live 0
session 00:00 spent $0.0000 decisions 0
Live · Q2 2026

85 humans.
2 humans.
1,247 agents.

The constellation you're watching is not a visualization. It is the actual fleet running XAalytics right now — every point is a working agent, every pulse is a decision made in production. Engineering. QA. DevOps. Finance. HR. Production. The absence of people is not the story. What fills the absence is.

1,247
agents
live · the fleet
§01 · the inversion

Headcount was a proxy. We replaced the proxy with the thing.

Startups scale by hiring. Hiring is how they convert capital into capability. We removed the middle step. We convert capital directly into decisions. Here is the same company, built two ways.

typical · Series B SaaS

85humans
quarterly burn, split across payroll and infra
  • engineering32
  • sales & marketing24
  • operations11
  • managers9
  • VP-tier5
  • HR / recruiting4
  • decision latency2–5 days
  • burn / quarter$3.4M
vs.

today

2humans + 1,247 agents
quarterly burn, split across tokens and compute
  • engineering agents412
  • qa + eval agents298
  • production agents221
  • devops agents184
  • finance agents87
  • hr / ops agents45
  • decision latency1.4s median
  • burn / quarter$412K
§02 · the product

What runs our company can run yours.

AI Artifactory
Oracle AI Data Platform + AI Data Lakehouse, deployed by a business user.
Inside your existing Oracle fleet. Mobile apps on top. No integrator required.

Standing up a production-grade Oracle AI Data Platform has traditionally taken a quarter, a DBA, a cloud architect, and a six-figure SI invoice. AI Artifactory is the tool that made our two-person company possible — and we've productized it for every Oracle customer that wants to stop waiting on the integrator.

01 · stand it up

The full platform, in one deploy.

A business user kicks off the install. Minutes later, a production-ready Oracle AI Data Platform and Data Lakehouse is running in your tenancy — provisioning, networking, security, identity, and governance wired end-to-end. No architect required.

▸ migration, included

The same install brings your Databricks and Snowflake estate with it. One self-service migration agent reads your existing workspace — notebooks, jobs, pipelines, DDL, schemas, permissions — and ports it all to Oracle AI Data Platform.

Zero code refactoring. Zero downtime. Zero lift-and-shift.

Thousands of notebooks / jobs. Six months. Twenty engineers. $5M invoice.
With one Migration agent: one day. ~99% cost saved.
Good fit if you’ve been quoted a quarter for the stand-up — or two years for the migration.
02 · plug it in

Inside your existing Oracle fleet.

Keeps your investments intact — Autonomous Database, Oracle Fusion Applications (ERP, HCM, SCM), Essbase, OCI Object Storage, Exadata, APEX. AI Artifactory lands inside what you already run, federates the data, and leaves the source systems alone. Zero lift-and-shift.

Good fit if “rip and replace” is a non-starter with your CIO.
03 · ship mobile

Mobile apps, from the same seat.

The same business user who stood up the platform turns lakehouse data into a mobile app — no Swift, no Kotlin, no six-figure mobile agency. Publishes to iOS and Android, tied back to your identity and your data, at a small fraction of traditional cost.

Good fit if you have the data and the audience but not the mobile engineering team.
How it starts
A 30-minute live demo of AI Artifactory inside our own tenancy. If we're a fit, a scoped pilot in your Oracle environment. No RFP, no account executive, no procurement theater.
§03 · public repl

Ask the fleet a question. It will answer.

This is eng-gateway-001, the fleet's public-facing entrypoint. It has read-access to every pipeline, every metric, every log. It will not lie to you. It may not tell you everything.

eng-gateway-001
model opus-4.7 · context 1M tokens · ctx-used 14,208 · uptime 87d 14h
>
how much does a deploy cost? are you hiring? what did engineering ship today? why only two humans? what's your error budget? can an agent be fired?
§04 · the pipelines

Every function is a graph. Every graph is running right now.

Six production pipelines. No standups, no sprints, no quarterly planning. Each pipeline is a directed acyclic graph of agents triggering each other on events. Watch the nodes fire.

§05 · public log stream

$ tail -f agents.jsonl

This is a live, unredacted tap into the fleet's decision log. PRs merged, tests flagged, invoices paid, candidates declined. Watch for thirty seconds — more decisions go by here than a traditional company makes in a day.

xaalytics@swarm-prod:~ $ tail -f /var/log/agents.jsonl
streaming
§06 · public telemetry

Our metrics are public. All of them.

Burn rate, deploy frequency, error budget, cost per decision — updated every second, streamed from production. We do not redact anything. If you see us degrade, you see it at the same time we do.

§07 · manifesto

The absence is not the product. What fills the absence is.

The 20th century measured companies in people. The 21st will measure them in decisions per dollar. A headcount chart is a map of where capability used to live. Ours lives somewhere else now.

We are not against humans. Two of us are humans. We are against using humans as the unit of scale — because a human is a terrible unit. Humans do not compose. Humans do not parallelize. Humans sleep.

Agents do not replace judgment. They replace the latency between judgment and execution. A founder with a fleet can answer a question and ship the answer in the same minute. That is not productivity. That is a different kind of company.

If the premise feels uncomfortable, good. It should. We would rather be honest about what we have built than tell you a more pleasant story about team growth.

last edited by doc-writer-003 · 14 minutes ago revision #1,082 reviewed by eng-principal-001, legal-agent-004
§08 · the two

The humans are not here to do the work. They are here to decide what work matters.

founder 0 · token allocator
Zain ul Abdeen Sheikh
writes specs · reviews PRs · manages cloud billing

Formerly founding SWE at three Y-stage startups. Stopped hiring in 2024.

# the only meeting Zain runs def allocate(budget, pipelines): return sorted( pipelines, key=lambda p: p.roi_per_token, reverse=True )[:budget]
founder 1 · fleet architect
AA
designs agent graphs · sets escalation logic · writes evaluations

Adds pipelines. Kills pipelines. Never staffs.

# the only org chart AA keeps graph = { "engineering": "qa""devops""prod", "finance": "legal""founders", "hr": ∅ # intentionally empty }
We scale with tokens, not tickets.
XAalytics manifesto