Every engagement produces a running agentic system — not a report, not a prototype. Quad builds and operates AI infrastructure for regulated enterprises. Fully auditable, regulator-ready, and live in production.
Engagement Model
6–10 weeks · Fixed scope · Fixed price
Discovery → Design → Deployed agent. In weeks, not quarters.
We take one regulated process — onboarding, underwriting, claims triage, trade booking — and turn it into a running agentic system. You leave with a deployed agent, a compliance layer, and a signed audit trail. Not a prototype. Not a slide deck.
Engagement Model
4–6 weeks · Modular deployment · Ongoing licence
Compliance embedded into every decision — not bolted on at the end.
The Layered AI™ Compliance Framework wraps your agentic workflows in an enforcement layer that a regulator can audit, an auditor can sign, and a board can explain. It runs inside your infrastructure, not ours.
Engagement Model
8–12 weeks · Industry template + customisation
Running agents tuned to your industry's specific regulatory stack.
Off-the-shelf agentic infrastructure ignores regulatory context. This offering deploys pre-built agent patterns tuned to Financial Services, Insurance, Trade Finance, Asset Management, or Supply Chain — with the jurisdiction-specific compliance rules pre-loaded.
Engagement Model
Ongoing · Monthly retainer · SLA-backed
Your agentic systems. Fully operated by Quad. SLA-backed.
Don't want to run the infrastructure? We do it for you. Quad operates your agentic workflows end-to-end — monitoring, incident response, compliance updates, agent retraining — under a service agreement with defined SLAs and escalation paths.
Engagement Model
3–6 weeks · Fixed deliverable · Signed outputs
When you need to show a regulator exactly how your AI makes decisions.
A point-in-time engagement for enterprises facing regulatory scrutiny, audit, or enforcement inquiry. We produce a complete, regulator-ready evidence pack for any AI system — trained by you, procured by you, or built by us.
RPA solved a real problem in 2015. It is solving a different problem in 2025 — the problem of how to maintain a fragile estate of bots that break when anything changes, cannot handle exceptions, and leave no evidence trail a regulator would accept. If you have a meaningful RPA estate, the question is not whether to migrate. It is how to do it without rebuilding everything at once.
RPA bots execute hard-coded sequences against UI elements and fixed data schemas. When a regulator updates guidance, when a system UI changes, or when a new product variant is introduced, bots break. Silently. The fix requires a developer, a UAT cycle, and usually a production outage window. In a regulated process, there is no such window.
RPA has no judgment. Anything outside the happy path is either failed silently, queued for human handling (defeating the automation), or — worst — processed incorrectly and silently passed downstream. In an insurance claim or a trade declaration, a mishandled exception is not a queue item. It is a compliance exposure.
RPA bots process and move on. The audit record is whatever the downstream system captured — which is typically the outcome, not the reasoning, the inputs, or the decision logic applied. When a regulator asks "why did your system make this decision?", RPA cannot answer that question.
The comparison is tempting because both automate processes. But the architectural contract is completely different. RPA scripts steps. Layered AI reasons within constraints. RPA is brittle by design — it executes exactly what it was told. Layered AI is adaptive by design — it executes within codified policy, against live authoritative data, and produces evidence of every decision.
The migration path is not a rip-and-replace. We run Layered AI alongside your existing RPA estate, process by process, as a shadow system first. We prove equivalence on outputs, demonstrate superiority on exceptions and evidence, and cut over one process at a time. Your RPA estate shrinks methodically. Nothing breaks.
"The bots worked until the FCA changed the Consumer Duty guidance. Then seventeen of them broke simultaneously and we spent six weeks fixing them. That was the conversation that started this."
Head of Operations, UK Retail Bank
Bot ran the old FCA stress-test rule for 6 weeks post-update. 340 loan decisions made on stale methodology. Potential Consumer Duty exposure. Six-week developer remediation cycle.
Policy layer ingested the FCA update on publication day. All subsequent decisions applied the updated stress-test methodology automatically. Zero developer intervention required.
Hard-coded sequences against fixed UI and schema
Agents reason within codified policy and authoritative live data
Breaks silently when regulation, UI, or data changes
L03 policy layer updates when regulation changes — agents adapt same day
No exception handling — falls off the edge or stalls
Exception handling by design — agents assess, escalate, or resolve with reasoning
Requires developer intervention for every rule change
Policy updates are configuration, not re-engineering cycles
No reasoning — executes steps, not decisions
Every action is a reasoned decision with a cited justification
No evidence trail — audit record is whatever downstream captured
Polyglot evidence substrate records every prompt, retrieval, decision and action
Cannot cite regulatory justification for any action
Every action cites the regulation, tariff code, or policy rule that authorised it
Maintenance cost grows with every process added to estate
Shared L01-L04 architecture across all processes — cost per process decreases
Regulator cannot examine the decision logic — only the outcome
Regulator can examine reasoning, inputs, policy applied, and evidence — end to end
Bring your most painful compliance or operations workflow. In 30 minutes we'll tell you which offering solves it, what the artefacts look like, and what live delivery takes.
