Aelix Echo · Process Automation
Work that runs
while you're not watching.
Audit-clean workflow automation for healthcare, financial services, and manufacturing. Cycle times cut 60-80%, every action logged for SOC 2, HIPAA, and the internal compliance team.
Agentic AI · Process agents · Design-partner preview
From scripted bots to reasoning agents that handle the exceptions.
Traditional RPA breaks the moment the script meets reality. A field is empty, a counterparty is new, a payer's portal changes a label. Aelix Agents are the layer that picks up where the script ends. They reason over the exception, gather the missing context, propose the next step, and escalate when judgment is needed. The deterministic workflow engine is still the system of record. Agents become callable steps inside it.
Process Orchestrator Agent.
Reads an incoming case, decides which sub-workflow it belongs in (eligibility, coding, intake, reconciliation), and routes it. Cites the policy or rule that drove the decision.
Exception-Handling Agent.
Picks up cases that fall out of the deterministic flow. Gathers the missing fields, drafts the correction, and proposes a resubmission. Critical breaches block. Soft breaches escalate.
Handoff Coordinator Agent.
When an action needs a human, the agent prepares the case file: the inputs, the decision points, the agent's draft, and the rationale. The human picks up a worked case, not a blank one.
Continuous-Improvement Agent.
Watches recurring exception patterns and drafts proposed rule changes for the team. Drafts only. No rule ships through the agent. The detection engineer reviews and stages tests.
Trust architecture
Agents propose, the workflow engine acts.
Every irreversible step (submit a claim, post a payable, send a customer message) stays a dedicated, gateable tool. The agent never bypasses a deterministic gate.
Human-in-the-loop at every threshold.
Dollar caps, timely-filing deadlines, regulator-bound actions, customer-facing outbound: each routes through an explicit approval gate. The agent prepares. A person approves.
Audit-logged on every step.
Every agent decision, every tool call, every approval lands in the same tamper-evident log. The audit trail is non-forgeable.
Tenant-scoped and isolated.
The agent runs under the caller's role and tenant. Cross-tenant reads are impossible at the data layer. PHI, PII, and financial data stay inside the trust boundary.
What ships first
Phase 1 ships the Process Orchestrator and Exception-Handling agents in shadow mode. The agent proposes. The deterministic workflow still executes everything. Auto-execution graduates per workflow per tenant only after measured accept-rate clears the threshold. We do not ship fully autonomous as a default.
The thesis
An automation only works if a human can audit it, recover from it, and trust what it did when they weren't watching.
Most automation programs stall on the same thing: the work that runs fine in the demo falls over the moment it meets a real exception, a regulator, or a Monday morning. We build for the Monday.
What we automate
Workflow orchestration that survives the real edge cases.
We model the full process, the happy path and the wrong turns, then encode it as a versioned workflow. Every branch, retry, and timeout is a deliberate design choice, not a discovery in production.
Integration with the systems your business actually runs on.
ERPs, core banking, EHRs, claims systems, ticketing, mainframes. We connect to the systems already in place, and we treat their schemas, latency, and downtime as engineering constraints, not afterthoughts.
AI agents with intent, scope, and guardrails.
Agents handle the steps that benefit from judgment: classification, extraction, drafting, summarization, decisioning. Every call is scoped, logged, and reversible, so model behavior never quietly becomes business policy.
Human-in-the-loop, by design.
Confidence thresholds, exception queues, reviewer workflows, and SLA-bound handoffs. The operator stays in command for the calls that matter, and the system gets out of their way for the ones that don't.
Audit trails, evidence, and the boring parts that pass inspection.
Every action timestamped, every input preserved, every model decision explainable. Built to clear SOC 2, HIPAA, GDPR, and the internal compliance team that's seen too many half-built workflows.
A glimpse of the work
Accounts payable · run #28471
Workflow run trace · accounts payable
+ Agentic AI
Where rules end, the agent begins.
Workflows do the deterministic work fast. Agents take over for the one step that needs judgment, and hand back the moment they're done.
Outcomes
60-80%
Cycle-time reduction on automated paths
99.5%+
Exception capture rate
Days → hrs
Typical workflow turnaround
Measured across Aelix Echo pilot deployments, 2025-2026
Where it runs today
Six industries, the process that bottlenecks them, and the number we give back.
How we work
A short discovery, a measured rollout, and a long, quiet operate.
Discover
We sit with operators, map the process, and find where time, money, and trust quietly leak today.
Map
Every system, hand-off, exception, and human checkpoint, sketched as a working process map before any automation runs.
Orchestrate
Workflow code, integrations, AI agents, and human queues, deployed behind feature flags and observed from day one.
Operate
Monitoring, exception triage, model recalibration, and the iteration that turns a launch into compounding savings.
A note from the practice
We don't build bots that hide what they did. Every action is auditable, recoverable, and explainable, because the moment one isn't, the operator stops trusting the system, and the trust is what kept the throughput.
The Aelix Echo automation practice
When the work needs to run without you watching, we're here.
45-minute working session · One real process, scoped live