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Fulkerson Advisors
Practice · No. 02

Move from pilot to production, on the calendar you committed to

Pilots are easy; production is the part where most AI programs stall. We work with Fortune 500 operators who have a working prototype, an executive sponsor, and a finite window to prove the system can survive real users, real data, and real audit. Our job is to close that window cleanly.

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The engagement

What this looks like in practice

We staff implementation the way we'd staff our own product team. A senior partner leads the engagement; a small pod of forward-deployed engineers embeds with your operators, your data team, and your security reviewers. We work in your repo, on your tickets, against your environments. No offshore handoff, no junior team running a Gantt chart from a different time zone. Every recommendation ships as code, evaluation harness, runbook, or instrumented dashboard; never as a slideware deliverable that someone else has to translate into a build. By week six there is a named engineer on your side who can defend the system in a postmortem without us in the room.

Buying triggers

When you should talk to us

The pattern we see repeatedly: a pilot demoed well in March, the board approved scale-up in May, and by September the system is still living on one engineer's laptop while procurement, infosec, and a reluctant platform team debate ownership. Or: a partner shipped a proof-of-concept, the CFO asked what the ROI was, and no one instrumented anything to answer. Or: a regulated business unit blocked the rollout because the eval harness is a spreadsheet. These are the conversations where we add the most value; the technology is mostly fine, the production path is the bottleneck. If you're still deciding whether AI is the right move at all, talk to our Strategy practice first.

Method

How we approach implementation

We work in a sequence that's been refined across Fortune 500 engagements, and we hold the order. Architecture and evaluation harness come before code; the smallest defensible production slice ships before the ambitious one; instrumentation is wired in before launch, not bolted on after. Stages overlap, calendars shift, scope negotiates. The discipline is in moving in order: prove the failure modes, ship the slice, instrument the value, transfer the keys.

  • Week 1-2: architecture review, data-access reality check, eval harness v0 against real traffic
  • Week 3-6: smallest defensible production slice; security review runs in parallel, not after
  • Week 7-12: scale the surface, harden the runbooks, wire telemetry to a dashboard finance can audit
  • Week 13+: named owner on your side in the codebase and on-call; we step back on a scheduled milestone
  • Throughout: weekly demo to the executive sponsor; no monthly steering committees that hide bad news

Proof

Case files

We cite work by industry rather than client name unless we've been given permission. The pattern across these engagements is consistent: a working prototype, a real production constraint (a $55B portfolio, attorney-client privilege, a regulated medical workflow), and a calendar measured in weeks. In each case the deliverable was a live system with a named internal owner, not a report.

Anti-positioning

What we don't do

We don't write strategy decks for clients who haven't decided to build; that's a different practice and often a different firm. We don't run staff-augmentation contracts where a roster of contractors gets dropped into your Jira and disappears. We don't ship systems we can't instrument, and we won't run a production handoff to a team that hasn't been in the codebase with us. If the right answer is to kill the project, we'll tell you in week three and refund the rest of the engagement. The boutique move is knowing what to decline.

Who you’ll work with

Christian Adib

Christian Adib

Founder & Managing Partner

Christian leads this practice out of his BCG forward-deployed background and a hedge-fund quant tour running production systems against a $2B book; he has shipped AI into environments where downtime has a dollar figure attached.

Practice background

  • 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.

Case files

  1. Asset Management

    Pension Fund Equity Index Backtesting

    Production backtesting environment shipped against a $55B portfolio; quant team now iterates strategies in days, not quarters.

  2. Top-5 US Law Firm

    Pre-Litigation Document & Interview Automation

    LLM agent live in pre-litigation workflow; attorney review time on first-pass discovery reduced materially, with audit trails partners trust.

  3. Technology / Data Management

    SQL Chatbot 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
Frequently asked

Questions we hear most.

Our pilot worked. Why is it stuck in IT?
Because 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 the boring part: integration, observability, and ownership. Plan for it from week one or pay for it in quarter three.
How do we build an evaluation harness that survives reality?
Start from the failure modes your operators actually fear, not 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 is a living artifact; if it isn't updated monthly, it's already wrong.
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, not a hope.
How do we instrument so we can prove value?
Instrumentation has to be designed before the system ships, not bolted on after the CFO asks. 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. Value you cannot measure is value you cannot defend at budget review.
How long does a typical implementation engagement run?
Most run between twelve and twenty weeks from kickoff to production handoff, depending on integration surface and data readiness. The first four weeks are architecture, eval harness, and the smallest defensible production slice. If a vendor is quoting you six weeks end-to-end on a Fortune 500 system, 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 the team stronger than we found it.

Bring us a question, and we’ll bring you an honest read.

Schedule an implementation briefingRead the case files