Layered AI™ — how a regulated process becomes an agentic system.
A four-tier architecture for adaptive regulatory compliance. Government data on top, a polyglot evidence substrate underneath, agents in between — and a deterministic policy engine that decides what those agents are actually allowed to do. Below: watch the same process run the old way and the QuadX way, side by side.
Layer
Same process. Two architectures.
The difference is the entire business case.
Traditional AI
Data enters the system without cryptographic verification. The system cannot guarantee the document has not been tampered with before processing.
Quad Layered AI™
Data is ingested into an isolated, Zero-Trust enclave. Cryptographic signatures are verified before the model even sees the data.
The model proposes. The policy engine disposes. The evidence ledger remembers.
Not a wrapper around a single model.
A production-grade stack assembled from the best components that exist.
Quad builds Layered AI deployments on best-of-breed components — the strongest agentic orchestration, the most capable models for each task, and a polyglot persistence design that treats each data type as a first-class citizen. The architecture is model-portable, cloud-portable, and designed to scale from a single regulated process to a full-enterprise agentic estate.
Multi-agent orchestration built for reliability under regulatory scrutiny.
Agentic orchestration in regulated environments is not the same problem as agentic orchestration in general. You need deterministic control flow, explicit tool boundaries, traceable state, and a graph architecture where every edge is auditable. We use and contribute to the orchestration frameworks that provide exactly that — and we select per-deployment based on your infrastructure, scale, and compliance posture.
Every orchestration layer sits above LG3 — it never bypasses the policy engine.
Agents propose. Policy disposes. Orchestration manages the sequence, the state, and the trace.
OPEN & FRAMEWORK PRIMITIVES
ORCHESTRATION ENGINES & RUNTIMES
No model allegiance. The right model for each task — chosen for intelligence-per-parameter, not brand.
Foundation model selection in regulated AI is a systems question, not a preference. We evaluate against four axes: reasoning depth for complex policy interpretation, latency for real-time decisions, data-residency compatibility for sovereign deployments, and the ability to run offline or on-premises for data that cannot leave the building. No single model wins on all four — so we architect for model composability.
The models below are current Quad deployment options. The architecture is model-portable: swapping the foundation model at LG4 does not require re-engineering the LG1-LG3 stack.
FRONTIER PROPRIETARY - CLOUD HOSTED
OPEN & ON-PREMISES - AIR GAPPED & OFFLINE
PERSISTENCE - RIGHT STORE FOR EVERY DATA TYPE
MULTIMODAL REASONING ACROSS INPUT TYPES
Regulated processes are not text-only. A trade declaration is an image of a bill of lading. A pharmacovigilance case is a scanned fax. A BNYM affordability assessment includes bank statement PDFs. Layered AI handles structured, unstructured, and multimodal input natively — with the same evidence rigor across all types.
Real-time GOV & regulatory data, closed-learning cycles, and HITL feedback — looped between models to drive reasoning.
This is the architectural property that separates Layered AI from a static agentic deployment. The system does not just run inference — it continuously ingests authoritative data, collects human-in-the-loop feedback, routes it through automated review loops, and feeds fine-tuning pipelines that improve model performance within the regulatory constraints already encoded in LG3. The loop is the intelligence. This is the foundation for the AI Accelerators that extend your use cases.
Real-time authoritative data
FCA handbook updates - ONS economic releases - HMRC tariff changes - EBA guidelines - EC regulation... ingested at the boundary, verified, timestamped, and routed into LG2 ground truth.
HITL & closed-learning signals
Human reviewers at LG4 approval gates provide explicit accept / reject signals. Regulator examination outcomes, complaint results, and system audit findings contribute structured feedback.
Automated reasoning & evaluation
Automated review agents evaluate LG4 output quality against LG3 evidence and ground-truth outcomes. Drift detection surfaces when behavior diverges from policy expectations.
Fine-tuning & inference improvement
The combined signal feeds structured fine-tuning pipelines. Models improve on your specific regulatory context, vocabulary, and decision patterns. The agentic system gets more accurate with every cycle.
The AI Accelerators — extending the stack for your sector's future use cases.
The learning loop, the polyglot substrate, and the orchestration layer are not just infrastructure — they are the launchpad for AI Accelerators: pre-built, domain-specific extensions that turn the Layered AI foundation into sector capability. Each accelerator is built on the same architecture, pre-trained on the same regulatory corpora, and tuned for the specific decisioning patterns of your industry.
High-trust evidence isn't a single database. It's the right store for every data type.
A blockchain is the wrong answer to a real problem: how do you store regulated evidence in a way that is secure, durable, queryable, and designed for the specific shape of each data type? Our answer is a polyglot persistence model — purpose-fit storage, write-discipline at the boundary, and explicit data lineage across the whole substrate.
Structured audit store
Decision records, policy evaluation outcomes, agent action logs — structured, relational, indexed by time and actor. Designed for point-in-time queries, regulator submission packs, and aggregated reporting.
Object storage · WORM policy
Document evidence — original submissions, retrieved government data, regulator correspondence — stored as immutable objects under a write-once-read-many retention policy. Jurisdiction-scoped. No delete without retention expiry.
Versioned policy store
Policy module state versioned by date of effect — every version of every rule retained, timestamped against the regulation that produced it. Any decision can be re-evaluated against the rules in force on the day it was made.
Provenance & lineage graph
Data lineage recorded as a directed graph — every data point traced from its authoritative source, through each transformation, to its final use in a decision. Answers the regulator's real question: "where did this number come from, and who touched it?"
Why this matters more than blockchain.
Blockchain-based evidence systems fail regulated industries on three counts: they are expensive to query, they cannot be corrected when a genuine error occurs, and they cannot satisfy GDPR's right to erasure without architectural gymnastics.
The polyglot persistence model gives you the properties that actually matter to a regulator — durability, lineage, queryability, and jurisdiction-appropriate retention — without the constraints blockchain imposes.
WORM-policy object storage enforced at the infrastructure layer — not at the application layer where it can be bypassed.
Directed provenance graph answers the regulator's real question end-to-end: from authoritative source to final decision.
Structured audit store supports point-in-time queries, aggregated reporting, and regulator export formats without ETL.
Jurisdiction-scoped retention policies per data type — satisfies GDPR erasure obligations without breaking evidence integrity.
A fundamentally different approach.
Compliance is the architecture.
Architecture
Single "black box" LLM call handles all logic
Separation of concerns: Models extract, Policy Engine enforces
Reliability
Hallucination risk on business rules and outcomes
Deterministic execution of business logic — zero drift
Compliance
"Prompt engineering" for compliance (unsafe, untestable)
Code-based compliance — guaranteed and testable
Human Review
Outputs require manual review and fact-checking at every step
Agentic execution — no human review needed
Data Provenance
Data provenance is lost inside the model's context window
Cryptographic data provenance and immutable evidence ledger
Integration
Requires UI layer for human-in-the-loop validation
Headless API integration directly into core systems
Execution
Unpredictable execution paths and unverified state transitions
Predictable execution paths and cryptographically verified state
See Layered AI applied
to your process
We build secure, compliant Agentic systems for regulated enterprises. See how Quad can transform your operations in a matter of weeks.