Fractional Chief AI Officer services for organizations that need senior AI leadership now and a full-time hire later.
Book a 20-minute fit callCurrently accepting 2 engagements for Q3 2026.
You raised your B, C, or D round, your board has started asking about AI strategy, and you don't have a senior AI leader in the building. You're not ready to hire a full-time CAIO at $400K+, but you need someone senior in the room when vendor, roadmap, and board questions come up.
You have 3 to 8 portfolio companies that need AI capability and none of them can justify a full-time AI executive yet. You want a senior fractional resource you can deploy across the portfolio without managing the relationship yourself.
Your funders are asking about AI. Your board is asking about AI. You have some internal data capacity but no one with the seniority to set strategy, evaluate vendors, and represent AI work to your trustees.
Senior AI judgment on call, when you need it.
Embedded AI executive leadership, without the full-time hire.
From AI ambition to a working system in 8 weeks.
All engagements start with a 20-minute fit call. No proposals before we've talked.
One Degree connects low-income families to safety-net benefits across multiple states. Their team needed to demonstrate to funders that AI could power proactive benefits navigation, surfacing eligibility changes and new programs to families before they had to search. The team had three weeks to build a working demo for a funder presentation.
Designed and shipped a five-module proof of concept in three weeks: voice intake using Whisper, retrieval-augmented benefits matching on a Pinecone vector database, eligibility reasoning over a structured benefits ontology, an SMS notification flow, and a browser-based demo frontend for the funder presentation.
The demo shipped on schedule. The funder presentation proceeded with a working system, not slideware. The architecture is now the basis for One Degree's larger benefits navigation initiative.
A global human rights organization needed to understand what drove and blocked member participation in its national board elections. Thousands of pieces of member feedback existed across post-vote surveys, peer-to-peer text messages, member services emails, governance correspondence, and long-form interviews with non-voters, but the data was unstructured, multi-format, and impossible to synthesize at scale. Manual analysis would have taken months and reached only a fraction of the corpus. Leadership needed a defensible read on member voice that could shape strategy for the next election cycle.
Built an NLP pipeline that turned heterogeneous member feedback into structured, actionable clusters of motivation. The pipeline cleaned and normalized data across six different source formats, used LLM-powered extraction to convert raw text into 880 standardized motivation statements, applied BERTopic semantic clustering tuned to produce specific themes rather than generic groupings, and added an LLM reclassification step that mapped every motivation to one of 77 clusters and filtered noise. Final clusters were faceted by voter status so leadership could distinguish what motivated voters from what blocked non-voters. Delivered as a board-facing report with prioritized strategic recommendations.
The analysis surfaced a finding leadership hadn't previously held with confidence: operational access (reminders, ballots, passcodes) was the dominant driver of participation and the dominant barrier when it failed, far more decisive than mission alignment or candidate quality. The board report gave leadership a defensible, data-grounded basis for prioritizing infrastructure investment over messaging changes for the next cycle. The methodology became reusable infrastructure for ongoing membership analysis.
The State Department's global media monitoring operation tracked foreign policy coverage across embassies in 190+ markets and dozens of languages, but analysts and embassy staff couldn't keep up with the volume of multilingual content. Decisions about diplomatic response, public affairs strategy, and crisis communication were being made on a shrinking sample of what was actually available. Existing tools were fragmented across posts, deployment cycles were slow, and the agency had no shared AI capability across distributed analyst teams.
Architected and built ClipsLab, an NLP-driven platform that automated global media monitoring for the Department and US embassies worldwide. The platform applied sentiment analytics and adversarial pattern detection across multilingual content at scale, replacing manual review with a shared AI capability that analysts and embassy staff could rely on. Led a 25-person engineering and analytics team that designed, shipped, and operated the platform. Led a parallel GCP cloud migration that reduced infrastructure costs 30 to 40 percent while improving deployment velocity. Built and deployed 200+ business intelligence dashboards on top of the platform for embassies and headquarters stakeholders.
ClipsLab saved thousands of analyst labor hours annually, expanded media monitoring coverage across embassies, and received Project of the Year recognition in 2019 for AI-driven operational transformation. The platform and team continued supporting Department operations after handoff, and the methodology became a reference pattern for multilingual signal processing in federal mission environments.
The founder spent 15 years inside federal mission environments, AFRL, Kessel Run, the State Department, the US Agency for Global Media, building AI systems that had to work the first time, in production, in regulated environments. Starsight clients get an executive who has done the work, not a strategist who has only advised on it.
Fixed monthly retainer, fixed sprint fee. No hourly billing. No scope creep. No staff-the-engagement model. Starsight wins when the client's AI program wins, not when the engagement runs long.
Every engagement is designed for the day Starsight leaves. Documentation, decision frameworks, vendor relationships, and internal capability transfer happen continuously, not at the end. The goal is to be replaceable by an internal hire when the client is ready, not to become permanent overhead.
Philip Anderson is a principal-level AI and data engineering leader with more than 15 years building and managing teams that ship production machine learning, agentic AI, and real-time data systems. He built AI and data systems at the US Air Force Research Lab, US Air Force Kessel Run, the US Department of State, and the US Agency for Global Media, and has grown AI and data teams from small cores to more than 25 engineers across distributed organizations. He holds an active TS/SCI clearance (personal). He earned an MA in New Media and Digital Culture from the University of Amsterdam and a BA in Public Policy from Hobart and William Smith Colleges. He serves as elected Technology Steward for the Jones Hill Neighborhood Association in Boston, and speaks on AI, agentic systems, and applied machine learning at international forums.
A consultant produces recommendations and leaves. A Fractional CAIO sits in your leadership meetings, owns the AI roadmap, reports to your board, and is accountable for outcomes. You get an executive in the seat, not a deck. The work is measured by what ships and what your team can run, not by hours billed.
That is the goal. Engagements are built so your organization is ready to hire and onboard a full-time leader when the time comes. Starsight helps write the role, interview candidates, and hand off the roadmap, vendor relationships, and documentation so the transition is clean.
Yes. The Fractional CAIO engagement includes direct management of an internal AI or data team if you have one: setting priorities, reviewing architecture, and supporting hiring. If you do not have a team yet, Starsight helps you decide whether and when to build one.
That is a common starting point. The first work is usually mapping where AI creates real value for your business, separating it from the noise, and producing a prioritized roadmap your board can understand. You do not need an existing AI program to start.
Faster than most people expect. The path is a 20-minute fit call, a short scoping conversation, and a signed agreement, which can happen inside a week. When the need is urgent, an engagement can begin within a day or two of signing. The pace is set by your decision, not by our availability.
Each engagement runs a short initial term, then continues month to month. The Fractional CAIO carries a 90-day mutual notice period, so neither side is locked in beyond a quarter, and the Advisory Retainer runs a three-month initial term. Because documentation and handoff happen continuously, you keep everything produced to date. No long-term contract holds you hostage.
Yes. Starsight is based in Boston and works with clients across the US, remote-first, with on-site time as engagements require. Most leadership and board work happens over video regardless of location.
Yes. Starsight signs NDAs before any substantive conversation about your data, systems, or strategy. Confidentiality is standard for every engagement.
Book a 20-minute fit call. No pitch deck, no sales process, just a real conversation about what you're trying to do.
Book a 20-minute fit call hello@starsightgroup.com