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Automation that runs
under real load.

Document intake, approval workflows, orchestration across systems, and AI-augmented decisioning. Built for the edge cases, observable end to end, and human-in-the-loop where judgment is required.

The problem we solve

Why automation doesn't stick.

Workflow automation projects look impressive in demos. Then the source system pushes a UI update and the screen-scraper breaks, or a new edge case appears that wasn't in the spec, or the AI misclassifies a record that nobody noticed for three days. Within six months, half the bots are unplugged and a human is back patching the outputs.

The way through is to treat workflow automation as production software. Typed contracts at every boundary, structured logs on every step, retries and dead-letter queues for the failures that happen anyway. AI for the parts where rules can't cover the variation, but with explicit confidence thresholds and a human-in-the-loop path for everything below them. The audit trail is part of the workflow, not a separate artifact.

Apollo builds workflow automation as production software. Fault-tolerant where it has to be, observable end to end, with human-in-the-loop UIs where the rules run out. The deliverable is software you can run six months from now without rebuilding it.

Where it usually breaks

The screen-scraper RPA bot pointed at an external system. The vendor pushes a UI update, the bot starts producing garbage, and nobody catches it for three days because the bot just keeps running and the dashboard still shows green.

What gets overlooked

The exception path. Demos cover the happy case. Production has bad input formats, partial records, conflicting source systems, and humans who change the rules without telling anyone. If those cases aren't designed for, the automation becomes a problem source rather than a solution.

The compliance reality

Automated decisions in regulated industries need an audit trail: who approved what, on which inputs, with which version of the rules, and which model produced any AI-generated outputs. The audit trail belongs in the workflow itself.

What we deliver

Five shapes of automation work.

Most workflow-automation engagements land in one of these patterns. Each has its own balance of rules, AI, and human review, and each ships with the audit trail the work demands.

Document Intake & Processing

OCR, structured extraction, classification, and routing for PDFs, scans, emails, and forms. AI handles the unstructured parts. Confidence scores drive routing: high-confidence records flow through, low-confidence ones land in a review queue.

OCR · extractionClassificationRouting rulesConfidence scoring

Approval & Routing Workflows

Multi-step approvals with conditional routing, SLA tracking, automatic escalation when steps stall, and the audit record auditors expect. The workflow definition lives in code that gets reviewed, versioned, and tested like any other production change.

Multi-step approvalConditional routingSLA & escalationVersioned definitions

System Orchestration & Integration

The workflow that moves work across CRM, ERP, custom apps, and partner APIs. Idempotent at every step, with retries for transient failures and dead-letter queues for the inputs that need human eyes.

Multi-system orchestrationIdempotent stepsRetries · dead-letterEvent-driven

AI-Augmented Decisioning

Workflows where an LLM or classifier handles the parts that resist rules: review, summarize, categorize, extract intent. With explicit confidence thresholds, escalation paths for low-confidence cases, and the model version recorded against every decision.

LLM & classifierConfidence thresholdsEscalation pathsModel versioning

Human-in-the-Loop UIs

The review interfaces that make exception handling fast: queue management with priority and SLA visibility, side-by-side compare for original document and extracted data, one-click approve or send-back-for-correction, and keyboard shortcuts the team will actually use. Built so that the human work scales as the AI work scales, instead of one becoming the bottleneck for the other.

Review queuesSide-by-side compareKeyboard-first UXSLA visibilityAudit annotations
Reference architecture

A workflow platform, end to end.

Simplified, but representative of how we lay out a production workflow platform. The Workflow Engine is the centerpiece. Every step it runs is independently retryable, every decision it makes is audit-recorded, and every exception it can't handle lands in a human queue.

TRIGGERSINTAKE + EXTRACTWORKFLOW ENGINEACTIONS + HITLEvents & WebhooksCRM · ERP · SaaS · streamsSchedulesCron · windows · cadencesInbox & UploadEmail · SFTP · portal uploadDocument ParsingOCR · layout · structureAI ExtractionLLM · classifier · confidenceValidationSchema · rules · enrichmentState & RetriesDurable · idempotent · resumableWorkflow OrchestrationStep-based · branching · audit-recordedDecision LayerRules · thresholds · escalationSystem WritesCRM · ERP · DB · APIsNotificationsEmail · Slack · webhookHuman Review QueueExceptions · approvals · low confidenceAudit & MetricsEvery step · every decision
Cross-cutting concerns
Typed contracts at every stepRetries & dead-letter queuesConfidence thresholdsAudit trail per runThroughput & SLA metrics
Durable workflow executionAI where rules can't coverHumans where judgment is requiredAudit-recorded by construction
What it looks like in code

A workflow, the way we'd write it.

Step-based. AI for the fuzzy parts. Humans for the rest.

Below is a simplified invoice-processing workflow. Each step is independently retryable. AI handles the extraction. A human-in-the-loop task fires only when the validation confidence falls below a threshold the business agreed to.

Every step gets its own audit record automatically. When someone asks "how did this invoice get approved," the answer is in the workflow run: every input, every decision, every human action that touched it.

apollo / workflows / process-invoice.ts
import { workflow, step, humanTask, ai } from "@apollo/workflow"
import { z } from "zod"

// Step-based execution. Every step is independently retryable.
// The audit trail is generated by the engine, not by hand.

const InvoiceInput = z.object({
  documentId: z.string(),
  vendorId: z.string(),
});

export const processInvoice = workflow(
  { name: "process-invoice", input: InvoiceInput },
  async ({ documentId, vendorId }) => {
    // 1. Extract structured data with AI. Records model + version.
    const extracted = await step("extract", async () => {
      return await ai.extract({
        document: documentId,
        schema: InvoiceSchema,
      });
    });

    // 2. Validate against business rules. Returns a confidence score.
    const validation = await step("validate", () => {
      return validateInvoice(extracted, { vendorId });
    });

    // 3. Human review whenever the rules can't decide on their own.
    if (validation.confidence < 0.95) {
      const review = await humanTask("invoice-review", {
        queue: "ap-team",
        extracted,
        validation,
        slaMinutes: 240,
      });

      if (!review.approved) {
        return { status: "rejected", reason: review.reason };
      }
    }

    // 4. Write to ERP. Idempotent on the workflow run id.
    return await step("post-to-erp", async () => {
      return await erp.invoice.create(extracted);
    });
  },
);
How we engage on automation work

Four phases. Built around the edge cases.

Apollo's standard methodology, applied to the specific failure modes of workflow-automation programs. Each phase produces working software, and the edge cases get designed for before the happy path gets celebrated.

Discovery

Map the work. Pick the steps.

The current process, the volume, the edge cases your team already knows about, and the ones the data will reveal. We pick the steps where AI helps, the steps where rules suffice, and the steps where a human still has to look.

Design

Workflow, HITL UX, audit plan.

Workflow architecture and step definitions. AI integration plan with confidence thresholds and escalation paths. Review UI wireframes for the human-in-the-loop work. Audit trail design that satisfies the relevant compliance regime.

Build

Engine. Integrations. Review UIs.

Workflow engine deployed, integrations connected, AI extraction wired up, review UIs functional. Two-week iterations. Each shipped step arrives with its tests, its monitoring, and the audit fields the auditor will ask for.

Operate

Measure. Tune. Expand.

Throughput, error rates, and AI accuracy in production dashboards. Threshold tuning based on what the data shows. Gradual rollout to adjacent processes once the first workflow is steady. Knowledge transfer to your team along the way.

Technology stack

The shortlist we work from.

What we deliver on. We pick specifically for the workflow's durability requirements, the human-in-the-loop volume, and the systems the work has to touch, and we'll explain why in any proposal.

Workflow engines

TemporalAWS Step FunctionsAzure Logic AppsCamundaInngestRestate

AI & document tooling

OpenAI APIAnthropic ClaudeAWS TextractAzure Document IntelligenceGCP Document AI

Integration & messaging

KafkaAWS SQSAzure Service BusWorkatoMuleSoftCustom REST

All product names, logos, and brands are property of their respective owners. Listed for identification purposes only. Apollo Technologies is not affiliated with, endorsed by, or sponsored by any of the companies named above.

Start a conversation

Tell us what you're trying to automate.

Send a paragraph about the workflow: the volume, the systems involved, the documents or events that trigger it, and the parts that still have humans doing repetitive work. We'll reply within one business day, either with a 30-minute call or with an honest "this is not the right fit; here's who you should call instead."

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