Invoice processing
Extract line items, validate against POs, and route exceptions to finance.
Service
AI Workflows
Agentic workflows that automate ops, support, and content, reliably.
65%
Reduction in manual task hours
12 sec
Average automated task completion
6 weeks
Typical first workflow live
Problem statement
Why teams struggle without AI Workflows
We build AI-powered workflow automation using LLM agents, RAG pipelines, and orchestration tools like n8n and Make, connecting your docs, CRM, support desk, and internal APIs. Every workflow is observable, versioned, and guarded against runaway costs and bad outputs.
01
Manual process bottlenecks
Skilled staff spend hours on copy-paste, summarization, and routing that machines should handle.
02
Fragile Zapier chains
Simple if-this-then-that flows break when logic gets complex or AI steps need evaluation.
03
Uncontrolled AI outputs
Unguarded LLM steps send wrong emails, update wrong records, or leak sensitive data.
How we help
Key focus areas
- 1Agentic orchestration
Multi-step agents with tools, memory, and human approval gates for high-stakes actions.
- 2RAG document pipelines
Ingest PDFs, tickets, and wikis into searchable knowledge with citation-backed answers.
- 3n8n / Make + custom code
Visual workflows where they help; TypeScript or Python where reliability demands it.
Built for
Who gets the most from this service
Ops teams drowning in repetitive document and data tasks
Support leaders automating tier-1 resolution
Marketing teams scaling content production safely
CTOs exploring agentic automation beyond chatbots
Capabilities & stack
Core capabilities & execution scope
Practical delivery scope for AI Workflows — not a generic feature dump.
What we cover
- Process Agents
- RAG Pipelines
- Document Automation
- Ops Bots
- n8n / Make
- Custom Integrations
Business impact
Why this service matters
65%
Reduction in manual task hours
12 sec
Average automated task completion
6 weeks
Typical first workflow live
Our approach
We combine discovery workshops with senior engineering so every decision is tied to a measurable outcome for AI Workflows.
- Identify: Process mining, ROI estimate, and automation candidate selection.
- Design: Flow diagram, tool permissions, and human-in-the-loop points.
- Build: Agent logic, integrations, and eval datasets.
What you get
Tangible artifacts and a clear handover — not vague “strategy decks.”
- 1Production workflow with monitoring dashboard
- 2RAG index and ingestion pipeline if scoped
- 3Runbook for failures, retries, and rollbacks
- 4Cost and latency benchmarks
- 5Team training on workflow maintenance
Delivery framework
A structured 4-step path
Clear checkpoints and review gates keep AI Workflows transparent from kickoff to launch.
1
Identify
Process mining, ROI estimate, and automation candidate selection.
2
Design
Flow diagram, tool permissions, and human-in-the-loop points.
3
Build
Agent logic, integrations, and eval datasets.
4
Operate
Shadow mode, gradual rollout, and continuous optimization.
Use cases
Where AI Workflows creates impact
Real scenarios where this service produces measurable business results.
Support triage
Classify tickets, draft replies, and escalate with full context summaries.
Content ops
Research, outline, and draft blog posts with brand voice guardrails and human review.
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