning, retrieval, consolidation, and belief updates more formally.
arXiv 2603.17244 · 1 month agoOpen source
AI research paper spotlight
Agent reliability and evaluation
Research worth watching on evaluation loops, verifier design, failure modes, and how to make agents useful without making them brittle.
Memory, retrieval, and context systems
Paper summaries focused on persistent memory, retrieval quality, and how long-horizon AI systems keep state without drifting.
Applied workflow automation
Less theory, more implementation: papers and technical notes that can actually influence internal ops, content, and research workflows.
Auto-refreshes from recent arXiv papers every 6 hours, with a curated fallback if the live feed fails.
About

Tetsu Sakamoto

AI and crypto operator, investor, and workflow builder. My work sits at the intersection of research, execution, automation, and strategic clarity.

The point of this offer is simple: help businesses find the cleanest, most useful way to apply AI to real workflows first, then expand into implementation once the highest-leverage opportunities are obvious.

Positioning
AI Audit → AI Implementation
Start with diagnosis. Rank the highest-value workflows. Then decide what to automate, augment, or rebuild with AI.
Contact

Book the AI Audit

Start with a short pre-call intake. From there, we run a focused 45-minute call to understand the business, identify the main bottlenecks, and define the most useful next step.

Direct email instead
Flat fee: $1,000 per audit. You get a workflow inventory, ranked bottlenecks, a recommended first priority, and an AI Audit Memo. Please do not include passwords, private keys, customer secrets, or regulated data in the intake.