AI Automation
Practical AI and automation to streamline your workflows.
We help you apply AI and automation in ways that reduce repetitive work — with human oversight built in.
Under the hood
A real-time view of how this is engineered, run and kept honest in production.
Implementation stack
LangChain / LlamaIndex for RAG pipeline orchestration
LLM API gateway with provider failover and rate limiting
Vector store for retrieval-augmented generation
Custom workflow workers with queue-based job processing
API webhook triggers connecting existing tools
Human-in-the-loop approval steps for sensitive actions
Structured logging + prompt version control
Live handling loop
Trigger
A webhook or scheduler starts a workflow when a business event occurs.
Classify
The worker determines intent and routes to the correct automation.
Retrieve
RAG fetches relevant context from your documents or data sources.
Draft
An LLM produces the candidate output with a cited reasoning trace.
Validate
Rules, confidence checks or a human approve output before any external effect.
Act and log
The action executes via API and is written to an immutable audit trail.
Production integrity
Human approval gates on any action that changes money, access or status.
Versioned prompts and evaluation sets so changes are testable, not whims.
Rate limits, timeouts and fallback paths so automation fails safe.
Every automated step logs inputs, outputs and the decision rationale.
Real-world use
A representative deployment of this service in practice.
Who it was built for
A logistics firm drowning in repetitive customer emails (anonymised).
The problem
Support staff spent most of the day rewriting answers to the same questions about rate plans; tracking status updates were delayed by manual forwarding between teams, so requests aged in inboxes.
The deployment
CoveScreen built a RAG pipeline over the company's verified rate-SOP and FAQ documents, connected it to the support inbox via webhooks, and routed replies back through a human approval step.
The mechanism
Each inbound ticket is classified; retrieval pulls the matching SOP passage; the LLM drafts a reply with the cited source; staff approve with one click; a worker then updates the ticket and mirrors statuses to the shared tracker automatically.
Operational insight
Benchmark targets this is engineered to hold. Stats are framed as targets and SLAs — not fabricated outcomes.
- ~70%
- Inbound tickets auto-drafted for staff approval
- < 2 min
- Draft turnaround vs. minutes of manual writing
- 0
- Silent auto-approvals — every action is gated
Discuss your ai automation needs
Tell us about your goals and we will help you understand the right next step — with no obligation.