Workflow Automation

How it works.

The system, step by step — each stage is an agent or service with a clear job and a hand-off to the next.

01 · Trigger Identification

Identifying the precise digital events (Webhook, Email, DB Change) that initiate value creation.

02 · Logic Mapping

Designing complex conditional branching, loops, and error-handling protocols.

03 · API Orchestration

Custom API connectors for tools that don't talk to each other naturally.

04 · Human-in-the-Loop

Strategic checkpoints where AI drafts the work, but a human approves it via Slack/Teams.

Built for production, owned by you.

Proven frameworks, not thin wrappers. Open any capability for the technique, the stack and an implementation sketch.

Intelligent Document Processing (IDP)

OCR + LLM extraction to turn PDFs (Invoices, POs) into structured database rows.

Unstructured-to-Structured Pipeline — Technique: Vision LLMs (GPT-4o) + Regex Validation

We build pipelines that ingest messy PDF invoices, receipts, and contracts. We use multimodal LLMs to 'read' the document layout and extract key-value pairs (Total, Date, Vendor) into JSON. We add a validation layer that checks math (Subtotal + Tax = Total) before pushing the clean data to your ERP (NetSuite/QuickBooks).

Stack: Azure Document Intelligence, GPT-4o, Python Pandas, SQL

Lead Enrichment Pipelines

Automatically enriching new leads with Clearbit/LinkedIn data and scoring them before CRM entry.

Automated lead enrichment — Technique: API Chaining & Lead Scoring

When a user signs up with just an email, our system triggers. 1. Query Clearbit for Company Size. 2. Query LinkedIn for Job Title. 3. Use an LLM to analyze their website and classify 'Intent'. 4. Calculate a Lead Score. 5. If Score > 80, route to Sales; if < 80, route to Nurture Sequence. Zero manual research required.

Stack: Clearbit/Apollo API, n8n, Salesforce, OpenAI Classifier

Automated Reporting

Pulling data from Ads, Analytics, and Stripe to generate executive dashboards every Monday morning.

Self-Writing BI Dashboards — Technique: ETL Pipelines & NL Generation

Stop copy-pasting CSVs. We build ETL (Extract, Transform, Load) pipelines that pull raw data from Meta Ads, Google Analytics, and Stripe into a warehouse (BigQuery). We then use an LLM to analyze the week-over-week trends and write a text summary ('CPA increased 10% due to...'). This report is emailed to the C-Suite automatically.

Stack: Fivetran, BigQuery, Looker Studio, Python

Self-Healing Workflows

Error-handling logic that retries failed API calls and alerts admins only when necessary.

Resilient Automation Architecture — Technique: Exponential Backoff & Dead Letter Queues

APIs fail. Scripts crash. Our workflows are designed for failure. We implement 'Exponential Backoff' (retry in 1s, then 2s, then 4s) for network glitches. If a task fails permanently, it is routed to a 'Dead Letter Queue' and logs a ticket in Jira, ensuring no customer data is ever lost in the void.

Stack: AWS SQS, Try/Catch Logic, PagerDuty Integration

Typical stack: n8n, Make.com, Python, Webhooks, AWS Lambda

From experiment to production.

We scope it on your stack, ship a measured first system, and run it against a baseline you can read.