WHAT WE DO
Five Disciplines.
One Integrated Practice.
Most firms sell you a strategy deck or an engineering sprint — rarely both, and never with an acquirer's eye on the P&L. We run all five as one connected capability, because in practice, your data architecture, your AI roadmap, and your M&A pipeline are the same conversation.
Strategic AI & COE Enablement
Turn AI from a pilot project into institutional capability.
- AI Readiness & Maturity Assessments — quantified scoring against a defined framework, not a gut check
- 12-Month AI Roadmaps — sequenced, budgeted, tied to business KPIs
- Use Case Discovery & Prioritization — separate the use cases that pay back in quarters from the ones that pay back never
- AI Center of Excellence Setup — governance, tooling standards, ROI tracking, and operating model so AI scales past the first three use cases
Outcome — You leave with a roadmap your CFO will fund and your engineers will actually build.
M&A & Business Acquisitions
We evaluate deals the way we evaluate AI use cases — for durable, defensible advantage.
- Target identification and sourcing
- Due diligence through an operations-and-AI lens
- Valuation and deal structuring
- Post-acquisition AI integration
- Operational turnaround advisory and exit planning
Outcome — Diligence that catches the AI and data liabilities a generalist advisor won't know to look for.
Applied AI & Engineering
Where the roadmap becomes production software.
- Model fine-tuning for domain-specific accuracy
- GenAI context engineering — retrieval architecture, prompt systems, evaluation harnesses
- Token cost optimization — model routing, prompt compression, and caching that cut LLM spend without cutting capability
- Enterprise knowledge graphs — connecting fragmented systems into a queryable map of the business
- Industry-specific AI agents — AP/AR reconciliation, supply chain exception handling, and other high-friction, high-volume workflows
Outcome — Agents that resolve exceptions instead of creating tickets for a human to resolve later.
Enterprise Data & Lakehouse Architecture
AI is only as good as the data foundation underneath it.
- Cloud-native platform modernization
- Databricks and Snowflake implementation and integration
- Real-time streaming pipelines — Kafka, Snowpipe
- Data governance and access architecture built for AI workloads, not just BI dashboards
Outcome — A lakehouse that can actually feed a model in production, not just a quarterly report.
Physical AI & Edge Computing
Some decisions can't wait for a round trip to the cloud.
- Edge hardware selection and setup
- Cloud integration for hybrid inference architectures
- Low-latency processing for shop floor, field, and site-level operations
Outcome — Inference where the sensor is, not three seconds and a network hop later.
Engagement Models
Project-Based
Defined scope engagements. Fixed scope and deliverable timeline. Clear milestone-based billing. Discovery to delivery in 4–8 weeks. SOW executed before work begins.
Best for: AI assessments, roadmaps.
Retainer
Ongoing advisory relationship. Monthly hours bank (flexible use). Priority scheduling and access. Quarterly strategy review included. Cancel anytime with 30 days' notice.
Best for: Continuous AI enablement.
Hourly Advisory
Targeted spot expertise. No minimum commitment. Flexible on-demand scheduling.
Best for: Second opinions, due diligence, expert review.
We don't publish rates. Every engagement is scoped to the client — book a call and we'll size fees with you live.
Not Sure Which Engagement Fits?
Run the AI Readiness Assessment first, or book time directly — we'll recommend the right entry point and walk through pricing live, based on where you actually are, not where a sales deck assumes you are.