Forward deployed engineers, embedded in your stack. Start with a paid diagnostic. One workflow shipped. The retainer only earns its place after the work survives.
FIG. 00
Engineer on workflow
We use them. We help your team use them too.
Pick the one that fits where your team is today.
A forward-deployed engineer (FDE) is part business consultant, part software engineer. We embed two with your team for six weeks — they sit with your operators, scope what's worth building, ship the software or AI agents that move the business, and hand off the code, the evals, and the runbook to your team.
Your team picked the tool — Codex, Claude Code, Cursor, or another AI assistant. We embed an enablement lead who runs 1-on-1 sessions and team trainings on the prompts, the habits, and the workflows that turn "we have it" into "we use it every day."
We sit with the people who run the work — front desks, dispatch rooms, job sites, branches — and ship AI into the workflow they already use.
Drag to explore
01 · Active
Guest comms, bookings, concierge ops, maintenance, reviews, revenue.
02 · Active
Leasing pipelines, tenant ops, maintenance routing, owner reporting.
03 · Active
Bid intake, RFI handling, job-site exceptions, change-order tracking.
04 · Active
Underwriting prep, KYC ops, document review, advisor knowledge.
05 · Active
Dispatch comms, exception handling, POD reconciliation, claims.
06 · Active
Intake triage, scoping, time recovery, deliverable drafting.
07 · Active
Patient intake, scheduling, intake routing, documentation.
Three phases, one workflow at a time. No rip-and-replace, no six-month roadmap, no surprise bill in month six.
FIG. 04
Diagnostic in progress
One workflow mapped. We tell you what shouldn't be automated.
Evals on your real data, against your real examples of "great." If we can't prove it, we don't ship it.
APIs over your existing stack. Smallest unit of autonomy first. The agent earns trust the same way a new hire does.
Companies pulling ahead don't have better models — they have AI inside the workflows their team already runs. That's the only edge left, and the reason most AI budgets get written off by 2027.
of enterprise apps will include task-specific AI agents by end of 2026
“The slowdown for enterprises is not model intelligence — it is how agents are built and run inside organizations.”
of organizations have scaled a single agentic AI system
“Product differentiation increasingly comes from implementation across customer contexts.”
of agentic AI projects will be canceled by 2027 — cost, weak value, poor controls
“AI leaders show double the revenue growth and 40% more cost savings than laggards.”
Forward deployed engineering is how the edge gets built.
Every recurring outcome already runs through inputs, decisions, tools, exceptions, and feedback. We call the loops that matter business engines.
Forms, calls, inboxes, CRM records, docs, tickets — the signals the work already runs on.
Rules, model calls, approvals, routing, thresholds, exceptions — the calls your team makes today.
Draft, update, reconcile, notify, assign, escalate — the next action that has to ship.
Evals, human review, cycle times, quality checks — the loop that keeps the engine honest.
Don't buy an AI retainer. Buy one deployed workflow. If it doesn't survive real work, stop.
One workflow mapped, systems and data inspected, a working prototype and eval harness built, ROI math run against your real numbers. A one-page go/no-go. Fixed fee, credited to the deployment.
One workflow shipped into production — integrations, evals, permissions, human review, team training, and measurement engineered into your stack. Enablement is included. The retainer only opens if the deployment survives the operators using it.
After the first deployment lands. Operate, monitor, and improve the workflow you shipped — then ship the next one. Each workflow rides on the data, evals, and integrations of the last, so the cost per outcome compounds down. Cancel anytime.
Enablement is included in deployment. Add it back as a department-wide program only when there's real ground to cover.
Optional. When a department needs more than the deployed workflow — manager rituals, prompt and eval libraries, AI governance, embedded office hours, rollout ownership. Bolt on to any active engagement.
We only sell deployment when the diagnostic shows the workflow can plausibly create or protect $15K–$30K USD per month in value. If the math doesn't clear, we say so.
EngineB runs out of Barbados and Montreal so we can sit with Caribbean and Canadian teams in their time zone, in their language, inside their workflows.
If we can't show the hours back, we didn't deploy it. Every workflow we ship comes with the measurement attached.
Cycle time, response time, hours-per-week back to the team — measured against the workflow we deployed, not generic AI claims.
Status across systems, exceptions surfaced, audit trails built in. Permissions and human review designed in, not bolted on after a demo.
Built with the people who run the work, not for them. Training and playbooks ship with the deployment, so the system gets used after launch.
Each workflow becomes a node — shared data, reusable evals, integrations the next workflow rides on. Cost per outcome drops every time we ship.
Every business I've operated had the same constraint: the work moved faster than the operating system around it. Data scattered across tools. Decisions stuck in someone's inbox. Quality riding on the one person who remembered the exception. AI didn't fix that — it exposed it.
I watched Claude Code and Codex live inside my engineers' terminals and reshape engineering in a year. The step-change was not model quality alone — it was that the agent moved inside the work. That's the shape of every workflow worth deploying. It doesn't come from a chat window or a SaaS subscription. It comes from someone sitting with the team that owns the work, and shipping the agent inside it.
I built EngineB to be that team for operators in Canada and the Caribbean. No strategy decks. No transformation roadmaps. We diagnose one workflow, deploy it, and only keep working with you if the work compounds.
Ten thousand dollars, fixed fee, credited to the deployment. You walk away with a working prototype, an eval harness, and a one-page recommendation. Keep us or don't.
