Ensure You Have the Capabilities to Win with GenAI
Adopt Advanced GenAI Capabilities
Coming in Q1 2026
The advanced capability stack you need to deliver enterprise-grade GenAI
Move beyond pilots by designing and implementing reusable GenAI capabilities—agentic workflows, intelligent orchestration, fine-tuning, knowledge graph grounding, and hyper-personalization—built securely and responsibly.
To win, you’ll need more than use cases: you’ll need the platform, patterns, governance, and skills that let teams deliver production-quality GenAI repeatedly, at scale.
The Challenge
Most organizations can launch a pilot or point solution—but struggle to build the advanced capabilities required for scale, differentiation, and control. As complexity and demand grow, it becomes hard to answer questions like:
- Where do we need applied AI/ML depth (vs. prompt iteration)—and how do we build that capability sustainably?
- How do we deploy agentic AI and orchestration safely across tools, workflows, and data without losing oversight, quality, or compliance?
- Which advanced capabilities will actually move the needle (fine-tuning, evaluation, personalization, knowledge graphs)—and what is the fastest, lowest-risk path to implement them?
If you don’t intentionally build these capabilities, GenAI delivery remains fragile, expensive, inconsistent, and difficult to govern—making enterprise scale elusive.
Our Solution
An integrated capability-building engagement designed to assess your current state, define the target capability stack, and implement the highest-impact advanced GenAI capabilities—securely and responsibly.
- Leadership Secure & Responsible AI Workshops — Align leaders on what “secure and responsible at scale” means, establish decision criteria, and define guardrails for advanced GenAI (agents, orchestration, personalization, model adaptation).
- Targeted Scans & Whitepapers — Focused POV and landscape scans tailored to your priorities, including best practices, reference patterns, and platform/vendor implications.
- Detailed Assessments & Acceleration Guides — Structured diagnostics across the capability stack, with maturity views, risks, and actionable recommendations by capability and domain.
- Capability Design & Implementation Support — Hands-on architecture, integration, evaluation, and operating model support to translate recommendations into working enterprise capabilities.
- Technical Coaching — Practical enablement for engineering, data science, and product teams through pairing, reviews, and playbooks to build internal momentum and durable skills.
In a focused, iterative engagement, you’ll move from ad hoc experimentation to a repeatable, enterprise-ready capability stack with a clear roadmap and working implementations.
Areas of Focus
- Applied AI & ML Expertise
- Enterprise Agentic AI Capabilities
- Intelligent Orchestration Capabilities
- Deliver Hyper Personalized Experiences
- Model Training & Fine Tuning Capabilities
- Leveraging Knowledge Graphs for Enterprise Quality GenAI
Outcomes You Can Expect
- A clear capability baseline — Where you stand today across advanced GenAI capabilities (people, process, tech, data, governance, and risk).
- A target-state capability blueprint — Reference architectures, patterns, and an operating model for secure, responsible, enterprise-grade GenAI delivery.
- A prioritized build-and-adopt roadmap — Sequenced initiatives with owners, milestones, dependencies, and 30/60/90-day delivery increments.
- Reusable implementation proof points — Implemented reference components (e.g., orchestration patterns, evaluation harnesses, agent guardrails, knowledge grounding approaches) teams can scale across use cases.
- Accelerated team readiness and velocity — Coaching, playbooks, and engineering standards that improve delivery speed, quality, and control over time.
This is the Solution for You, if:
- You have GenAI pilots (or early production deployments) but need durable capabilities to scale quality, safety, and speed.
- You want to enable agentic workflows, orchestration, and personalization—without increasing operational, security, or governance risk.
- You need a pragmatic path to model adaptation (fine-tuning) and enterprise-quality grounding (knowledge graphs) to materially improve accuracy and user experience.