FIND AI by Intetics
Framework for Intelligent Needs Discovery
Move from fragmented pilots and ideas to a scalable,
value-creating execution plan.
Every one of these started with the same step. A disciplined assessment of data, use cases, and feasibility. That's what FIND AI 360 builds in 6–8 weeks.
GRDF · Europe
Acoustic and vibration deep learning on rotating equipment. General Electric reported similar 40% reductions across asset-monitoring deployments.
Public industry reportingSwiss coffee equipment manufacturer
AI-based predictive maintenance for industrial coffee grinders deployed in cafes globally. Downtime and proactive repair forecasting on AWS.
View case study →POSCO · Gwangyang steelworks
CNN on infrared streams of continuous-cast steel slabs. Siemens EWA reached 99.9988% operational quality on edge-camera ML in parallel.
Public industry reportingEuropean railway operator
Drone imagery + ML model for railway infrastructure defect detection. Automated inventory replacing manual inspection.
See our drone ML work →Raytheon Technologies
RAG over CAD modification histories, non-conformance reports, supplier material tests, historical shift logs.
Public industry reportingUS construction and infrastructure firm
AI Knowledge Base with QR-code access on every asset. Natural language search and chatbot replaced static close-out documentation. Built on the same architecture as Factory Memory.
Explore the solution →Industry reference outcomes sourced from public reporting. Intetics delivered cases drawn from our portfolio.
What every one of these has in common.
None of them started with vendor selection or AI tooling. They started with mapping their data, scoring use cases by impact and feasibility, and building a phased plan. FIND AI 360 is that step, compressed into 6–8 weeks.
Align on objectives, conduct 10–20 stakeholder interviews, run readiness surveys, and review key documents.
Analyze all inputs, apply our 1–5 maturity model, generate the readiness radar chart, and identify key systemic gaps.
Co-create and score potential AI/GenAI use cases, visualizing them on an Impact vs. Feasibility matrix to select the right first moves.
Deliver the phased (H0–H2) execution roadmap, quantified value case, dependency map, and final executive presentation.
Assessment · 6–8 weeks
Maps fragmented knowledge sources. Identifies the highest-impact use case.
3–4 months
Custom build for one plant, function, or priority use case.
6–12 months
Multi-plant deployment. Expanded integrations and knowledge graph.
Ongoing
Model evaluation. Knowledge curation. New use cases.
Manufacturers arrive with the same pattern. Knowledge trapped across ERP, MES, CMMS, QMS, PLM, SOPs, Teams, SharePoint. FIND AI 360 quantifies the cost. Factory Memory is what gets built next.
FIND AI Discovery: Assessment identified equipment downtime as the primary cost driver. Impact/Feasibility matrix scored predictive maintenance highest among automation opportunities.
17% lower operational costs
FIND AI Discovery: Assessment showed employees spent 50% of time searching across Confluence, JIRA, and SharePoint. Talent dimension gap identified — no dedicated ML engineers. Hybrid se...
2x faster onboarding
FIND AI Discovery: Assessment identified manual drone image inspection as highest-impact automation opportunity. Tech stack dimension scored 3/5 — legacy systems required integration layer...
30% cost savings
FIND AI Discovery: Readiness assessment revealed 80% of support queries were repetitive documentation lookups. Data dimension scored 4/5 — rich document corpus available. Roadmap prioritized...
80% fewer support calls