Agentic Automation

Workflow Automation — automating the repetitive middle of your operations.

Document processing, triage, approvals, reporting — we automate the work nobody wants, with AI agents that keep a human in the loop wherever judgement matters.

See the case study ↓
82%Straight-through*
<2minAvg cycle time
100%Actions logged
All evals passing
elhaa · automation pipeline
1Inbound Document / Email
2Extract, Classify & Score
3Confidence Router
4Auto-Complete or Human Queue
82% straight-through
What's included

A complete automation layer, not just a script.

Extraction, routing, human checkpoints, and audit — the full system that makes automation safe to trust.

Document & email processing at scale

Extract, classify, and act on invoices, forms, contracts, and inbound mail automatically.

02

Intelligent triage & routing

Requests classified and sent to the right queue, team, or system without manual sorting.

03

Human-in-the-loop checkpoints

Review gates where confidence is low or stakes are high, tuned to your risk appetite.

04

Full audit trails

Every automated action logged: what was read, decided, and done, and by which version.

05

Exception dashboards

One place to see what needs human attention and how the automation is performing.

Engineering deep dive

How it's actually built.

The confidence-routing logic and the topology — not a simplified marketing diagram.

1Inbound Document / Email / Ticket
2Extraction, Classification & Confidence Scoring
3Confidence Router (auto-complete vs escalate)
4Action Taken + Full Audit Log
confidence-router.ts
// elhaa Confidence Router
const decision = await elhaaAgent.process({
  document: inboundDoc,
  confidenceThreshold: 0.92,
  onLowConfidence: 'route-to-human',
  auditLog: true
});
Case study

Invoice Processing, Exception-Only Review

Distribution · Industrial parts wholesaler

The challenge

Every supplier invoice was read, keyed, and matched to a purchase order by hand — a team of five doing nothing but data entry during month-end close.

The approach

We shadow-ran an AI pipeline against a month of real invoices to prove extraction accuracy before cutover, then went live with high-confidence invoices flowing straight to approval and everything else landing in an exception queue with the AI's reasoning attached.

Invoice intake → extraction & classification → confidence router → auto-post or human exception queue
81%Straight-through rate
4.2×Faster month-end close
100%Actions logged w/ reasoning

*Illustrative example based on a representative engagement.

The difference

The typical approach vs the elhaa approach.

Typical approach
With elhaa
Throughput
Scales with headcount and overtime
Scales with volume — spikes get absorbed
Judgement
Everything handled by hand
AI handles volume, humans handle exceptions
Traceability
Email trails and memory
Every action logged, exportable, versioned
Rollout
Big-bang cutover and hope
Shadow-run proves accuracy before go-live
How the engagement runs

Four steps from mining to monitored automation.

1

Process mining

Identify the flows with the best ratio of volume to judgement — the automation sweet spot.

2

Design the loop

Define what AI does, what humans approve, and what always escalates.

3

Build & shadow-run

Run the automation alongside the manual process to prove accuracy before cutover.

4

Cut over & monitor

Go live with exception handling, dashboards, and continuous accuracy tracking.

How success is measured

Agreed in week one, on a dashboard by go-live.

Automation

Straight-through rate

Share of items processed end-to-end with no human touch.

Control

Exception rate

Items routed to humans — and whether that share falls over time.

Accuracy

Vs shadow-run baseline

Automated output checked against how humans handled the same items.

Speed

Cycle time per item

Intake-to-done duration, before vs after automation.

Works with your tools

Typical systems & standards.

Outlook & GmailSAP & ERPsSalesforceZohoDocuSignExcel & SheetsMessage queues & webhooksRPA bridgesYour ticketing system
Who's involved

Small teams on both sides.

From elhaa
  • Automation engineerBuilds the agent workflows and integrations.
  • Process analystMaps the as-is flow and designs the human checkpoints.
  • QA engineerRuns the shadow-run and signs off accuracy.
From your side
  • Process ownerSets the confidence thresholds and approval rules.
  • Exception reviewers1–2 people who work the exception queue daily.
  • IT contactProvides system access and deployment approvals.
FAQ

Questions about Workflow Automation AI.

In our experience it changes roles more than it removes them: people shift from processing to reviewing, exceptions, and higher-value work. You decide how the capacity gets used.

It depends on document quality and variety, which is why we shadow-run against your real workload and measure accuracy before anything goes live. You see the numbers before you commit.

Yes — approval chains, thresholds, and escalation rules are configured to mirror your existing policy, and every gate is logged.

It stops and escalates. Every workflow has an exception queue with the AI's confidence and reasoning attached, so a human resolves it quickly — and recurring exception patterns feed back into improving the automation.

Often better than the manual process they replace: every action is logged with inputs, decision, output, timestamp, and software version. We map the audit trail to your compliance framework during design so internal audit signs off before go-live.

Sounds like your situation?

A 30-minute call. We'll tell you honestly whether this is the right solution — and what it would take.

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