[ AI AUTOMATION ]
Workflow Automation Agency
Point-and-click automation tools handle simple trigger-action sequences. We build LLM-augmented workflows that process natural language inputs, extract structured data from documents, route work intelligently based on context, and handle exceptions that would break a rule-based system.
LLM-Augmented Workflows, Not Just Zapier
Zapier, Make, and Power Automate are excellent for connecting structured data between SaaS tools. They fall apart when the input is a PDF, an email, a form with free-text fields, or a decision that requires understanding context. We build workflows that use LLMs at the points where structure ends: extracting fields from documents, classifying unstructured inputs, generating responses, and routing based on intent rather than keywords.
Every workflow we build includes explicit exception handling — what happens when the LLM is uncertain, when a document is illegible, when a required field is missing. Exceptions are surfaced to a human with full context, not silently dropped or incorrectly processed. Your team sees only what needs a decision; the volume work runs autonomously.
Workflow Automation Services
LLM-Augmented Workflow Design
We design and build workflows where LLMs handle the steps that require language understanding: classifying inputs, extracting structured data, generating responses, and making routing decisions. The LLM operates within defined guardrails with fallback to human review when confidence is low.
System Integrations
We connect your workflow to the systems it needs: CRMs, ERPs, ticketing systems, document stores, email, Slack, and custom APIs. Integrations are built at the API layer — stable, maintainable, and not dependent on UI scraping or brittle connectors.
Document Processing & Extraction
Invoices, contracts, onboarding forms, compliance documents — we build extraction pipelines that pull structured fields from unstructured documents using LLMs with validation logic. Output lands directly in your target system, flagging exceptions for human review.
Monitoring & Reliability
Every workflow runs with observability: throughput metrics, exception rates, LLM decision logs, and latency tracking. We review weekly post-launch, tune prompts and routing logic as your inputs evolve, and alert on anomalies before they become problems.
Frequently asked questions
What’s the difference between workflow automation and process automation?
The terms are often used interchangeably, but workflow automation typically refers to automating sequences of tasks within a defined process — routing a document through approval steps, triggering notifications, updating records. Process automation is broader: it encompasses redesigning the whole process, not just digitising the existing steps. We do both, but we always start with understanding the process before designing the automation.
What tools do you use for workflow automation?
We select tools based on what the workflow requires. For orchestration: LangChain, LlamaIndex, Temporal, or custom Python depending on complexity. For integrations: REST APIs, webhooks, and official SDKs — not screen scrapers. For simple trigger-action steps where it fits, we use n8n or Make. We avoid locking you into a single vendor’s platform. All code goes into your repository.
How is LLM-powered automation different from traditional workflow automation?
Traditional workflow automation requires every input to be structured and every decision to be expressible as an explicit rule. LLM-powered automation handles unstructured inputs (emails, PDFs, free text), makes decisions based on context rather than keywords, and adapts to variation without breaking. The trade-off is that LLM steps require monitoring and prompt maintenance — they’re not set-and-forget. We build the observability layer that makes them manageable.
What types of workflows are best suited for automation?
High-volume, repeatable workflows where the inputs vary but the overall structure is consistent. Best candidates: document intake and data extraction (invoices, contracts, applications), customer request triage and routing, internal approval and sign-off chains, data synchronisation between systems, and report generation from structured data sources. If a workflow currently requires a human to read something and decide what to do next, it’s worth assessing for automation.
Tell us about the workflow you want to automate
Describe the inputs, the decisions, and the outputs. We’ll assess what’s automatable, where LLMs add value over rules, and what a realistic build looks like.
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