AI Strategy & Implementation
The gap between AI demos and AI in production is where most organizations stall. We close that gap.
The Problem
Every executive has seen the ChatGPT demo. Most have approved a proof of concept. Very few have AI running in production, delivering measurable business value, and scaling across the organization.
The reasons are consistent. Data is not AI-ready. ML infrastructure does not exist. The team built a notebook prototype but has no path to production deployment. Governance and compliance frameworks are absent. The organization bought a platform before defining a strategy.
The result is predictable: millions spent on AI initiatives with no measurable ROI, pilot projects that never graduate to production, and growing executive skepticism about AI's practical value.
Our Approach
We bridge the gap between AI ambition and AI execution through a three-tiered scaling framework:
Tier 1: AI Exploration
Identify high-value use cases through structured workshops with business and technical stakeholders. Run rapid proof-of-concept pilots with controlled scope and clear success metrics. Build organizational understanding of what AI can and cannot do. Typical duration: 4-8 weeks.
Tier 2: AI Strategy & Governance
Establish the enterprise foundation for scalable AI. This includes data quality standards, model risk management frameworks, responsible AI policies, vendor evaluation criteria, and build-vs-buy decisions. Define the MLOps pipeline architecture. Typical duration: 8-12 weeks.
Tier 3: AI-Enabled Workforce
Deploy AI across the organization with copilots, autonomous agents, and automated workflows. Integrate AI into daily operations, measure productivity gains, and iterate based on real usage data. This is where ROI materializes. Ongoing engagement.
What makes our approach different: we do not just advise on AI strategy. We build AI systems. Our team operates a production AI platform with 68 MCP (Model Context Protocol) servers, 46 autonomous skills, and real-time integration with enterprise systems. We bring that operational experience to every client engagement.
Key Capabilities
- LLM Integration & Fine-Tuning -- Production deployment of large language models with retrieval-augmented generation (RAG), fine-tuning, and prompt engineering for domain-specific accuracy
- Agentic AI Architecture -- Design and deployment of autonomous AI agents that execute multi-step workflows, integrate with enterprise APIs, and operate with human-in-the-loop governance
- MLOps & Model Operations -- End-to-end ML pipeline design: data preparation, training, evaluation, deployment, monitoring, and retraining automation
- Computer Vision & NLP -- Document processing, image analysis, named entity recognition, and classification models for industry-specific use cases
- AI Governance & Risk Management -- Model validation frameworks, bias detection, explainability requirements, and regulatory compliance (SR 11-7, OCC guidance)
- Generative AI Strategy -- Enterprise GenAI roadmaps covering use case prioritization, vendor selection, cost modeling, and change management
- Data Readiness for AI -- Feature engineering pipelines, embedding generation, vector database architecture, and data quality frameworks for model training
- AI Cost Optimization -- Token-level cost analysis, model selection frameworks (when to use GPT-4 vs. Claude vs. open-source), and inference infrastructure optimization
Proof Points
- Top-10 P&C Insurance Carrier: Designed and deployed AI property intelligence platform processing satellite imagery, inspection reports, and public records to generate property risk assessments. Targeting $27M ARR by Year 3.
- National Claims Processing Platform: Built multi-tenant AI claims adjudication system with natural language processing for automated claims intake, reducing manual processing time by 60%.
- Internal Platform (ORAC): Built and operate a production agentic AI system with 68 MCP servers, 46 skills, automated meeting intelligence, financial analytics, and cross-system orchestration -- proving the architecture at scale before recommending it to clients.
Why BAG
Most AI consultancies advise. We build. Our team operates production AI infrastructure daily. We manage model deployments, monitor inference costs, handle edge cases, and iterate based on real-world feedback -- not theoretical frameworks.
When we recommend an architecture, it is because we have deployed it. When we suggest a model, it is because we have benchmarked it against alternatives on real data. When we design a governance framework, it is because we have operated under one.
The advisory-to-product flywheel is our core advantage: advisory engagements fund product development, products prove the architecture, and proven architecture wins the next engagement.
Book an AI Readiness Assessment -- A structured evaluation of your organization's AI maturity across five dimensions: data readiness, infrastructure, talent, governance, and use case pipeline. Delivered in two weeks.