An AI engineer, embedded in your business.
The AI industry calls this the forward-deployed model: instead of handing you a tool and a login, a senior engineer works inside your business — against your real data, in your real systems, with your real team — until AI is something your operation runs on, not something it's still "trying out."
Why tools alone haven't moved the needle
Demos close deals; deployments are where AI succeeds or dies. Off-the-shelf AI stalls in real businesses because real data is messy, workflows are undocumented, and nobody owns accuracy after launch. The forward-deployed answer is a person, not a product: an engineer accountable for the AI working in your environment — and for your team being able to run it without them.
What your embedded engineer does
Deploys against your real data
AI workflows built and tuned on your actual records, documents, and edge cases — not demo data. Where the data isn't ready, they fix the data layer first.
Integrates with your systems
Wired into the CRM, email, documents, and internal tools your team already uses. Human in the loop on every decision an AI touches.
Measures accuracy, continuously
Evals, guardrails, and regression checks — so you know how often the AI is right, and changes never silently degrade it.
Trains your team to own it
SOPs, playbooks, and hands-on training so the systems get used — and keep working — as your operation changes.
How the engagement works
Build first, then embed. The project creates the system; the retainer makes it compound.
1 · The build AI Operations project
A scoped AI Operations build: readiness, implementation, and enablement — agents, copilots, and workflows wired into your operation, delivered in milestones.
2 · The embed Embedded AI Engineer retainer
Your engineer stays embedded: monitoring accuracy, extending workflows to new use cases, integrating new tools, and training your team as the operation grows. Part of the retainer ladder — capacity-capped, not unlimited.
Already have AI systems someone else built? The retainer can start with a stabilization audit instead of a new build — scoped on the call.
Proof: BH Capital Funding
AI underwriting engine — BH Capital Funding
Underwriting review time went from 30 minutes to approximately 5 minutes per file. Approvals moved from days to 2–3 hours. Intake, parse, score, route, package, submit, and track — wired end-to-end, then run long-term.
Read case →Daniel Speiss co-founded BH Capital Funding. This case reflects work completed during that engagement. Metrics are operator-reported.
When you need this
- An AI build shipped, and now nobody owns keeping it accurate and expanding it.
- You want AI in the operation but don't want to hire a full-time AI engineer yet.
- Every AI initiative dies between the demo and the deployment.
- You need someone who can sit with your team, understand the workflow, and ship — not a vendor on the other side of a ticket queue.
Put an AI engineer inside your operation.
Book a scoping call and we'll map where AI pays back in your workflows, what to build first, and what staying embedded looks like.