AI Workflow Automation
We automate operations where inputs vary by layout and language, combining LLM understanding with deterministic workflow controls.
Where the operational bottleneck appears
- Rule-based automation performs well on structured inputs and fails on the messy data operations teams handle daily: scanned invoices, free-form support emails, and shipping documents with handwritten exceptions.
- When format variation breaks extraction or classification, teams fall back to manual review for edge cases. Those edge cases are usually the expensive ones, so costs stay high even after automation projects.
- The process bottleneck becomes an exception queue where staff re-enter data by hand and retry failed steps, reducing confidence in the automation program overall.
We combine deterministic workflow automation with LLM-powered understanding at the exact stages where variation is unavoidable. Ingestion handles document, email, and form inputs; classification and extraction models interpret meaning rather than rigid keyword patterns; and confidence scoring determines whether a case is auto-processed or routed to human review. The objective is not replacing controls that already work. We keep rule-based logic for validation, routing policies, and downstream updates where deterministic behavior is preferred. LLM assistance is inserted only where templates and hard-coded rules create fragile failure modes. For low-confidence cases, reviewers receive extracted fields pre-filled with model output and trace context, so they correct and approve quickly instead of starting from scratch. This raises straight-through processing rates while keeping ambiguous decisions visible and auditable. Over time, confidence thresholds and prompts are tuned per workflow so automation coverage grows without sacrificing data quality.
graph TD
I["Incoming Input (Email/Document/Form)"] --> N["Ingestion"]
N --> X["LLM Classification & Extraction"]
X --> C["Confidence Check"]
C -->|High confidence| A["Auto-process & Route"]
C -->|Low confidence| H["Human Review Queue"]
A --> S["System of Record Update"]
H --> S
S --> L["Audit Log"]Confidence thresholds are tuned per workflow so high-risk pipelines can stay conservative while mature workflows auto-process more volume. Human review receives model-extracted fields pre-filled so operators validate quickly instead of re-entering data from a blank screen.
Stack selected for this delivery pattern
In practice: A logistics operations team was manually reviewing every inbound shipping document before records could be updated. Document formats varied by carrier and route, and exceptions were buried in free-form notes, so template-based extraction was unreliable. We implemented an automation layer that ingests each file, extracts key cargo and routing fields, and classifies exception conditions with confidence scoring. High-confidence cases are processed automatically through existing update APIs. Low-confidence cases enter a reviewer queue with extracted data pre-filled and source excerpts attached, so operators confirm or correct in seconds. That changed the team's workload profile: humans now focus on ambiguous documents that require judgment, while routine cases complete without touch. Review throughput improved because staff no longer retyped structured fields from scratch, and audit logs made each decision path traceable for operational controls. The process moved from universal manual handling to targeted intervention on true exceptions.
Service-specific answers
RPA breaks on unstructured input; we add LLM-based understanding at exactly the steps where input varies, while keeping deterministic rules everywhere they are sufficient.
They route to a human review queue with the extracted data pre-filled, not a blank task — review is fast, not a redo.
Yes — we integrate via your existing APIs; we don't require replacing your systems of record.