The decision intelligence platform.
United States
Joined August 2024
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AI can move a decision from days to minutes, but if the evidence underneath it gets thinner on the way, you’ve only accelerated the risk.
Decision Intelligence should shorten the distance from signal to action while keeping the confidence bar intact.
Syntaxia is built around that standard.
Explore the platform: syntaxia.com
30% duplicate accounts.
90% were created by reps who are both still active.
That’s territory friction hiding inside data quality.
Syntaxia reconciles the records and surfaces the ownership conflicts that need review. RevOps gets the call, the owner, the deadline, and 83% confidence.
Fix the ownership. Then trust the territory.
Request a demo: syntaxia.com/contact
When Darwin tried to decide whether to marry, he made a pros-and-cons list.
The format deserves credit. But for serious decisions, it also has real limits.
That is where What a Good Decision Actually Is begins.
It starts with the limits of the list and builds toward a fuller framework for making high-stakes decisions under uncertainty.
Full guide: syntaxia.com/publications/wh…
Every AI interface decides how much work the system takes on, and how much it hands back to you.
Dashboards make you interpret. Chat makes you frame the problem. Workflows make you design the path. In revenue, too much of the decision still lands on the person in the room.
Syntaxia carries more of that work into the system. The call arrives with an owner, evidence, and a confidence bar already attached.
When you evaluate an AI interface, look at what it still leaves the user to carry.
See how Syntaxia works: syntaxia.com
You can't fix what you don't score.
This card tells us where our Go To Market motion stands: 62. Where the decisions take us: 80. What's in the way: targeting and productivity. The decision has an owner, a due date, and 78% confidence. That's above the threshold, so we move.
A decision without a score is a feeling. A feeling with a budget is how companies bleed.
Know where you are. Score what matters. Move the number.
Syntaxia retweeted
On this week’s edition: Anthropic’s 2030 scenario tool, Jacob Coxon’s public resignation warning, YC on harnesses, some useful notes on running Claude Fable 5.1, and a deeper reflection on what happens when coding agents move software closer to the speed of compute.
Read it here 👇
linkedin.com/pulse/against-e…
You can make a bad decision, get a good result, and accidentally turn luck into process. The reverse is just as expensive: a sound decision gets a bad outcome, and you learn never to make that call again.
Before you decide what to repeat, reward, or fix, grade the decision separately from what happened afterward.
What a Good Decision Actually Is is a working framework for making high-stakes, hard-to-reverse decisions under uncertainty, with the reasoning recorded before the result arrives.
Read it here: syntaxia.com/publications/wh…
AI on top of a messy CRM is a very fast way to be confidently wrong.
A duplicate account can start as a reporting error. Once AI reasons over it, the same error can influence a recommendation that leadership acts on.
Data quality has become decision quality. The decision owner sets the confidence bar. RevOps makes sure the evidence underneath it can actually clear it.
Syntaxia is built around that handoff.
See how we approach Decision Intelligence: syntaxia.com/
Syntaxia retweeted
A man spends $2 on a lottery ticket and wins $40 million.
The outcome was excellent, but the decision wasn’t. Mixing those two up is how you end up learning the wrong lesson from both success and failure.
I wrote "What a Good Decision Actually Is" to demonstrate a better way to grade the call.
The framework starts from a simple premise: judge a decision by what was knowable when it was made. Make the assumptions explicit. Put uncertainty back into the analysis. Write down what would prove you wrong before the outcome gets a chance to rewrite the story.
I show how this works in practice with a concrete example featuring a real revenue targeting decision, all the way from the first signal to the final call.
Read the full framework here: lnkd.in/dreSEE_q
A source that hasn’t synced in 31 days can still produce a clean-looking report. A CRM with 57% duplicate account rows can still produce an exact total.
Source freshness and entity resolution issues can make a poor decision look good, undermining the process and outcome.
Before your next planning session, ensure that your data reflects the reality of your business.
A model can solve dramatically more real software tasks when engineers put it inside a purpose-built environment than when they leave it in a bare chat window.
The jump comes from the system around the model.
Business AI is still often dropped on top of CRM data and asked for a call.
At @Syntaxia_ , we're building the harness around that call. The model gets a defined decision and enough business context to answer it. People get a confidence bar before they act.
Which business decision would you trust AI to support today? At what confidence?
Territory coverage has two dimensions: how much of the book you reach, and how far into each account you get.
This team had touched 1.8% of the priced-open book. And in 41% of the accounts they did reach, nobody senior was involved. By week 4, that gap is already shaping the quarter.
The card below models the intervention: +4.6 points of touch rate by January, with accounts that have no senior contact falling from 41% to 15%.
How deep is your team actually getting into the accounts it covers?
You don’t miss quota in week 12.
That’s when you get the receipt.
By week 4, there’s usually enough evidence to make a call. The hard part is deciding how much evidence is enough to move.
What signal do you trust first?
Syntaxia retweeted
Agents need access to act. They do not need the keys.
Against Entropy #17 is about what happens when agents start to enter the places where work happens.
Read it here 👇
linkedin.com/pulse/against-e…
Your ICP is a budget. Every deal you pursue spends rep time somewhere, and some segments return far more of it than others.
This team had 53% of its pipeline sitting in a segment it wins 1% of the time. Coverage still counted every deal. Forecast still rolled them all up. Meanwhile, win rate had been sliding for three quarters.
The card prices the decision: redirect pursuit now, and the model projects 13.4 points of win rate by January. CRO’s call. Sep 15 deadline.
