Platform: A New Architecture for Intelligence
MindLab is not a single model. It’s a governed operating system built on four pillars — unified under a control plane that enforces policy, memory, and audit across every workflow.
1) Orchestrator
The Orchestrator plans work as DAGs, selects Capsules, allocates resources, verifies outputs, and keeps humans in the loop. It prevents head‑of‑line blocking with predictive scheduling.
What you get:
- Faster cycle times via adaptive computation and speculative verification
- Consistency via manifests, policies, and reusable playbooks
- Auditability through an immutable execution log (inputs, steps, artifacts)
Deep‑tech highlights:
- Heterogeneous routing (big jobs → big models; small jobs → small models)
- Generate→Verify loops; best‑of‑N sampling; deterministic/procedural verifiers
- Durable execution: transactionality, idempotence & retries, time‑travel debugging
2) Marketplace
An ecosystem of Capsules — outcome‑specific, governed workflows that ship results. Capsules include inputs/outputs, playbooks, dependencies, policies, evaluation results, and a Capsule Model Card. Private Capsules live in your workspace; public Capsules can be composed like Lego.
What you get:
- Outcomes on demand (Idea→PRD, RFPs, Board Packs, Vendor Reviews, etc.)
- Composition: Capsules can call Capsules for complex flows
- Discovery, ratings, usage analytics, and enterprise‑ready compliance signals
3) Work IDE
A no‑code studio that turns domain expertise into productized Capsules. Import examples, define the Capsule manifest, design playbooks, wire dependencies, and run the hardening loop.
What you get:
- Capsule scaffolds and Shared Experts (PDF parser, data cleaner, etc.)
- Auto‑generated Capsule Model Cards and test suites
- One‑click publish to Marketplace (public or private); versioning & changelogs
4) Proving Ground + Context Spine
- Proving Ground: continuous test/eval, red‑teaming, reliability metrics, preference‑learning feedback.
- Context Spine: knowledge backbone for short, high‑signal retrieval and positional optimization — solves the cost/reliability crisis of long context.
What you get:
- Verifiable performance before deployment
- Economical, repeatable context delivery (no long‑context cost spiral)
- Fewer regressions, safer automation
Trust & Compliance
Your data, your control. No training on your private data; fine‑grained access; residency controls. Compliance‑ready. Alignment with GDPR, FERPA, EU AI Act, DSA, ISO/IEC 42001, NIST AI RMF. Transparency. Capsule manifests, Capsule Cards, immutable audit logs. Safety. Stateful defenses against multi‑turn attacks; continuous red‑teaming.