We Find the Right AI Starting Point
and Build It With You
Kamna Ventures operates as a build-first AI lab. We help manufacturing and industrial companies identify where AI creates the most leverage, then deliver a working system in 30–45 days—not a strategy deck.
Start with a Discovery Call →Why “Incubation Lab” Rather Than Consulting
Most AI consulting engagements deliver recommendations. We deliver running systems. The difference is that we work shoulder-to-shoulder with your team—not to advise, but to build, deploy, and validate before we leave.
Build-First
Every engagement produces working software. Our deliverable is a deployed agent workflow, not a PowerPoint. You see it running on your data before the end of the sprint.
Vertical Grounding
We don’t sell horizontal AI platforms. We work in manufacturing, healthcare, and engineering—domains with real operational constraints, compliance requirements, and messy data.
Outcome-Tied Scope
Every sprint is scoped to a specific, measurable outcome: reduce RFQ turnaround by X%, detect failures Y hours earlier, eliminate Z hours of manual extraction per week.
The Buyer Journey
We’ve structured our engagements around how SMEs actually buy and adopt technology—small bets, fast validation, then scale.
AI Opportunity Sprint
We embed with your team to identify the highest-ROI AI use case and build a working prototype on your actual data and systems. You end the sprint with a running demo, a validated architecture, and a clear decision on whether to proceed to pilot.
- Use case selection and prioritization
- Data audit and access setup
- Working agent prototype on real data
- Architecture document + pilot scope definition
Pilot Deployment
We deploy the agent workflow into your production environment with real users and measure against the success metric defined in the sprint. By day 45, you have empirical data on ROI—enough to justify or redirect investment.
- Production deployment (cloud or on-premise)
- Integration with your MES, ERP, or CMMS
- User onboarding and feedback loops
- Weekly outcome measurement vs. baseline
Scale to Production
Once the pilot demonstrates ROI, we expand to additional workflows, departments, or data sources. We also build internal capability—documentation, monitoring, and training so your team can operate and extend the system independently.
- Additional agent workflows and use cases
- Governance, monitoring, and cost controls
- Human-in-the-loop approval workflows
- Internal documentation and runbooks
Domain Focus Areas
We go deep in domains where we have prior engineering and AI implementation experience—not just familiarity.
Example Use Cases We’ve Built
Predictive Maintenance Agent
Multi-agent system that monitors equipment telemetry, classifies deviations, predicts failures 48+ hours ahead, and automatically creates work orders in your CMMS with supporting evidence.
Drawing-to-BOM Pipeline
Automated extraction of part numbers, quantities, materials, and assembly structure from CAD/PDF drawings. Structured output feeds ERP and estimation tools, eliminating manual data entry.
RFQ-to-Quote Automation
Agent that ingests RFQ documents, extracts requirements, cross-references your capability and pricing data, drafts a quote, and routes for human review—reducing turnaround from days to hours.
Quality Inspection Copilot
Computer vision pipeline for real-time defect detection on the production line, grounded in your inspection standards and feeding results to your quality management system.
Maintenance Documentation Assistant
RAG-based agent grounded in your equipment manuals, SOPs, and historical maintenance records. Technicians get step-by-step guidance, fault code interpretation, and relevant precedents.
Process Deviation Detection
Real-time monitoring of process parameters against recipe setpoints. Classifies deviations, recommends adjustments within approved bounds, and escalates out-of-spec conditions for human review.
Start with a 30-minute Discovery Call
Tell us your biggest operational friction point. We’ll tell you whether AI can address it, what the data requirements are, and what a 2-week sprint would realistically produce.
Book Your Discovery Call →