AttractorFactory
Human-supervised AI orchestration

Turn fragmented AI assistance into one supervised workflow.

AttractorFactory coordinates specialized AI agents that analyze, plan, code, review, test and report — open models first, on infrastructure you control, with budgets, human approval and a full audit trail.

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49 execution routes, open models firstHuman review on every sensitive step
Trust model

Supervised workflow loop

From intake to evidence, every step has a role, a route and a review surface.

01
Intake
Describe the task
02
Routing
Agents execute
03
Review
You review
04
Audit
Evidence recorded
76
Model providers catalogued
66
Execution routes integrated
66
Provider integrations maintained
0
Catalog drift (watchlist)

What it is

A control layer between your intent and model execution.

Describe the work; the system decomposes it, routes each piece to the right model, coordinates the agents, and brings the result back for your review — with the cost, the trace and the evidence attached.

Open models by default
Your data stays yours
Budgets you set
Everything auditable

Why now

AI assistance is everywhere. Supervised AI workflows are rare.

Most teams juggle chat tabs, coding assistants and API scripts that don't share context, budgets or review steps. The next step is not a bigger model — it is one workflow where agents cooperate under your supervision, on your terms.

01
Pilot
02
Control
03
Scale
Problem
01

Fragmented assistance doesn't compound.

Each tool helps a little, but nothing connects the analysis to the code to the review to the report — and nobody can audit what happened.
Goal
02

Repetitive work converges to a reviewed result.

Not full autonomy: faster iterations with clear ownership, explicit escalation, and evidence that the outcome can be trusted.
AttractorFactory
03

One supervised orchestration layer.

Task decomposition, model routing, budget gates, human checkpoints and audit trails — with open models first and your data under your control.

Doctrine

Why convergence matters more than raw model power.

01

State space

Your work has code, documents, decisions, constraints and exceptions. The system maps that state before any agent acts.
02

Target states

Every workflow aims at a testable end state: reviewed, tested, reported, approved or shipped — never 'the model said so'.
03

Feedback under control

Telemetry, human review and error traces improve the routing over time, without letting the system drift outside your boundaries.

Trust model

Human oversight, traceability and cost control — by design.

01

Humans stay in charge

Approval points, escalation paths and review surfaces are designed into every workflow, so automation assists your decisions instead of hiding them.
02

Auditable by construction

Traceability, logging and documentation are architectural requirements — aligned with the European risk-based approach, useful far beyond compliance.
03

Budgets enforced before spend

Cost limits are checked before calls are dispatched, with fallbacks when a route is unavailable. You define the ceiling; the system respects it.

Capabilities

What you can do with it, from day one.

01

Multi-agent improvement workflows

Coordinate specialized agents that analyze, plan, code, review, test and report on your codebase — each step visible, each result reviewable.
02

Open models first

The default fleet is open models — local or hosted. Connect paid providers only for the tasks where they earn their cost, and see why.
03

Local and sovereign deployment

Run the whole loop on your machine or your infrastructure. Your code, prompts and results never leave the boundary you set.
04

Hybrid model routing

Route each task across local runtimes, free GPU endpoints, freemium APIs and premium providers — with budget caps and automatic fallbacks.
05

Budget-aware AI usage

Set a budget before work starts. The system reserves, meters and stops at the limit — no surprise bills, no silent overruns.
06

Human approval gates

Nothing sensitive ships without you. Review surfaces, approval steps and escalation paths are part of the workflow, not an afterthought.
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Who it's for

Built for people who want AI leverage without losing control.

01

Developers and software teams

Run analyze → plan → code → review → test loops on real repositories. You keep the decisions; the repetitive passes accelerate.
02

Technical founders

Ship faster without handing your codebase to a black box. One supervised workflow instead of five disconnected AI tabs.
03

AI builders

Compose specialized agents with routing, budgets, retries and stopping conditions you can actually observe and tune.
04

Privacy-conscious teams

Code, contracts and client data stay on infrastructure you control. Open models by default; external calls only when you allow them.
05

Legal and document-heavy work

Summaries, clause extraction and document comparison prepared by agents — professionals stay the final authority on risk and advice.
06

Operations, HR and finance

Recurring documents, reports and reconciliations handled with explicit review points, so the exceptions reach a human — always.

Architecture preview

A routing layer between work, agents, policies and model providers.

1

Task intake and decomposition

2

Policy, budget, privacy and risk gates

3

Open-model fleet: local, hosted, freemium — premium optional

4

Multi-agent execution with review, test and retry loops

5

Telemetry, outcomes and convergence signals

6

Human approval, audit trail and integrations

Evidence

Measured before trusted: outcomes, routes, budgets, convergence.

76
Model providers catalogued
66
Execution routes integrated
66
Provider integrations maintained
0
Catalog drift (watchlist)
01Describe the work
02Agents execute, supervised
03You review the evidence
04Converge

Tell us about your stack and your constraints — we'll show you what a supervised AI workflow looks like on your own terms.

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