Understand your data foundation and identify your highest-impact opportunities
You know data and AI can improve efficiency, reduce costs, and enhance decision-making. But before investing in new systems or initiatives, you need clarity: What data do you actually have? What's its quality? Where are the gaps? And which opportunities are worth pursuing first?
Without this foundation, organizations risk investing in technology that doesn't align with their data capabilities or business priorities.
We conduct structured discovery through three phases:
Preparation - Review your process documentation, system architecture, current pain points, and operational challenges through the provided materials and preliminary discussions
Discovery sessions - On-site or virtual structured interviews with cross-functional teams covering strategy, operations, technology, quality, and organizational readiness. Includes facility walkthrough (if applicable), system architecture review, and initial findings presentation
Analysis and reporting - Comprehensive assessment development with quantified maturity scores, industry benchmarking, gap analysis by functional area, and actionable recommendations
A comprehensive, quantified assessment of your current data landscape and a clear path forward:
Format: Comprehensive assessment report including current state analysis, dimension-by-dimension evaluation with detailed findings, your Data and AI Readiness Score with industry benchmarking, gap analysis identifying improvement priorities, ROI evaluation for recommended initiatives, and a proposed proof of concept with defined success criteria to guide next steps.
Timeline: 2–3 weeks, including on-site discovery sessions if necessary (2-3 days)
Participants: Cross-functional team including operations, quality, IT, planning, and leadership
Investment: Price available on request, reflecting scope and complexity. Travel expenses are invoiced separately if on-site.
Discover and prioritize AI and analytics use cases specific to your operations
AI and advanced analytics offer proven benefits across industries, but knowing where to start is difficult. You need to separate hype from practical applications, understand what's possible with your current data, and identify opportunities that align with your business priorities and operational reality.
Generic use cases don't account for your specific data readiness, organizational constraints, or strategic goals.
Structured facilitation through the workshop session:
Context setting - Review current operational challenges, business priorities, and relevant AI/analytics applications in your sector
Opportunity discovery - Facilitated brainstorming across functional areas relevant to your organization (e.g., process optimization, quality improvement, predictive maintenance, demand forecasting, resource optimization)
Evaluation and prioritization - Structured scoring of each opportunity using a consistent framework:
Sprint planning - Define specific implementation sprints for top 3–5 opportunities, each with: goal, problem solved, required data, technical approach, success metrics, timeline
A facilitated workshop that produces actionable prioritization:
Format: Workshop summary report, detailed use case catalog with scoring rationale, prioritization matrix with visual quadrant chart, sprint roadmap for top opportunities
Timeline: 1 day workshop + 1 week follow-up documentation
Participants: 4–6 people recommended, representing operations, quality, maintenance/engineering, planning, IT, and leadership (must include decision-makers)
Investment: Price available on request. Can be conducted standalone or as a follow-up to the Assessment. Travel expenses are invoiced separately if on-site.
Validate your data strategy with a targeted, low-risk pilot project
Before committing to large-scale data or AI initiatives, you need proof that the approach works for your specific operations, with your actual data, in your environment. Without validation, organizations risk investing in solutions that don't deliver expected value or that encounter unforeseen implementation barriers.
A well-designed POC demonstrates value, tests feasibility, and builds organizational buy-in before major investment.
Structured sprint implementation in three phases:
Sprint planning - Define specific problem to solve (based on assessment/workshop recommendations), identify target area, confirm data sources and access requirements, set success criteria (business outcomes + maturity improvements)
Implementation - Data collection and integration setup, dashboard/model development with iterative testing, refinement based on user feedback, and adoption support to ensure actual usage
Validation and handover - Performance measurement against success criteria, comprehensive documentation and knowledge transfer, focused re-assessment of affected areas to quantify improvement, scale-up recommendations, and roadmap
A functional proof of concept that demonstrates measurable value and serves as a template for scaling:
Common POC types (adapted to your industry and operations):
Each POC includes:
Timeline: 6–8 weeks of implementation
Investment: Price available on request. Complexity varies by POC type—operational dashboards (lower complexity), predictive analytics (medium complexity), AI/computer vision (higher complexity). Investment includes full implementation cycle, data integration setup, user training, documentation and knowledge transfer, and post-POC assessment. Travel expenses are invoiced separately if on-site.
Problem solved: Downtime events and productivity losses go unrecorded when operators rely on manual logging
Target area: Specific production line or equipment group
What we build:
Deliverables:
