Initiative
A database client
Checks nothing until you ask it to. It has no view on what happened overnight, and no opinion about the table that has been quietly growing since March.
Argyronix
Looks continuously, whether or not anyone is watching.
DBA Operating System
Know what is wrong with your databases
before your users tell you.
Argyronix continuously inspects every database you run, explains the root cause of what it finds, and acts only inside the boundaries you approve.
Where It Fits
This Is Not a Database Client
pgAdmin, DBeaver, DataGrip, psql. These are good tools, and most database work still starts by opening one. They give you access to the database. They do not give you three other things — and those three are the job.
Judgment
A database client
Returns rows, plans and catalog contents. Reading them correctly is your job, and doing it well takes years.
Argyronix
Returns the cause, the confidence behind it, and two or three things you could do about it.
Timing
A database client
Runs what you type, at the moment you type it.
Argyronix
Determines when the object is genuinely idle — and, if you have allowed it, acts then.
None of this replaces the client. You will still open one. You will open it already knowing what you are looking for.
How Control Works
Six Levels. You Choose Where to Stop.
Argyronix is an operating system for database administration, not a single automation switch. Levels are granted per capability rather than to the system as a whole, and each one builds on the level below it. The two that can change your database stay off until you turn them on.
Invariants
- No level operates without the level below it.
- No action without a diagnosis.
- No automation without an explanation.
- No action whose result cannot be verified reaches L5.
Open specification
These six levels are not our private vocabulary. The model is published as an open specification under Apache-2.0 — the levels, the invariants, the trust lifecycle, the machine-readable policy format, and a working reference implementation. You can read exactly what we hold ourselves to, and hold us to it.
Read the LORM specification- L0
Structural Awareness
Read-onlyWhat exists across your databases, and what state each object is in.
- L1
Behavioral Observation
Read-onlyWhat is happening right now — collected continuously, not sampled when someone remembers to look.
- L2
Diagnostics & Explanation
Read-onlyWhy it is happening: cause, effect, confidence level, and the data the conclusion rests on.
- L3
Recommendation
Read-onlyWhat can be done — as two or three options with expected effect, risks and trade-offs. Rejecting all of them is a valid answer.
- L4
Controlled Execution
Off by defaultBounded execution inside limits you define, in windows you approve, reversible wherever reversal is possible. A person authorizes each action.
- L5
Policy-Driven Autonomy
Off by defaultAction within policies you have written, where the policy authorizes rather than a person in the moment. Every grant carries an expiry; none of them are permanent.
Levels are earned slowly and lost quickly. Promotion takes a deliberate human decision; an incident, a rollback, a failed verification or lost telemetry demotes a capability on its own. Levels 0 through 3 never write to your database.
The Problem
Every Preventable Incident Pulls Engineering Away from What Matters
41%
Three problem classes account for 41% of the pain signals in our five-year study of practitioner forums and mailing lists — measured on PostgreSQL, the first engine we instrumented
15–20%
An unexpected execution plan change causes 15–20% of production slowdowns — not more data, and not more load
The failure taxonomy is engine-independent: garbage left by versioned rows, statistics drift, plan regression, index decay, the hunt for a safe maintenance window. The mechanics differ per engine. The questions do not.
Read the study — method, taxonomy, and full distributionAlert Fatigue
Teams drown in monitoring noise without actionable context or clear resolution paths.
Reactive Firefighting
Troubleshooting and optimization happen only after incidents — driving unnecessary costs.
Knowledge Silos
Critical tuning expertise lives in one person’s head, creating significant operational risk.
Configuration Drift Impact
Accumulated changes make it difficult to link performance and stability issues to any specific change.
Scaling Blind Spots
Performance degrades silently over time until users notice and complain.
Cognitive Overload
Root cause analysis and remediation depend heavily on individual skills and experience.
Industry Evolution
The Missing Layer in Database Infrastructure
2010s
Monitoring
Observability dashboards
2015s
Diagnostics
Query analysis tools
2018s
Automation
Scripted runbooks
Now
Complexity Gap
Human bottleneck
Next
Continuous Optimization
Argyronix Platform
The Vision
An Intelligent System That Understands, Adapts, and Optimizes
01Continuous Analysis
Every query pattern, every resource allocation, every performance signal — analyzed continuously, without anyone having to go looking.
02Safe Automation
Changes are validated, staged, and reversible. No black-box decisions. Full transparency at every step.
03Adaptive Intelligence
The system continuously learns workload behavior and adapts optimization strategies based on real production patterns.
04Application-Guided Optimization
Embedded libraries allow applications to guide optimization based on real usage context.
A New Layer in the Stack
Introducing a New Control Layer for Database Health and Performance
Current Stack
Fragmented Operational Tooling
Monitoring, runbooks, scripts, and expert intuition remain disconnected across the database lifecycle.
Argyronix Layer
Diagnostic and Control Layer
A dedicated layer that continuously evaluates every instance, explains what it finds, and orchestrates only the actions you have authorized.
Autonomous Future
Self-Optimizing Infrastructure
Database performance and reliability improve continuously with transparent, policy-aligned automation.
The Team
Built by Engineers Who Lived the Problem
"We built Argyronix because database optimization is still manual, reactive, and intuition-based."
Argyronix was founded by three people, each with more than fifteen years in their own field: a software architect and a former CTO, both of whom spent those years building systems that run against high-volume, high-concurrency databases, and a CFO. A senior database administrator with fifteen years across DBA and CIO roles builds the product with us. The workload the founders know first-hand is OLTP and the HTAP drift that follows it — transactional systems that accumulated analytical demands on the same instance, until the two halves started competing for the same resources.
We were not the database administrators. We were on the other side of the queue — the people whose systems depended on one, waiting while someone senior figured out why a query had gone slow. We saw the limits of monitoring tools, the fragility of manual tuning, and the operational risk of resting production databases on one person’s memory. The industry does not need another dashboard or object browser. It needs an operating system.
Our approach combines deep systems engineering with rigorous, research-driven methodologies to build a platform that engineering teams can confidently trust in production.
Timeline
01
Problem research
DBA interviews, market and competitor analysis
02
Specifications
8 documented failure classes, PostgreSQL 13–17
03
Build
In progress
04
First release
September 2026
Three Founders
A software architect, a former CTO, and a CFO — more than fifteen years each in their own field
8 Failure Classes
Documented detection and remediation specifications, from dead tuple accumulation to transaction-ID wraparound
PostgreSQL 13–17
Every supported version covered, including where their permission models differ
Production-Derived
Detection and diagnosis methods drawn from running production systems, not designed in the abstract
Architecture
Intelligent Optimization Pipeline
Integration Layer
Telemetry Ingestion • APIs • Connectors
Continuously evaluates workload telemetry and queries to identify high-confidence optimization opportunities.
Ranks candidate actions against workload context, policy constraints, and expected impact before any change is proposed.
Applies staged checks and rollback mechanisms so only policy-compliant, low-risk actions are approved for execution.
Engine Support
PostgreSQL 13–17
First releaseMS SQL Server
NextFurther engines
RoadmapThe control model above is engine-independent: same levels, same decision flow, same invariants. Only the implementation beneath it changes per engine — which is why further engines, relational and non-relational, join the same model rather than a parallel one.
System Evolution
From Fragmented Operations to a Unified Control Layer
Continuous optimization shifts database operations from reactive to proactive.
EARLY ACCESS — LIMITED AVAILABILITY
Apply for Early Access
We are selecting a small number of organizations operating production database systems to collaborate on early validation and system design.
Applications are reviewed directly by the founding engineering team.
Whether you manage one database or many, Argyronix helps you stay in control.
Early access is open to teams running PostgreSQL. Further engines follow.
Direct collaboration with founding engineers
Full data privacy and controlled rollout
Influence product direction
Research
From Our Technical Journal
Built for Production Databases
Private early access — limited availability
Custom pricing based on your database environment