94% of PLRX missions resolve without a human. from $0.99 per mission when they do The question is not whether your operations will run autonomously — it is whether you build toward that for 15 months or more, or be in production in 12 weeks.
Every mission PLRX runs for you is a mission it understands more deeply than the last. Your payer mix, your exception patterns, your workflow preferences — all of it compounds into a more capable, more tailored autonomous operation over time.
PLRX learns the specific behaviour of your counterparty landscape — which systems deviate from standard protocols, which parties respond slowly, which exception patterns recur across your workflows. That knowledge compounds into every mission and is not portable to another system.
Your exception handling rules, approval routing logic, and operational preferences are encoded in your PLRX environment over time. Replacing the system means rebuilding that configuration from scratch — months of operational tuning that does not transfer.
Every mission PLRX runs creates an auditable record that belongs to you. The longer you run, the richer your compliance evidence base. Early deployment is not just faster ROI — it is a compounding compliance and operational advantage.
Most of the agents proliferating across enterprises today are Personal AI Agents — running on employee devices, tied to user sessions, invisible to IT, ungoverned, unauditable. For simple tasks that is manageable. For operational workflows that touch regulated data, external parties, and financial outcomes, it is a liability.
A second category — agent-building platforms and frameworks — runs agents server-side. But their workflows are trigger-based: they start, they finish. When a workflow needs to wait days or weeks for an external response and resume exactly where it paused, a trigger-based framework stops being the answer. That requires durable execution — workflows that hold state across weeks, not sessions.
PLRX is a different architecture. It runs Enterprise AI Agents — built server-side on dedicated infrastructure, always-on, not tied to any device or session, governed from the first line of code. IT can see everything. Compliance can examine everything. Finance knows exactly what it costs.
Every AI prompt, model response, agent decision, and workflow state transition is captured in WORM-locked, append-only storage. Every action is attributed to a model, a timestamp, and an identity. The audit trail is permanent and unalterable — ready for regulatory examination.
from $0.99 per settled mission. One price per outcome. There is no uncontrolled token bill that scales with agent proliferation. Finance knows exactly what autonomous operations cost — before a single mission runs, not after the invoice arrives.
Dedicated Kubernetes per tenant. No shared runtime. No shared data plane. PLRX agents running your operations cannot touch another customer's environment — by architecture, not by policy. This is what IT approval requires in regulated industries.
The stages below are not hypothetical. They reflect what a well-resourced team with senior engineers actually goes through to reach production on a first agentic operations workflow. The PLRX track is what the same team gets when they use PLRX instead.
The challenge is not finding any one of these specialisations. It is that all five need to coexist in the same team, working on the same codebase, at the same time. Most organisations — including large ones with excellent engineering — have two or three. Rarely all five.
The coordination cost between specialists who do not share context is substantial. The domain expert and the infrastructure engineer do not speak the same language naturally. Getting them to design the right system together takes longer than most organisations anticipate.
PLRX has assembled this team and has been running it in production. The institutional knowledge is embedded in the platform, not in any one person's head.
Deep experience with durable workflow execution — exactly-once semantics, deterministic replay, idempotency at the activity level, durable suspension patterns. Not reading documentation — debugging a workflow that replayed incorrectly after a production crash.
Operational knowledge of the specific workflow being automated — the edge cases, the regulatory requirements, the way third-party systems actually behave vs. how their documentation says they behave. This takes years to accumulate. It is not in a textbook.
Not API integration. Production ML — building and maintaining models trained on real operational data, managing model failures gracefully in a live workflow, prompt engineering for consistent structured output under production load, evaluation frameworks that catch degradation before it affects outcomes.
WORM audit log design that satisfies a regulatory examination. Field-level encryption for sensitive data before persistence. PHI protection at the data layer. Exactly-once guarantees on side-effecting operations that have compliance consequences. Requires engineering skill AND compliance expertise simultaneously.
Running multi-tenant Kubernetes infrastructure with dedicated environments per customer, automated provisioning, network policy enforcement, secrets management, CI/CD pipelines that enforce code quality and security standards, and observability across the full agent fleet.
Same outcome. Very different path.
Durable workflow engineers, domain specialists, production ML engineers, compliance architects, DevOps — all five specialisations, all at once, before a single mission runs.
Architecture, integration, compliance review, production hardening. The last phase always takes longer than the plan says.
Kubernetes clusters, workflow engine, observability stack, secrets management. Your team maintains it indefinitely.
Your engineers learn the vertical from scratch. Edge cases appear in production after go-live.
Engineering salaries and infrastructure costs are incurred before a single production mission runs.
Legal and compliance sign-off happens after your architecture is complete. Usually adds months.
PLRX engineers, domain experts, and ML specialists are embedded in the platform. Your team deploys the outcome.
Workflow mapping, integration, compliance configuration, and validation happen in parallel. No sequential phase gates between you and production.
Dedicated Kubernetes environment provisioned and operated by PLRX. Your team sees outcomes in Mission Control, not server logs.
One year of running live missions across regulated workflows. The edge cases are already handled.
You pay when the mission succeeds. Failed missions are not billed. No capital outlay before results.
WORM audit logs, database-level PHI encryption, dedicated tenant environments. Ready for IT sign-off from day one.
One price. One condition. Volume drives the rate down. Nothing else changes.
For teams getting started or running at lower volumes. Pay as you go, no commitment required.
For teams processing consistent mission volume. Commit to a monthly minimum and your per-mission rate decreases. The economics improve as you scale.
For large organisations with multiple workflow types, custom compliance requirements, or multi-region deployment needs.
The operations team buys PLRX. IT approves it. Here is what your security and compliance review will find.
Most AI vendor evaluations stall at IT review. The architecture is unclear, the data handling is undocumented, the compliance posture is an afterthought bolted on after the product was built. PLRX is designed so that IT review is straightforward, not a blocker.
Dedicated environments mean there is no shared infrastructure to audit. WORM audit logs mean every AI decision is already documented. Open protocols mean there are no proprietary black boxes to evaluate. The architecture was designed with regulatory examination in mind from the first line of code.
Request access and we will show you a live mission running autonomously on a workflow identical to yours — no generic demo, no slide deck, no timeline fiction.