Independent advisory practice
Data Sphere Consulting advises Series B through pre-IPO companies on where AI and agents pay off, how to build agent systems you can measure, and how to grow a data organization that stays useful under load. The advice comes from operating experience: I built a data function from the first hire through a public offering.
Approach
Business first
I start from the business, not the tooling. The first question is where AI and agents change the economics, and where they add cost and risk without a return. Sometimes the honest answer is that the current tools do not fit yet, and I will say so.
Vendor-neutral
I do not resell anyone's platform and I take no referral fees. Recommendations are grounded in your constraints and your data, using open foundations like MCP and standard orchestration so you are not locked into one vendor's roadmap.
Measured, not vibes
Agent systems ship with evaluation harnesses so quality is a number your team can track over time, not a feeling from a good demo. When something regresses, you see it before your users do.
Built to hand off
You keep the roadmap, the architecture, and the hiring plan. Every engagement is designed so your team runs the result without me. The goal is durable capability, not a dependency.
Services
Each engagement is scoped to a specific decision or capability. No retainer is required to start, and there is no pricing on this page because the shape depends on your situation.
A clear read on where agents and AI tooling earn their keep in your business, and where they do not. Built for leaders under pressure to “do something with AI” who want a decision they can defend to a board. You walk away holding a prioritized roadmap, honest cost and risk estimates, and a short list of what to skip.
Typically 3–5 weeks
Design for agent systems on vendor-neutral foundations: MCP, orchestration frameworks, retrieval, and tool design, paired with an evaluation harness so quality is measurable rather than assumed. For teams past the prototype who need something that survives real traffic. You walk away holding a reference architecture and an eval suite your engineers own and can extend.
4–8 weeks, hands-on with your team
Team topology, hiring sequence, metrics governance, and the operating cadence that keeps a data function useful as headcount grows. For companies where data has become a bottleneck instead of an advantage. You walk away holding an org plan, a hiring order, and a governance model that holds up as the team scales.
Typically 3–6 weeks
Senior data leadership on a part-time basis for companies that need the judgment before they can justify a full-time executive. I set direction, sequence the work, hire the team, and keep quality honest until a full-time leader is the right call. You get the experience of a seasoned head of data without the full-time cost.
Ongoing, part-time (retained)
Background
I started in applied AI and machine learning at IBM Watson, then became the first data hire at Braze. Over the following years I grew into Senior Director of Analytics, leading a 21-person team while the company scaled from roughly $50M to $500M in ARR and went public.
That means I have built the parts that do not show up in a diagram: the hiring sequence, the metrics governance, the on-call for data quality, and the judgment calls under real pressure. Data Sphere Consulting is where I bring that experience to other teams, so they can move faster without relearning the same lessons.
Contact
Tell me what you are working on and what a good outcome looks like. I read every message and reply within two business days. If you would rather talk, book a time directly.