Don't worry, this isn't a pitch for a product, I built this for my own company's private use. I just wanted to share and get feedback from you fine folks 😄

This is probably the most valuable thing we've created for ourselves and it's only a small part of what we've been building with AI over the last few months.

This internal tool (codenamed Armory) is designed to keep us informed about our network and stay engaged with people whom we've had a positive working relationship.

Our business is entirely relationship-based, so naturally we began with designing a system to organize our contacts. Starting with ingesting ~3M personal and professional emails over the course of the past 20 years, we were able to create contact records that consolidate known emails and companies for every individual person in our network.

To summarize what you see on this screen, we've combined several different data sources including:

• email history
• contact information
• employment history
• Otter.ai meeting transcripts
• MailChimp campaign interaction history
• Manual category and relationship classification

Sentiment Analysis

The Sentiment Analysis chart in the center is derived from a combination of all those signals and describes:

• the overall positive/negative tone of our conversations (Tone)
• how recently I was in contact with this person (Recency)
• how long ago was our first contact (Longevity)
• how often this person replied to me (Reciprocity)
• the amount of communication (Volume)

Once we have a Sentiment, we can perform a Deep Dive analysis and subsequently what we call a "World Model" analysis.

Deep Dive Report

The Deep Dive analysis performs an internet search using information like associated email addresses, name, name and employment history in order to find things like articles this person has written, articles or press about them or their company, activity on social media, etc.

Once we have a Deep Dive report, we can perform a World Model analysis which includes an assessment of this person's:

• career trajectory (how engaged are they professionally)
• transition moment (have they started a new position recently)
• likelihood (are they in a position to hire us)
• urgency (have they posted recently or is there recent news related to our services)
• budget (company size and revenue, and potential type of engagement)
• relationship (incorporates sentiment analysis in the context of an engagement)

World Model Report

If the Deep Dive Report answers "who is this person", the World Model analysis answers "what should we do about them, when, and how." It's a decision-oriented LLM synthesis pass that forbids speculation without evidence.

In general, it assesses:

• Current state: 3-5 bullets on role/tenure, company phase, active initiatives, location, public posture
• Derived signals: Career velocity (accelerating/steady/decelerating/pivoting), transition moment, behavioral patterns, network activity, domain trajectory
• Predicted intent: Hiring / vendor evaluation / platform scaling / rebranding / thought leadership / fundraising — each with High/Medium/Low probability + evidence
• Opportunity score: 3 dimensions (1-10): Likelihood, Urgency, Budget. Overall = average
• Relationship strength: One of: deep / strong / active / stable / distant / cold (based on email count + recency + tags)
• Suggested action: One primary: engage / reconnect / pitch / monitor / introduce / collaborate
• Message angle: Only when the action is outreach: lead_with, connect_to, avoid, tone, channel

In closing

The key here is automation. The Sentiment Analysis, Deep Dive, and World Model reports all happen automatically in nightly batches. Though the graphs and charts look pretty, the utility is the near real-time actionable insights. Our goal is to understand our network, understand what the market needs, and only spend time connecting with people we can actually do great work for and who already know us.

One of the biggest regrets I've had is losing touch with great people we've worked with over the last ten years. I've often felt like those connections slip through your fingers but with this type of intelligence, we no longer have to worry about that.

This is just one of a suite of internal products we've built which include things like analyzing industry trends and social chatter, and auto-generating scheduled goal-oriented content across a variety of channels including social, blogs, newsletters, and direct outreach using automated self-learning a/b test data from multiple analytics sources. Super fun stuff like that.

A final important word of warning about building these types of "automation" systems:

>

These tools can help us automate information gathering, and they can even help tell you where and how and what to write but the final decision to execute those recommendations is performed by an actual person. There are already too many cold calls and emails, mastermind calendar invites, newsletters, and other types of un-targeted spam on the internet; we don't want to add to that noise.

The goal here is to spend our time enriching existing relationships, and pursuing only the most valuable opportunities.

———

Tech stack notes:

• OpenClaw
• Neo4j
• Qdrant
• N8N
• Postgres
• Chrome extensions

Claude Code with VS Code using the Smith harness was responsible for configuring all services, workflows, prompts, and integrations. There are 11 Docker containers running a variety of other aspects of this system, with the Contact Graph connecting with the Sentiment Engine, Communication Triage, Meeting Intelligence, Social Listening, Mailchimp Sync, and Deepdive Worker applications (again, all custom built with Claude Code)

Wanted to share with everyone a pretty cool experiment I worked on over the last couple weeks. Let me know what y'all think!

SUMMARY

This system was developed using Claude Code. The seven-stage pipeline (audio-based serve detection, video segmentation, MediaPipe pose extraction, statistical baseline computation, and comparison reporting) was architected and implemented through spec-driven development over a few weeks using the Smith harness (https://smith.attck.com). Our role was direction, review, and the manual curation steps where human judgment matters, like selecting which serves to include in the baseline.

Final comparison video: https://youtu.be/F9wHacvYza0

THE PROCESS

1. Ingest professional gameplay
Match broadcasts are downloaded and indexed by audio. The distinctive "pop" of a paddle striking the ball is detected acoustically to flag every serve candidate in the footage.

2. Isolate serves from a consistent perspective
Each candidate is filtered by camera angle and court position so that every clip used in the baseline shows the server from the same viewpoint.
Inconsistent angles are discarded.

3. Extract pose data with MediaPipe
Every frame is passed through Google's MediaPipe pose landmarker, producing a 33-point skeleton for the serving player. Smoothing filters remove noise while preserving the kinematic detail that matters.

4. Derive a professional baseline (with statistical margin of error)
111 curated pro serves across 11 professional matches are aggregated into a statistical baseline. Each biomechanical feature gets a mean, standard deviation, and confidence interval. To further inspect the data, we segmented the serve into four stages: setup, backswing, forward swing, and follow-through. This lets the system make more targeted recommendations.

5. Process amateur footage through the same pipeline
The amateur serve runs through every identical stage, same detection, same pose extraction, same feature computation.

6. Calculate statistically significant differences
Each amateur feature is compared against the pro baseline. Differences are ranked by effect size and statistical significance so the recommendations that come out of the system aren't opinions. In this case: the amateur's serve is fundamentally an arm-only motion (4° of trunk rotation vs 50° for pros), the kinematic chain isn't sequencing correctly (forearm peaks 63% slower than pros), and there's almost no follow-through (75% shorter than the pro median).

TECH STACK

Python · MediaPipe · OpenCV · NumPy · SciPy · librosa · Claude Code

Here are the full analysis reports if you want to see how detailed the data gets:
https://attck.com/wp-content/uploads/2026/pro_serve_baseline_report.md
https://attck.com/wp-content/uploads/2026/amateur_vs_pro_report.md