AI Automation Sprint
Ship one production-grade AI workflow in 2–4 weeks.
The sprint turns one high-ROI workflow into a working production system — with API integrations, AI decision logic, human approval, logging, monitoring, testing, and documentation.
Typical outcome: manual workflow → production system in 2–4 weeks
Not sure which workflow to sprint on first? Start with the Audit →
What ships
Everything required to run the workflow in production.
Not a demo. A documented, monitored system your team can operate.
Outcome
Prove value with one workflow. Then expand.
The goal is not to automate everything. The goal is to ship one workflow that matters — reliably, in production — and use that foundation to grow.
At the end of the sprint
- One defined workflow running in production
- Measurable time or cost saved
- AI and approval logic built and documented
- Monitoring and alerting active
- Your team owns it with full documentation
- Foundation to expand to adjacent workflows
Requirements
What Inferon Labs needs from your team.
- One clearly defined workflow.
- One accountable process owner.
- Access to required tools or sandbox environments.
- Sample data for testing.
- Clear approval criteria before production deployment.
- Availability for weekly reviews (approx. 1–2 hours/week).
- Decision-maker available for final go-live approval.
Timeline
Four focused weeks from scope to production.
Simple workflows may ship faster. Complex multi-system workflows may require the full four weeks.
Week 1
Scope & Architecture
- Confirm and finalise workflow scope
- Approve technical architecture
- Set up staging environment and API access
Week 2
Core Build
- Build core workflow orchestration
- Connect APIs and data sources
- Implement AI layer with validation
- Configure logging and data storage
Week 3
Approval & Testing
- Add human approval flows
- Test happy paths and edge cases
- Test failure modes and API errors
- Review with client in staging
Week 4
Deploy & Handover
- Deploy to production with approval
- Monitor first production runs
- Deliver documentation and runbooks
- Training session with relevant team
Deliverables
Everything you need to operate, monitor, and maintain the workflow.
Examples
What a sprint can look like.
These are example sprint projects. Every engagement is scoped based on the actual workflow.
AI Sales Operations Agent
Inbound leads enriched, scored, routed, summarised, and pushed to the correct sales owner — with CRM updates, AI summary, and approval gate.
Support Ticket Triage
Tickets classified, matched to knowledge base, prioritised, routed, and drafted for human review before sending.
CRM Hygiene Workflow
New and existing records enriched, deduplicated, validated, and routed for correction or approval.
Client Onboarding Automation
Signed contract triggers project setup, folder creation, internal tasks, client notifications, and status tracking.
Weekly Reporting Automation
Data pulled from CRM, support, finance tools — cleaned, summarised, and sent to leadership with full audit trail.
What's next
Three paths after the sprint.
01
Operate independently
Your team owns the workflow with documentation, runbooks, and handover materials.
02
Add a Reliability Retainer
Inferon Labs monitors, maintains, and improves the workflow monthly.
03
Expand to an Operating System
Adjacent workflows are mapped and connected into a larger automation infrastructure.
Investment
Typical range: $15,000–$40,000
Scoped after audit when workflow, integrations, and reliability requirements are defined. Most sprints follow a completed Automation Opportunity Audit so scope, integrations, and success metrics are already defined.
Typical investment range. Exact scope and fee confirmed after audit fit review.
Prove value with one workflow.
If you already know which process is slowing the team down, the sprint turns that workflow into a reliable production system.
If the workflow is not yet clearly defined, the Automation Opportunity Audit comes first.