Engineering delivery consulting

Your engineers can write code faster than your organization can ship it.

Coding stopped being the bottleneck a while ago, and AI has widened the gap. What slows delivery now is the system around the engineers: handoffs, approvals, unclear ownership, and communication that routes through too few people. I find that friction and take it out.

Bart Elison Former CTO, VP of Engineering, founding engineer Two acquisitions, teams from 5 to 100

The problem

Where the time goes

Between an idea and working software in production, only a small fraction of the elapsed time is spent writing code. Most of it is spent waiting: for a decision, for a review, for an environment, for a meeting, for someone to explain what was meant.

AI assistants have made the coding portion faster still. That does not make delivery faster. It moves the constraint onto the parts of the system that were already slow, and those parts are made of people, habits and process.

These are the symptoms I see most often. If more than two of them sound familiar, the problem is structural, and adding engineers will not fix it.

Work waits more than it moves

Pull requests, QA and approvals sit for days while everyone stays busy. Lead time is measured in weeks, effort in hours.

Decisions get made twice

Requirements are argued again in every meeting because nobody owns the call, so the same question comes back next week.

Releases are events

Deploys need a window, a checklist and a room full of people. So they happen rarely, and each one carries more risk than the last.

Priorities change weekly

The team starts plenty and finishes little. Half-done work piles up and nobody can say what is shipping this month.

Everything routes through one person

A single lead, architect or channel sits in the path of every decision, and that person is always the wait.

Busy team, flat output

Utilization is high and throughput is not. Engineers feel stretched, leadership feels underserved, and both are right.

Services

How I can help

Engagements are scoped to the problem in front of you, not to a package. These are the shapes they usually take.

01

Delivery diagnostic

Two to three weeks

I trace how work actually moves from idea to production. That means conversations with engineers, product and leadership, plus the data already sitting in your ticketing, code review and deployment tools.

You get a clear picture of where the time is lost, ranked by impact, and a set of changes sized to what your team can absorb without stopping delivery.

02

Process and team design

Four to twelve weeks

Ownership, decision rights, planning cadence, review and release flow, and the communication norms that hold it all together.

Designed and put in place with your team, measured against lead time and throughput, and adjusted as the numbers come in. Not a deck handed over at the end.

03

Fractional engineering leadership

Ongoing, part time

Interim CTO or VP of Engineering for companies between leaders, preparing for a raise, or working through an acquisition.

I have built teams from five to one hundred, led two through exits, and know what a buyer or investor will ask about your engineering organization before they ask it.

04

Engineering with AI in the loop

Diagnostic plus redesign

AI tooling changes where the constraint sits. Teams that adopt it without changing review, testing, planning and staffing end up with faster coding and the same delivery dates.

I help restructure the surrounding process so the speed shows up for customers instead of piling up in queues.

Approach

How I work

No methodology transplant. The goal is a team that keeps improving after I leave.

  1. Look before prescribing

    The first weeks are observation: queues, pull requests, tickets, meetings, and conversations with the people doing the work. Most organizations already know what is wrong. They have not yet been asked in the right way.

  2. Measure lead time, not activity

    The number that matters is how long an idea takes to reach a customer. Story points, velocity and utilization are proxies at best and distractions at worst.

  3. Change a few things and check

    Pick the two or three interventions with the best odds, make them, watch the numbers, then repeat. Small changes that stick beat large ones that get reverted.

  4. Leave the team able to run it

    Every change comes with the reasoning behind it and a way to tell if it is still working. The team should be able to keep adjusting without me on retainer.

Track record

Twenty years of building and fixing engineering organizations

5 to 100
Engineering team built from the founding group at MX
30M+
Sessions per month, with 2M daily peaks, on the platform scaled at QZZR
2
Companies led through acquisition: QZZR to Riddle and Chargeback to Sift
50%
Reduction in manual work from ML-driven automation at Chargeback
  • 2025 to present
    Founder and engineering lead at Applica

    Built and shipped an AI-driven job search platform from nothing to production, bootstrapped it to profitability, and set up the monitoring and security validation that lets it deploy on demand.

  • 2023 to present
    Software product consultant across fintech, blockchain and AI

    Helped an AI real estate startup secure $2M in seed funding. Worked with the Bahamian government on the protocol for the first central bank digital currency. Coordinated a hybrid offshore team building a blockchain-based ecological resource management system.

  • 2013 to 2016, 2021 to 2023
    Chief Technology Officer at QZZR

    Directed an ML-powered lead generation platform, scaled it to 30M+ sessions a month, and led a 20-person team through hypergrowth and acquisition by Riddle.

  • 2017 to 2019
    Director of Business Intelligence at Chargeback

    Introduced ML-driven chargeback automation that cut manual labor in half while pushing client win rates above 80%. Built the data pipeline and dashboards used across three departments, through acquisition by Sift.

  • 2008 to 2012
    Founding engineering team at MX, then MoneyDesktop

    Grew the team from the first hires to 100 people and architected the service-oriented platform that processed millions of transactions for banks and credit unions. Finovate Best in Show runner-up, 2012.

  • Earlier
    VP of Engineering at NAV and Development Manager at Kuali

About

Bart Elison

I have spent twenty years inside engineering organizations in fintech, martech, edtech and AI. I have been the first engineer, the CTO, the director brought in to rescue a stalled program, and the founder shipping alone.

The pattern I keep running into is the same one: capable people held back by how work moves around them. Fixing that is more satisfying to me than writing another line of code, and it moves the needle further. rvn.consulting is where I do that work full time.

I work with teams anywhere. Most engagements are remote, with time on site when it helps.

Background

  • CTO, QZZR (acquired by Riddle)
  • Director of Business Intelligence, Chargeback (acquired by Sift)
  • Founding engineering team, MX
  • VP of Engineering, NAV
  • Development Manager, Kuali
  • Founder, Applica
  • Certified Scrum Product Owner, Scrum Alliance
  • Hands-on in Elixir, Ruby, AWS and GCP

Contact

If your team works hard and ships slowly, let's talk.

A first conversation costs nothing and is usually enough to tell whether a diagnostic would pay for itself. Bring the symptoms; I will bring the questions.