Proof of execution, not promise: the multi-tenant platform is built — 39 engines in 10 layers, governed and verifiable by evidence, not by slides. This page is for those who will pop the hood: architecture, performance, quality, governance and security, with the real status of each.
Where we honestly stand: Next chapter: dogfooding — Fluxomind running its own sales operation inside the product, the living proof that the agent operates and evolves.
In traditional systems, code calls the LLM when it needs to. Here, the inverse: an orchestrator decides what to do, consults memory and knowledge, plans and invokes tools — all governed by policy and visible as a state graph, auditable step by step.
Routing by cost/quality profile, automatic fallback across providers and cost measured per call.
The agent composes existing primitives — it does not generate loose code. Execution is visible and reproducible.
Extends the tenant's model via governed tools (create-only), with the system's base tables locked — dogfooding, not self-modification.
The platform doesn't deliver value once — it spins. Two coupled loops over a single semantic brain: intent becomes an app, the app generates activity, the activity returns the next action, and each turn starts from a higher point.
You describe the intent; the platform creates object, fields and screens — with no engineering cycle in the way.
Operating generates real data; the platform reads that activity and returns the next action — more precise than the last. Maturity: the full turn is proven by dogfooding — next chapter.
Classifies business entities automatically (embedding + LLM, aligned to Schema.org) — no manual ontology project. Maturity: under construction (Partial).
The thesis: the more you use it, the more Atlas knows — and the cheaper it gets to deliver the next thing. That is what separates a flywheel product from a tool: the tool delivers the same on the thousandth run; the flywheel delivers more, for less. The full turn is what dogfooding will prove.
39 engines organized in 10 layers (verifiable in src/engine/) — below, the layers with representative engines from each. Every engine is an isolated module with strict boundaries — an improper import between internals is blocked by the boundary verifier, which runs in the deploy pipeline. Evolution by layer, without breaking changes.
The scale capabilities are in the architecture. Production numbers arrive with the pilots.
Partial — design capabilityPrometheus metrics, SLOs per tier, anomaly detection (z-score/IQR) and cost trace per execution.
monitoringEngineDistributed queue with retries, backoff and DLQ; layered cache; connection pools and slow-query detection.
queueEngine · cacheEngine · dataEngineMulti-region DR, auto-scaling and self-healing (designed), tier-based backup with RPO/RTO, distributed tracing (OpenTelemetry).
operationsEngineThe principle is “trust earned by evidence”: the platform verifies itself by code — suites that prove isolation, integrity and contracts, not a checklist.
ImplementedIsolation (cross-tenant probes + RLS via pgTAP), integrity, contracts, resilience, compliance and evolvability. Boundaries, tests and the eval gate run on demand and in the deploy pipeline; the isolation suite, in a dedicated workflow.
validationEngineLLM-as-Judge and property-based tests block regressions before a flow is promoted.
evalEngineAPI contracts (OpenAPI) and event contracts (AsyncAPI) verified; tests co-located with the code.
validationEngine · co-located testsDeclarative policy the runtime enforces across the platform, with a person in the loop when it matters.
Partial — core implemented; self-service on the roadmapVersioned guardrails (checksum), evaluated with cache and explain; human approval (human-in-the-loop) on sensitive actions.
policyEngineRate limiting, usage tracking and enforcement per tenant; atomic reservation and approved overrides.
quotaEngineGDPR/LGPD consent lifecycle and an immutable hash-chain audit trail (see Security).
securityEngine · auditTrailEngineStrong multi-tenancy, encryption and masking — from day one, with nothing for the customer to configure.
ImplementedBring your own KMS key (AWS/GCP/Azure). Crypto-shredding: revoke the key and the data becomes unreadable immediately — independently of the platform.
securityEngine · spec-byokDedicated schema per customer + RLS as backstop. The tenantId never comes from the payload — it derives from the request context.
securityEngine · dataEngineSensitive data masked (4 strategies) before reaching the LLM; guards against prompt-injection/jailbreak.
securityEngine · spec-content-safetyDual token + RBAC; SHA-256 append-only trail — tampering is cryptographically detectable and verifiable per tenant.
securityEngine · auditTrailEngine · spec-hash-chainThe platform has automated scanners for SOC2 controls (CC6/CC7/CC8) and GDPR (Art. 17/20/30/32) — verification by code, not a manual checklist. Today they run on demand; scheduled continuous execution is on the roadmap.
| Pillar | What we already deliver | Status | Next step |
|---|---|---|---|
| Security | BYOK, PII masking, RBAC, hash-chain audit | Implemented | Broad fail-closed + external pen-test |
| Quality | Quality invariants, LLM-as-Judge, contracts | Implemented | Load coverage at scale |
| AI-first | Governed multi-agent orchestration; multi-provider with fallback and measured cost | Partial | Multi-worker journey in hardening; more autonomy patterns |
| Governance | Policies+HITL, quotas/entitlements, consent | Partial | Billing/payment + self-service governance panel |
| Performance | SLOs, cost trace, multi-region DR, self-healing (design) | Partial | Production scale numbers (post-pilot) |
| Compliance | Automated SOC2/GDPR scanners (on demand) | Partial | Continuous execution + SOC2 Type II certification (external audit) |
We provide access to the technical data room (arch docs, specs, evidence), an architecture session with engineering and an isolated evaluation environment.