Transformation

Turn strategy into sustained behavior change across teams.

• Deliverables: stakeholder maps & measurable goals, evergreen business case, risk/assumption log, comms plan, role-based training & enablement, adoption dashboards, feedback loops.
• How we work: co-create with your leaders, embed change champions, align incentives/RACI, instrument adoption from day one.
• Outcomes: faster time-to-value, higher utilization, reduced change fatigue, durable ways-of-working.

Make AI useful, safe, and measurable, company-wide.

• Foundations: data contracts, consent & preference management, content/prompt policy, model registry, evaluation & red-teaming, audit trails.
• LLMOps/MLOps: retrieval/RAG pipelines, human-in-the-loop, quality/bias/safety/drift monitoring, performance & cost controls.
• Risk & compliance: Responsible AI controls, IP/copyright governance, vendor due diligence, incident response.
• Enablement: CoE & community of practice, secure sandboxes, role-based training (exec/product/engineering/marketing/legal).
• Value realization: portfolio scoring, funding/chargeback, OKRs tied to revenue, cost, risk.

Diagnose, redesign, and automate processes to remove waste and risk.

• Diagnostics: time-and-motion + system logs, benchmarking, service blueprints, gap & root-cause analysis.
• Improvements: automation candidates (RPA/AI), SOPs & controls, SLO/SLAs, FinOps/CostOps, quality & incident management.
• Outcomes: lower cost-to-serve, shorter cycle times, higher reliability and CX/EX.

Build the right things, and prove the impact.

• Portfolio & product: strategy, prioritization (RICE/WSJF), stage gates, discovery→delivery loops, experimentation backlogs, pricing & packaging.
• Commercialization: ICP/segmentation, offers, launch playbooks, adoption & lifecycle programs.
• Measurement: LTV/CAC discipline, incrementality testing, MMM + MTA, adoption & quality scorecards, benefits tracking vs. baseline.
• Outcomes: faster product-market fit, higher retention/revenue, clearer ROI.