Future Ready Means AI Ready
AI agents powered by Small Language Models that are much more efficient, easier to train, accurate and secure are the future of Enterprise. Creating a semantic layer so that any AI output is as contextual and accurate as possible is key to successful implementation.
Your genAI & Prompt Engineering Partner
Bring prompt engineering expertise in-house to tailor language models to your specific needs. From fine-tuning models for diverse tasks to crafting adaptable soft prompts, we empower your team to build AI solutions that drive the most efficiencies in your own context.
- Prompt Engineering point that adapt to various contexts
- Enterprise task-specific fine-tuning
- Interpretability and explainability enabling transparency and trust
- Context-aware model guidance / semantic retrieval to enhance relevant output
- Real-world application expertise to ensure genAI solution performance
Use Case Identification and Feasibility Evaluation
Unlocking the potential of generative AI, we pinpoint the ideal use cases for your business while conducting a thorough feasibility assessment, strategically combining technology, tools, and methodologies to deliver tailored solutions that provide the highest ROI.
AI Maturity Assessment
Together we conduct thorough assessments of AI maturity and tech stacks, providing strategic recommendations to enhance and align your technological infrastructure for optimal performance and innovation.
Custom LLM Development
Craft highly scalable and performance-driven Custom Large Language Models (LLMs) to meet your evolving requirements and ensure optimal functionality for the future.
Generative AI Integration
Generative AI integration means weaving solutions into your existing frameworks and stacks to drive measurable results tailored to your business objectives.
Let’s build your Generative AI Roadmap
1. Align Organizational Strategy & AI Guiding Principles
Building a unified strategy and tailoring principles lays the foundation for a cohesive and purpose-driven integration of generative AI solutions.
2. Assess Data & AI Maturity
Assessing the readiness to optimize the deployment and integration of generative AI solutions involves a combination of understanding business processes and possessing in-depth, tech-agnostic knowledge of data engineering.
3. Define & Prioritize Opportunities
A deep understanding of the business, technology, skillsets, and overall data status and AI maturity means that a tech-agnostic partner can help define the ideal use cases for highest ROI in the quickest time to market.
4. Build a Roadmap
A tactical roadmap that defines how DataFactZ will equip you with tools and skillsets to scale across the organization for quantifiable adoption and transformation.
Data is key to genAI…
There is no AI without the right data or implementation. Your data is your competitive advantage- genAI unlocks the power of your data, and you should build the AI apps only you can build.
Modeling techniques are being commoditized, new tools and capabilities are appearing faster than can be tracked, but your business knowledge and data remain your differentiators.
The most important factors in any genAI for Enterprise or Analytics success are all determined by your data foundation.
Data Quality
Quality, well-structured data is essential for accurate generative AI results.
Semantic Understanding:
Understanding the context and meaning of the data is vital. A business semantic layer establishes the meaning and relationships within the data.
Lineage and Traceability:
Knowing the source and history of the data is crucial for tracking changes, identifying errors, and ensuring the accuracy of generative AI output.
Governance and Monitoring:
Governance practices will affect models and output at all levels, and effective monitoring needs to to identify and rectify issues as well as enable the feedback loop.
