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Fulkerson Advisors
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AI implementation consulting

From idea or pilot to daily use, on the calendar you committed to.

AI implementation consulting builds an AI system, or finishes one you started, and puts it into production: connecting it to the systems the business runs on, testing its answers, and leaving a named owner behind. At Fulkerson Advisors it is delivered inside the Build, in twelve weeks.

Production is where AI programs stall. We work with operators who have an executive sponsor and a date. Some bring a process that should be a system by then. Some bring a prototype that has been almost ready for months. Our job is to hit that date with a system that handles real users, real data, and a real audit.

How the Build works

How it works

What this looks like in practice

We staff the work the way we would staff our own product team. A founding partner leads it, and a small group of senior engineers works inside your team, with your data people and your security reviewers. We work in your code, on your tickets, in your environments, and there is no handoff to a bench in another time zone. Every recommendation ships as working software, a test of the system's answers, a written procedure, or a dashboard. None of it ships as a slide someone else has to turn into a build. By week six there is a named engineer on your side who can defend the system in a review without us in the room. Security review covers the AI suppliers and what their terms allow. In our study of nearly 3,700 software products, 87% of terms said nothing about whether customer data trains AI.

When to call

When you should talk to us

Two patterns bring people to this page. The first is work that runs on senior people's hours, a sponsor who has promised a system by a date, and a team that has not shipped AI before. The second is a pilot that demoed well in March, the board approved a rollout in May, and by September the system still lives on one engineer's laptop while procurement, security, and a reluctant platform team argue about ownership. Or a partner shipped a proof of concept, the CFO asked what it returned, and nobody measured anything. Or a regulated business unit blocked the rollout because the only test of the system's answers is a spreadsheet. The technology is usually fine, and the path to production is the bottleneck. If you are still deciding whether AI is the right move at all, start with the Diagnosis.

How we do it

How we approach implementation

We hold the order. The test of the system's answers comes before the code, the smallest version that does real work ships before the ambitious one, and measurement is built in before launch rather than bolted on after. The sequence stays the same when calendars shift and scope gets negotiated. We prove the failure modes first, ship the slice, measure the value, and hand over the keys.

  • Week 1: baseline measured, data access confirmed, the test the system's answers must pass written down
  • Week 3: the stop point. On a path to production, or we say so and refund the remainder
  • Week 6: the smallest version that does real work is live for a first group. Security review runs alongside
  • Week 12: rolled out, measured, handed to a named owner on your team
  • Every week: a demo to the sponsor. No steering committees

Proof

Case files

We cite work by industry rather than client name unless we have permission. The pattern across these engagements is consistent: a real production constraint (a $55B portfolio, attorney-client privilege, a regulated medical workflow), a calendar measured in weeks, and usually no working system on day one. In each case the deliverable was a live system with a named owner inside the company.

What we don't do

We don't write plans for clients who have not decided to build. That is the Diagnosis, and sometimes a different firm. We don't staff contractors into your ticket queue and disappear. We don't ship systems we can't measure, and we won't hand over to a team that has not been in the code with us. If the right answer is to stop the project, we say so at week three and refund the rest of the engagement.

A system in production needs an owner after the team leaves, and the Build names one on your side. When the portfolio grows past one system, the fractional Chief AI Officer engagement takes ownership of the whole.

Related case files.

  1. Asset Management

    Pension Fund Equity Index Backtesting

    A production backtesting environment for a $55B pension fund. Strategy evaluation moved from a quarterly cycle to a daily one.

  2. Top-5 US Law Firm

    Pre-Litigation Document & Interview Automation

    An LLM agent runs the first client interviews and drafts the pre-litigation documents. The drafts pass the firm's compliance review.

  3. Technology / Data Management

    AI Data Agent Over Redshift

    Natural-language access to a production warehouse, instrumented with evals and guardrails the data team owns on day 91.

See all case files

Questions we hear most.

Our pilot worked. Why is it stuck in IT?

A pilot proves a model can work in a sandbox. Production proves it can work against your identity provider, your data lineage, your change-management board, and a user who has Monday-morning quotas. We've watched promising pilots stall for nine months over a missing service account. Implementation is mostly integration, observability, and ownership, and the teams that plan for it from week one are the ones that ship on the date.

How do we build an evaluation harness that survives reality?

Start from the failure modes your operators fear rather than from a generic benchmark. We build evaluation sets out of real (anonymized) production traffic, score them on the dimensions your business cares about (accuracy, latency, cost, escalation rate, regulatory tolerance), and wire the harness into CI so a regression blocks a deploy. The harness needs updating monthly, because production traffic changes and a harness that stands still stops catching regressions.

Who owns the system on day 91?

Your team. We staff every engagement so a named owner on your side is in the codebase, the eval harness, and the on-call rotation by week six. We will not run a system in production that your engineers cannot debug at 2 a.m. without us. Handoff is a scheduled milestone.

How do we instrument so we can prove value?

Instrumentation is designed before the system ships. We tie every AI surface to two layers of telemetry: system metrics (latency, cost per call, failure rate) and business metrics (cycle time, conversion, hours reclaimed, error rate avoided). Both flow to a dashboard your finance team can audit.

How long does a typical implementation engagement run?

Twelve weeks, as the Build. The first week is the baseline, data access, and the test the system's answers will have to pass. Week three is the stop point. Week six is the smallest version that does real work, live for a first group. If a vendor is quoting you six weeks end-to-end on a system that touches your production data, ask them where the security review fits.

Will you work alongside our existing systems integrator or in-house engineering team?

Yes. Most of our work is joint. We're frequently brought in when an in-house team has the talent but needs senior pattern recognition on the AI-specific failure modes (eval design, retrieval architecture, agent control flow, cost containment). We embed, share the codebase, and leave it with the team.

Bring us a question and we’ll tell you whether it’s worth pursuing.

Schedule a callRead the case files