We don't just track tasks — we manage the risk, stakeholders, and dependencies that make AI, cybersecurity, and digital transformation programs genuinely hard to deliver.
New AI platforms, security tools, and transformation technologies arrive faster than most organizations can vet them. We run a structured, evidence-based evaluation process for products supporting AI, cybersecurity, and digital transformation initiatives — so deployment decisions rest on tested results, not vendor demos.
Document the business case, target use cases, must-have versus nice-to-have capabilities, and measurable success criteria with stakeholders — before looking at a single vendor.
Map the vendor landscape, gather analyst and peer references, and narrow the field to a shortlist that genuinely fits the documented requirements.
Assess data handling, hosting model, certifications (SOC 2, ISO 27001), and privacy posture. For AI products, add model transparency, training-data provenance, and governance controls.
Validate integration with your identity, data, and infrastructure stack — plus scalability, resilience, and the vendor's support and upgrade model.
A time-boxed pilot in a controlled environment, tested against pre-agreed acceptance criteria with realistic workloads and real users where possible.
Full cost of ownership — licensing, implementation, operations, and training — alongside vendor viability, product roadmap, lock-in exposure, and exit strategy.
A weighted scorecard across all evaluation criteria gives stakeholders a transparent, defensible basis for the go / no-go decision — and a record for audit.
Phased rollout plan, training, support model, and post-deployment success metrics defined before anything ships to production.
We manage AI implementations end-to-end — from identifying the right use cases to production deployment, governance, and adoption. The program is managed around the model, not just the model itself.
Workshops to surface, qualify, and rank AI opportunities by business value, feasibility, and risk — so investment goes where it earns.
Evaluating data quality, access, lineage, and governance gaps before they stall the build — with a remediation plan where gaps exist.
Applying our structured product evaluation framework to AI platforms, models, and tooling — scored, tested, and documented.
Time-boxed pilots with success metrics tied to business outcomes — not just model accuracy — and explicit go / no-go gates.
Deployment planning, monitoring, operational handoff, and adoption tracking so pilots become durable, supported systems.
Checkpoints for privacy, security, model risk, and acceptable use — aligned to recognized frameworks such as the NIST AI Risk Management Framework.
Every engagement is delivered through one of four flexible models — sized to where your organization is, from a part-time senior lead to a full PMO build-out.
Part-time program leadership for teams that need senior oversight without a full-time hire.
Full-time, on-site or remote leadership embedded directly in your delivery team.
Standing up governance, reporting, and intake processes from scratch.
A focused review of an at-risk program with a clear remediation plan.
AI projects stall for predictable reasons: undefined success metrics, data readiness gaps, and no clear path from proof-of-concept to deployment. We manage the program around the model.
Defining what "done" means before a pilot starts, aligned to business outcomes, not just model accuracy.
Keeping data science, engineering, and business stakeholders moving on the same timeline.
Planning the handoff from pilot to scaled deployment, including monitoring and governance.
Remediation roadmaps, audit cycles, and tooling rollouts touch every team in the org. We give security initiatives the governance structure they need to stay funded and on schedule.
A single source of truth for findings, owners, and deadlines — visible to leadership and auditors alike.
Coordinating SIEM, IAM, and endpoint tooling rollouts across IT, security, and business units.
Structuring evidence collection and milestone tracking ahead of SOC 2, ISO, or regulatory audits.
Platform migrations and process redesigns fail when technical work gets disconnected from how people actually operate. We keep both tracks moving together.
Phased migration plans with rollback paths, data validation gates, and cutover coordination.
Mapping current-state workflows and managing the transition to new systems and processes.
Training plans, communications, and adoption tracking so new systems actually get used.