Embedded engineering inside your tools and codebase.
Senior engineers working inside your repositories, your access controls, and your existing developer tooling. The system is built where it will run, not in a vendor sandbox.
Embedded AI Engineering.
Our engineers embed directly with your team to identify where AI moves your operation forward, then build and deploy the working system. You own the IP and the code at the end of the engagement.
Engineers Who Embed.
The fastest way to deploy production-grade AI is to put engineers inside your operation. We embed with your team, work inside your tools and your codebase, and ship the system your business actually needs. Not a slide deck. Working software.
Most AI consulting is strategy followed by referral. Strategy decks are an output, not a deliverable. We deliver running systems: retrieval-augmented generation pipelines tuned to your knowledge base, custom agent frameworks built around your workflows, internal copilots that your staff actually use, evaluation harnesses that prove the model is doing what it should. When the project calls for buyer-facing agents instead of internal tools, the build moves over to our AI automation practice.
Engagements run weeks, not months. The team is small and senior. The first week is spent inside your operation, the second week is spent on a working prototype, and the next two to six weeks ship the production system. Production systems have shipped for case-management automation inside law firms and PMS-integrated workflows for medical and dental practices. The handoff includes the code, the model artifacts, the deployment infrastructure, and a runbook your team can maintain.
You own the IP at the end. The work product is yours: every line of code, every prompt, every evaluation dataset, every deployment script. Nothing is held hostage to a vendor renewal.
Inside The Build.
RAG pipelines, agent frameworks, and evaluation harnesses.
Retrieval-augmented generation tuned to your knowledge base, custom agent frameworks built around your workflows, and evaluation harnesses that prove the model behaves correctly under production conditions. The internal-copilot pattern is documented in our field notes on the copilot every six-person team should build.
Production-grade software delivered in weeks.
Engagements scoped in weeks, not quarters. Working software at week two, production deployment by week eight at the latest. Where the product is a public-facing surface, the build pairs with an AI-optimized site rebuild.
You own the IP and the code at engagement end.
Full transfer at handoff: source code, prompts, evaluation datasets, model artifacts, deployment scripts, runbooks. No vendor lock-in, no renewal-or-die clauses.
How An Engagement Runs.
01
Discovery.
A week inside your operation. Stakeholder interviews, workflow mapping, data audit, technical-debt assessment. The output is a scoped statement of work for the engagement.
02
Prototype.
Week two ships a working prototype. Not a slide deck. Real code, real data, real outputs. The prototype proves the approach and surfaces the edge cases that drive the production build.
03
Build.
Weeks three through eight ship the production system. Code reviewed, evaluations passing, deployment infrastructure in place. Your team is brought along through the build, not after it.
04
Handoff.
Full transfer of code, prompts, datasets, model artifacts, and runbooks. Your team operates the system after handoff. Many engagements transition into a monthly retainer, sometimes alongside the agency white-label engineering tier for firms that resell, or feed into a content and SEO program when the output is buyer-facing.
Common Questions.
What team size do you embed?
Typically one to three engineers per engagement, plus a technical lead. Small and senior. No junior pyramid, no offshore handoffs.
What technical stacks do you work in?
Whatever your stack already runs. Python, TypeScript, Go, the major frontier model APIs, common vector stores, and the major cloud platforms. We meet the codebase where it is.
Who owns the IP at engagement end?
You do. Source code, prompts, evaluation datasets, model artifacts, and deployment scripts all transfer at handoff. Standard contract language is on the work order.
Do you offer ongoing support after handoff?
Optional retainer for follow-on evolution, tuning, and incident response. Many engagements transition to a small monthly retainer once the production system is live. The retainer model is consistent with the way we structure work across every industry we serve.
What kinds of projects fit AI Consultancy?
Internal copilots, RAG over a private knowledge base, custom agent workflows, evaluation systems, operational dashboards backed by AI. Anything where off-the-shelf AI tooling cannot solve the specific problem. Buyer-facing automation that compounds revenue, like inbound voicemail leakage on the plumbing dispatch side, runs through a different engagement model. Broader system audits use the Lighthouse Local platform.
Let's talk.
Tell us where your business is stuck and we'll walk through how Rhetor would run it. The marketing that gets you found across Google and every AI platform, the agents that handle the inbound and follow-up, and the engineering when the work calls for custom AI built around how your business actually runs.