AI Strategy, Planning & Implementation Services
We work across the full AI lifecycle: discovering where AI creates real value, planning work your team can execute, building the systems, and getting them to production. Senior practitioners at every step.
AI Strategy & Roadmapping
Most organizations approach AI the wrong way: they start with a technology and work backward to a use case, rather than starting with a business problem and evaluating whether AI is actually the right solution. The result is AI initiatives that generate demos but don't generate value.
We start with your business — your competitive position, your data, your team's capability, your regulatory environment — and work forward to an AI strategy that's grounded in what you can actually execute. The output is a multi-year AI plan and prioritized roadmap your leadership can commit to and your engineering team can implement, not a slide deck of use cases that evaporates after the kickoff.
- AI Opportunity Discovery — structured assessment of where AI creates defensible value in your context
- AI Readiness Assessment — honest evaluation of data, infrastructure, and organizational capability
- Multi-year AI Plans — phased roadmaps with clear milestones, dependencies, and success criteria
- Build vs. buy analysis for AI capabilities and platforms
- Vendor evaluation and selection support
- AI governance framework design
- Executive briefings and board-level AI strategy presentations
- Transformation Strategy — organizational and change management planning
Backlog Planning & Prioritization
AI backlogs have failure modes that traditional software backlogs don't. Stories are harder to size because model performance is uncertain. Data dependencies are non-obvious until they block a sprint. "Done" is hard to define when outputs are probabilistic. And initiatives expand continuously as teams discover adjacent use cases. Standard Agile frameworks don't account for any of this.
We structure AI initiative backlogs specifically for how AI projects succeed and fail: with explicit success criteria tied to business outcomes, data and infrastructure prerequisites surfaced as first-class dependencies, staged experiments with clear go/no-go criteria, and scope boundaries that survive contact with a language model that can always do one more thing. The result is a backlog your engineering team can commit to — and your product organization can actually manage.
- AI initiative backlog structuring from strategy to story level
- Epic decomposition with AI-specific acceptance criteria
- Data readiness assessment mapped to backlog dependencies
- Experiment design with staged go/no-go decision gates
- Velocity and estimation frameworks for AI development work
- Prioritization frameworks: value vs. feasibility vs. risk
- Stakeholder alignment workshops around backlog tradeoffs
- Ongoing backlog refinement as part of a retainer arrangement
AI System Development
Strategy without execution is just expensive documentation. When you're ready to build, the same senior practitioners who created your roadmap implement it — so there's no translation layer between the strategic intent and the technical implementation, and no junior team learning how transformers work on your production timeline.
We develop custom AI systems across the full stack: LLM integrations and orchestration layers, agentic workflows that automate multi-step processes, and the data pipelines and APIs that connect AI capabilities to your existing systems. We build for maintainability — systems your team can own and extend without calling us every time a model version changes.
- Custom LLM integrations and prompt engineering
- Agentic workflow design and implementation
- AI orchestration layers (LangChain, LlamaIndex, custom)
- API design and integration with existing systems
- Automated remediation and decision pipelines
- Evaluation frameworks and quality measurement
- Model fine-tuning and domain adaptation
- Deployment architecture and MLOps pipeline design
RAG & Knowledge Systems
Your organization has years of institutional knowledge locked in documents, databases, and SOPs that don't talk to each other. RAG makes that knowledge accessible to AI and to the people who need it. Done well, it changes how organizations find and apply what they know. Done poorly, it leaks sensitive data, gives confidently wrong answers, and erodes whatever trust you've built with users.
We design and build RAG and knowledge systems with both capability and reliability in mind: retrieval architectures that return relevant context, access controls that respect existing data boundaries, pipelines that stay current as your knowledge evolves, and evaluation frameworks that let you measure whether the system is actually working.
- Knowledge base architecture and information design
- Document ingestion and processing pipelines
- Vector store design and embedding strategy
- Hybrid search: semantic, keyword, and structured retrieval
- Access-controlled retrieval respecting existing data permissions
- Chunking strategy and context window optimization
- RAG evaluation frameworks and quality measurement
- Knowledge graph integration for structured domain knowledge
Security & Compliance Review
AI systems that reach production need to be ready for production — and for the regulatory and threat environment they'll operate in. We integrate security and compliance review into the development process rather than treating it as a separate audit that happens at the end, when the cost of findings is highest.
For regulated industries, we map HIPAA, SOC 2, and FedRAMP requirements to your AI architecture from the first design decision. For organizations taking AI to production, we conduct threat modeling and security reviews that cover the AI-specific attack surface: prompt injection, data boundary violations, access control gaps, and the supply chain dependencies most teams don't think about until something goes wrong.
- AI security assessment and threat modeling
- HIPAA, SOC 2, and FedRAMP compliance readiness for AI workloads
- Prompt injection and data boundary testing
- Access control and data permissions review
- Third-party AI vendor and supply chain risk assessment
- AI red team exercises for production systems
- Security architecture review and hardening recommendations
- Incident response support for AI security events
Transformation Advisory
AI transformation fails more often at the organizational layer than the technical one. A team that doesn't trust AI outputs won't use them. Governance that blocks every deployment won't enable any value. A vendor selection process that optimizes for the demo will leave you with a system that doesn't survive contact with your data.
We provide executive-level advisory for organizations navigating the organizational, governance, and vendor dimensions of AI adoption. This isn't strategy at altitude — it's grounded guidance from practitioners who have run these programs, made these decisions, and know where the bodies are buried.
- AI governance framework design and policy development
- Vendor evaluation and AI platform selection
- Build vs. buy analysis for AI capabilities
- Organizational readiness and change management planning
- AI Center of Excellence design and operating model
- Executive and board-level AI briefings
- Responsible AI framework and ethics policy development
- Fractional Chief AI Officer advisory
Engagement Models
Three ways to work together: a fixed-scope project, ongoing retainer access, or an embedded consultant who functions as part of your team.
Project-Based
Starting at $15,000
Typically 3–12 weeks
Fixed Scope · Fixed Fee
Best for defined deliverables — an AI opportunity assessment, a roadmap and backlog, a pilot build, or a security review. You know what you need, we scope it, and we deliver it for a fixed price.
- Scoped statement of work
- Fixed price, no overages
- Senior practitioner delivery throughout
- Knowledge transfer included
- Clear deliverables and milestones
Retainer
From $5,000/month
20+ hrs/month
Ongoing Advisory · Priority Access
AI strategy and execution is continuous work. A monthly retainer gives you a committed block of senior capacity — for backlog refinement, architecture review, stakeholder prep, implementation guidance, and whatever else the month brings.
- Committed monthly senior hours
- Consume however you need them
- Priority response and availability
- Scales with your needs quarter to quarter
- No per-project scoping overhead
Embedded
$8,000–$15,000/mo
10–20 hrs/week
Fractional · Integrated
A senior AI consultant functioning as part of your team on a consistent weekly schedule — in design reviews, in roadmap planning, mentoring your engineers, and owning AI architecture decisions as your program evolves.
- Fixed weekly hours, consistent presence
- Attends standups and planning sessions
- Owns AI architecture decisions
- Mentors internal team on AI best practices
- Deep organizational context builds over time