Systematically compose your AI project portfolio
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Why is it important
It is easy to identify dozens of AI use case ideas, even in a single workshop, but much more diligence is required to compose a suitable, synergetic selection of AI projects that is a good fit for your organizationβs situation.
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How we support
We tailor our approach to your organization size and situation:
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AI maturity & status quo check, reviewing your current approach to AI regarding data & AI infrastructure, organizational set-up & culture
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Identification of value-drivers & flywheel of your business models, core process reviews, value chain analysis & peer analysis with regards to AI
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Proposition of core team set-up, AI essentials training and immersive sessions with AI services on our platform querifai.ai
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Proposition of core team set-up, AI essentials training and immersive sessions with AI services on our platform querifai.ai
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Structured, user-centric AI ideation workshops, tailored to above findings
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Joint prioritization of long list of AI project ideas based on aligned criteria
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Expected outcome
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AI maturity review
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Long list of AI project ideas
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Short list/ initial AI project portfolio suggestion tailored to your situation
Modularize your AI project to iterate and learn quickly
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Why is it important
AI/ Data science projects are not as predictable as other types of projects. Iterations and feedback-loops are required to understand if generated insights are valuable for users and what impact data selection/ data quality has on outcomes. The experimental character should be reflected in an agile project design.
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How we support
- Thorough stakeholder interviews to foster end-to-end understanding of user and project requirements
- Structured use case specification workshops to identify most critical building blocks of project from a user-centric perspective
- Review of data availability & restrictions
- Review of technical feasibility of desired solution
- Alignment on initial scope and modules
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Expected outcome
- User stories and derived functional & technical requirements
- Modularized project scope & features roadmap
- Potential roadblocks and contingency strategies
A PoC aims to confirm the most critical hypothesis β before investing significant funds
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Why is it important
A Proof-of-Concept is a cost-effective way for testing feasibility and mitigating risk before full-scale development. It enhances stakeholder confidence by identifying potential issues early on, saves resources, and provides a clear understanding of technical requirements for efficient planning.
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How we support
- Quick validation of solution with existing AI services, readily available on querifai.ai
- Market screening and procurement support, if needed
- Alignment with of interdisciplinary team of our technical & your subject-matter experts on most critical hypothesis to test in PoC
- Implementation of project proof-of-concept with a mixture of local, customer-facing experts and our off-shore team, leveraging our existing platform
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Expected outcome
- Make or buy decisions & vendor evaluation
- Or: Tailormade proof-of-concept, typically within a few days
- Go/No-Go decision for productionalization (MVP) or project refinement
Scaling PoC to data product
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Why is it important
Many organizations fail to turn a Proof-of-concept into a data product, as it can be challenging to scale,
stabilize and integrate the data solution into existing processes.
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How we support
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Refinement of modularized project scope & features roadmap
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Ensure constant alignment between business requirements and technical implementation via our project leads, experienced in both areas
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Implementation of project proof-of-concept with a mixture of local, customer-facing experts and our off-shore team
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Leveraging our existing scalable cloud-based platform, especially for teams without own AI infrastructure
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Expected outcome
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Tailormade MVP, live on querifai.ai within 30 days
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Project plan and feature roadmap for ongoing development
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The MVP allows to assess the impact of the process innovation
Running your solution in the cloud
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Why is it important
Data and AI products not only need to be up and running reliably, but also need to be constantly checked for validity. On the one hand, data distributions and correlation can change over time, on the other hand, user expectations and needs may change.
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How we support
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We connect your system landscape to a scalable infrastructure and models via stable data pipelines
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We run everything on a stable cloud-based environment that is already hosting business-critical solutions
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We monitor technical parameters to ensure reliability
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We monitor customer journey parameters and derive adjustment needs
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We perform continuous market screening for better API offerings, to include faster, cheaper, and/or better options for your tasks
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Expected outcome
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24/7 Accessibility: A data product available on a reliable platform, accessible from anywhere with an internet connection
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Flexible Scalability: A solution that adjusts to your data needs, bypassing physical infrastructure limitations and large upfront investments.
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Proactive Alerts: Regular updates based on user interaction and market analysis, keeping you ahead of the curve.