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Fulkerson Advisors
Cambridge·Massachusetts

AI strategyfor organizationsthat need it toactually work.

We help companies move beyond pilots — designing, building, and shipping AI systems that survive contact with production.

Schedule a callRead our work

Selected engagements

  • Profit Isle
  • IntelliVen
  • ALEE
  • $55B US Pension FundIndexing automation
  • Top-5 US Law FirmPre-litigation automation
  • Caribbean ConglomerateDemand forecasting CoE
Case file · No. 01

Financial Services · Pension Fund

Equity Index Strategy Backtesting

Sector
Asset Management
Region
United States
Engagement
Implementation & Strategy
Duration
16 weeks

The challenge

A US pension fund wanted to evaluate candidate equity index strategies against historical data before committing capital to them. The existing evaluation process ran on a quarterly cycle, and strategy ideas waited on it.

What we built

We built a web application for backtesting equity index strategies, using GenAI to process the historical financial data the strategies are tested against, and automated the fund's proprietary model for tracking changes across S&P US indexes.

Outcome

Strategy evaluation moved from a quarterly process to a daily one. Investment managers test ideas the week they have them, and the strategies that survive arrive at the committee already backtested.

Read more case files
What we do

Four practices,
one engagement model.

We work with leaders who’ve seen the cost of pilots that never shipped. Each practice can stand alone or compose; most engagements span at least two.

  1. AI Strategy

    Decide where AI belongs in your business — and where it doesn't.

    Executive-aligned roadmaps that distinguish AI projects worth funding from the ones that will quietly stall in production.

    • Opportunity assessment by line of business
    • Build-vs-buy framework, with vendor diligence
    • Risk, compliance, and data-readiness review
    • Three-horizon investment plan with ROI bands
    Read more on AI Strategy

  2. Implementation

    Move from pilot to production — without the 18-month plateau.

    We pair our engineers with your team to ship AI systems that integrate with real workflows, real data, and real users.

    • Reference architecture and stack selection
    • Custom agent and pipeline development
    • Evaluation harnesses, observability, guardrails
    • Handover with runbooks and on-call coverage
    Read more on Implementation

  3. Adoption

    Make sure the people who'll use it, will actually use it.

    The hardest part of an AI rollout isn't the model — it's the team change. We bring playbooks honed across enterprise deployments.

    • Stakeholder alignment workshops
    • Communication and rollout plans
    • Training tailored by role and skill level
    • KPI dashboards tied to business outcomes
    Read more on Adoption

  4. Value Realization

    Tie AI spend to business outcomes you can defend to the board.

    Most AI investments lack a credible measurement framework. We build one — with the same rigor you'd apply to any capital decision.

    • Baseline measurement and uplift modeling
    • Cost-to-serve and unit-economics tracking
    • Quarterly value-realization reviews
    • Honest assessments — including when to stop
    Read more on Value Realization
How we work

A five-stage method,
calibrated to your readiness.

We’ve refined this sequence across client engagements. Stages overlap. Calendars shift. The discipline is in moving in order: discovery before plan, plan before commitment, commitment before scale.

  1. Stage I

    Weeks 1–3

    Executive Discovery

    We sit with your leadership and operators to map current state, surface real constraints, and identify where AI can move the needle — not where it can be retrofitted into a slide deck.

    • Current-state assessment
    • Executive alignment report
    • Strategic opportunity map

  2. Stage II

    Weeks 3–6

    Strategic Roadmapping

    A sequenced plan that ties each initiative to a business outcome, with risk and cost ranges you can defend. Compliance, data readiness, and build-vs-buy decided before commitments are made.

    • Strategic roadmap
    • Resource and cost requirements
    • Risk mitigation plan

  3. Stage III

    Weeks 5–8

    Stakeholder Alignment

    The work that determines whether AI projects survive their first quarter inside a real organization. Stakeholder maps, communication plans, the people who'll quietly block the rollout if no one engaged them.

    • Stakeholder matrix
    • Communication strategy
    • Change-management plan

  4. Stage IV

    3–6 months

    Phased Implementation

    We deploy in waves — highest-confidence, highest-impact first. Each wave is instrumented so the business can see the value compounding before the next commitment.

    • Implementation schedule
    • Success metrics framework
    • Progress reporting cadence

  5. Stage V

    Ongoing

    Value Realization

    After hand-off, we stay to validate the numbers the business uses to defend the investment. Real measurement, and explicit decisions about what to scale and what to retire.

    • ROI analysis dashboard
    • Performance optimization plan
    • Value realization review
Leadership

The people you’ll actually work with.

Every engagement is led by one of the two of us, supported by the network when specialist expertise is required.

Christian Adib

Christian Adib

Founder & Managing Partner

01

Areas of focus

  • Enterprise AI strategy for complex organizations
  • Executive advisory on AI implementation and value realization
  • Track record of delivering significant ROI through strategic AI initiatives
  • Speaker at global technology conferences and executive forums

Christian Adib began his career in management consulting at Booz Allen Hamilton and the Boston Consulting Group, where, as a senior data scientist, he led forward-deployed teams — embedding engineers alongside client operators rather than handing over slideware. He then joined a hedge fund, where, as part of a small team managing a $2B portfolio, he drove quantitative research and built products from scratch.

An engineer by training, Christian went on to complete MIT's Leaders for Global Operations program, earning an MBA alongside a master's in engineering. He founded Fulkerson Advisors to close the gap he kept seeing between AI's promise and what survives contact with production — building the firm around one conviction: that the people who scope an initiative should be the ones who ship it.

He works most closely with executives dragging AI from pilot to production, where his blend of consulting rigor, financial discipline, and engineering depth turns boardroom ambition into systems that actually run.

Cynthia Hajal

Cynthia Hajal

Chief Operating Officer

02

Areas of focus

  • Translating complex AI initiatives into scalable operational frameworks
  • Bridging technical capabilities with business objectives
  • Optimizing organizational processes for AI integration
  • Leading technical implementations from build to steady state

As Chief Operating Officer, Cynthia Hajal turns Fulkerson's technical work into something organizations can actually run. She pairs a systems engineer's instinct for how complex things fit together with the operational discipline to make them dependable — the difference between an AI capability that demos well and one that holds up as a process the business leans on every day.

Her strength is the translation layer: taking ambitious, technically intricate initiatives and reducing them to scalable operating frameworks, clear ownership, and the instrumentation that lets a client watch value compound. She leads the work of bridging what the technology can do with what the business actually needs — keeping engagements aligned, measurable, and moving.

Expert network

Specialists on demand,
not on payroll.

Twenty-plus subject-matter experts we’ve collaborated with on production engagements. We deploy them when an engagement needs depth beyond what the core team carries.

AI / ML Scientists

PhD-level researchers and practitioners specializing in machine learning, deep learning, and neural networks. Engaged on novel research-grade implementation problems.

Data Architects

Enterprise data architecture experts with experience designing and implementing large-scale AI systems and the infrastructure they sit on.

Security Specialists

Information security experts focused on AI system security, data privacy, and compliance frameworks — including SOC 2, HIPAA, and FedRAMP environments.

Domain Experts

Industry veterans bringing deep expertise in financial services, healthcare, retail, and technology sectors. Engaged for sector-specific judgment calls and regulatory awareness.

Integration Engineers

Technical specialists in enterprise system integration, cloud infrastructure, and distributed systems. The people who make AI deployments survive contact with your existing stack.

Implementation Leaders

Project leaders who have taken enterprise AI programs from kickoff to steady state. Engaged when an initiative needs senior on-site discipline.

Case studies

Selected engagements.

  1. Legal Services

    Legal Interview & Document Automation

    The agent now runs initial client interviews and produces pre-litigation drafts that clear the firm's document-quality and compliance review.

  2. Technology · Data Management

    SQL Chatbot for Enhanced Data Interactions

    Non-technical staff across departments now pull their own answers from the database, without SQL and without a ticket queue.

  3. Retail

    Store Labor Planning & Optimization

    The retailer now plans workforce allocation from the model's staffing recommendations.

  4. Education Technology

    AI-Powered Content Generation for EdTech

    The pipeline now drafts the bulk of new material.

  5. Enterprise Software

    GenAI Strategy for Enterprise Software

    The strategy produced a funded shortlist tied to business objectives — and an explicit list of initiatives not to pursue.

  6. Data Integration Services

    Unstructured Data Mapping Pipeline

    Mapping that required manual intervention now runs through the pipeline.

  7. Healthcare · Medical Devices

    LLM Agent for Medical Device Support

    The agent handles routine inquiries and follow-up calls.

  8. Executive Leadership Development

    AI-Powered Executive Coaching Platform

    His methodology now reaches executives he would never have had calendar time for, in interactive sessions that follow his approach rather than a generic chatbot script.

  9. Conglomerate · Retail

    Demand Forecasting Center of Excellence

    The center of excellence runs on trained local analysts.

Discuss a similar engagement
The research desk

Ask the assistant
trained on our work.

A research aide grounded in our case files and published essays. Useful for orienting before a first call, and a working preview of how we deploy these systems inside client engagements.

Try asking

Sample exchange

From the case files

What was the hardest part of shipping the legal interview agent?

Two things, in order. First, latency budget — attorneys won't tolerate more than ~1.5s between turns, so we had to constrain context, cache aggressively, and parallelize retrieval. Second, evaluation: legal interviews don't have a 'correct' transcript to compare against, so we built a rubric-based judge that scored coverage of required topics, gentleness of redirection, and document-prep readiness. The model work was unremarkable; the evaluation harness was the real product.

An illustrative exchange, grounded in the legal-automation case file.

Notes from the field

Letters from clients and partners.

Christian created an AI-powered MTL WHAT-WHO-WHY Sandbox that far exceeded my expectations in record time. He quickly intuited my needs, iterated efficiently, and delivered a scalable, user-friendly online tool in just a few days. Everyone I've shown his work to has been blown away and requested ongoing access. His ability to transform ideas into impactful, practical solutions, coupled with exceptional follow-through and responsiveness, is unmatched in my 40 years of experience working with tech experts.

Peter DiGiammarinoFounder & Managing Partner, IntelliVen
“Working with Christian was a game-changer for our business. His deep understanding of AI technologies and their practical application helped us focus our initiatives and uncover new opportunities. The insights and strategies he provided were not only innovative but also tailored specifically to our needs.”
John WassCEO, Profit Isle
“Christian's technical expertise initially drew us in, but his strategic mindset, flexibility, humility, and commitment have made him irreplaceable. He combines rare technical mastery with genuine partnership — making him the kind of partner you wish you could hire full-time. His willingness to listen first and execute second means he truly understands my vision and the mission of the company.”
MarjorieCEO & Founder, ALEE
“I am a software architect who has been developing solutions for clients worldwide for over 40 years. I am particularly impressed with his ability to articulate solutions by quickly building demonstrable, working prototypes, and the way he works with and guides a team to iterate toward production quality code. The results we produce together continue to impress clients — I would give Christian my strongest recommendation.”
GHTechnology Partner & Software Architect
“We engaged Fulkerson Advisors to automate a proprietary model for tracking changes across S&P U.S. indexes. The team exceeded our expectations at every stage — from understanding our requirements to conducting statistical analysis, as well as designing and executing the solution. The project was delivered on time with exceptional quality and flawless functionality.”
SGChief Investment Officer, $55Bn US Pension Fund
“Christian is a go-to expert and a one-stop-shop for all things Generative AI considered. The AI domain changes wildly fast and Christian was able to distill our requirements to a remarkable set of discrete deliverables that demonstrated durable value capture.”
SPProduct Management Executive
Insights

Notes from our practice.

  1. ·Strategy

    Beyond the pilot: why most enterprise AI dies in the 18-month gap

    The gap between a working demo and a production system that survives contact with users, data drift, and budget review is where most AI initiatives quietly fail.

    Christian AdibMarch 20268 min

  2. ·Implementation

    What we learned shipping LLM agents into a Top-5 US law firm

    Notes from automating pre-litigation interviews at scale: latency budgets, evaluation harnesses, and the unglamorous instrumentation that decides whether attorneys actually use the system.

    Cynthia HajalFebruary 202611 min

  3. ·Advisory

    Twelve questions to ask before approving an AI roadmap

    A working diligence checklist drawn from the engagements where AI initiatives stalled — and the ones where they didn't.

    Christian AdibJanuary 20266 min

All essays
Next step

Schedule a call.

Forty-five minutes with Christian, our founder. If we’re not the right firm for the problem, he’ll say so and point you somewhere better.

Format
45-minute video call
With
Christian Adib, Founder & Managing Partner
Prep
None — bring the problem, not a deck
Reserve a time

Or write to us directly at info@fulkersonadvisors.com.