Custom Build · AI software engineering
Ship production AI — engineered end to end, owned by you.
From the model layer to the interface, senior engineers design, build and ship the system your idea actually needs. Production-grade software you own outright — not a prototype, not a template, not a rented seat on somebody else's platform.
Four to twelve weeks from kickoff to production. Scope and price fixed before a line of production code is written.
Why an engineering practice sits inside a revenue operations firm
Because every Revenue Office engagement ends up needing real software. The middleware that catches a webhook when a retainer is signed and reports that conversion back to the ad platforms is software. The integration that reads treatment-plan events out of a practice management system and writes an appointment back is software. The dashboard that traces a dollar of ad spend to a collected dollar of revenue is software. None of that comes out of a box, and none of it gets built well by an agency that subcontracts engineering.
So we built the team. And once the team existed, it turned out a lot of the businesses we talk to have a second problem that has nothing to do with marketing: they have an idea for a product, or an internal process that should be software, and no engineering organization to build it.
This page is that team, available on its own.
The honest version
Most AI work sold today is a demo with a deadline. It looks extraordinary in a controlled walkthrough and falls over the first time real data and real users hit it. We build the opposite — production systems trusted by real users, owned by real teams, run for real money. That is a slower sale and a better outcome, and we would rather have the second one.
What we build
Six capabilities. Most engagements use two or three of them together, because a production system is rarely just one.
01 / Applications
Custom AI products
AI-native applications and SaaS products built around your use case rather than bent around a template. Full stack, from data model to interface.
02 / Agents
LLM apps and agents
Retrieval-augmented generation, copilots and multi-agent systems that reason over your data, retrieve the right context, and take real actions in real systems.
03 / Models
Machine learning and custom models
Training, fine-tuning and evaluation — benchmarked rigorously against a held-out set, not guessed at and declared finished.
04 / Vision & Language
Computer vision and NLP
Optical character recognition, object detection, semantic search, structured extraction and summarization — as production pipelines with error handling, not notebooks.
05 / Infrastructure
AI infrastructure and MLOps
Vector databases, inference serving, evaluation harnesses and observability, built to hold up under real concurrency and real cost constraints.
06 / Integration
AI inside the stack you already run
Clean APIs and secure pipelines that drop intelligence into the systems your business already depends on, without a migration and without a rebuild.
What comes out the other end
| Products | Internal systems | Data and intelligence |
|---|---|---|
| AI SaaS products Customer-facing copilots Conversational agents Generative applications Mobile AI applications |
Internal AI tools Workflow automation Knowledge systems Document intelligence AI APIs for your own teams |
Recommendation engines Predictive analytics Semantic search and retrieval Extraction and classification pipelines Evaluation and observability layers |
Industries we have built in
Healthcare · Finance · Commerce · Industrial · Legal · Education · Enterprise · Public sector
Regulated industries are the norm rather than the exception here, which is why the security and compliance section below is where it is and not buried in a footer.
The stack
We are not loyal to any vendor in this list. These are the tools that currently hold up in production, and the list changes when the evidence changes.
| Layer | What we build on |
|---|---|
| Models | GPT-4o and 4.1 · Claude Sonnet · Llama 3 · Mistral · Gemini · custom fine-tunes |
| Frameworks | LangGraph · LlamaIndex · DSPy · PyTorch · Transformers · vLLM |
| Data & vector | Postgres and pgvector · Pinecone · Weaviate · Qdrant · Redis · DuckDB |
| Infrastructure | AWS · GCP · Cloudflare · Modal · Kubernetes · Docker |
| Application layer | TypeScript · Next.js · React · FastAPI · tRPC · Tailwind |
| Operations & evaluation | LangSmith · Braintrust · OpenTelemetry · Sentry · GitHub Actions · Terraform |
Four things that make this different
Production, not prototypes
Systems built to run reliably at scale, not demos that collapse on real data. Evaluation harnesses, observability, cost controls and failure handling are part of the build, not a phase two that never gets funded.
Full-stack AI
One team owns the model, the data, the infrastructure and the interface. No handoffs between a data science vendor, an app shop and a DevOps contractor — which is where most AI projects actually die.
You own everything
Code, models, training data, infrastructure and intellectual property. Yours from day one, in your repositories and your cloud accounts. No lock-in, no rented access, no platform fee that appears in year two.
Senior, founder-led
Built by senior engineers and led personally by the principal. Never offshored to juniors on your budget, and never staffed by whoever happened to be on the bench that month.
Security, compliance and ownership
Most of what we build touches regulated data. The posture below is designed in from the architecture phase rather than retrofitted before a customer audit.
How we build
- Systems architected to the control expectations of SOC 2 Type II, ISO 27001, GDPR and HIPAA — least privilege, encryption in transit and at rest, audit logging, data residency and retention policy, documented access control.
- Business Associate Agreements executed where protected health information is in scope, across the full delivery chain.
- Secrets management, dependency scanning and infrastructure as code from the first commit.
- Model and vendor selection reviewed against your data-handling obligations before anything is wired in — including whether a provider trains on your inputs.
What you own
- Every line of source code, in your repository, from the first commit.
- Model weights, fine-tunes, prompts, evaluation sets and training data.
- Infrastructure running in your cloud accounts under your billing, not ours.
- Architecture documentation, runbooks and a recorded handover session for your team.
- No exclusivity clause, no non-compete on your own product, and no fee to take it elsewhere.
Compliance architecture is not the same as certification. Where your customers require an attested SOC 2 Type II report or ISO 27001 certificate from you, we build to satisfy the controls and work alongside your auditor — the attestation itself is issued by that auditor, not by us.
What it costs
Same discipline as every other engagement here: a paid architecture phase that produces a fixed scope and a fixed price, so nobody is quoting a build they have not designed.
Technical Discovery
The architecture phase
$7,5002 weeks · credited against the build- Problem definition and success criteria, written down and agreed
- System and data architecture
- Model and vendor selection, with the evaluation approach defined up front
- Security, compliance and data-handling design
- Build plan, fixed scope and fixed price
- An honest recommendation — including "do not build this," which we have given before
Production Build
Kickoff to live system
$45,000–$180,0004–12 weeks, fixed after discovery- Full implementation across model, data, infrastructure and interface
- Evaluation harnesses wired in from week one
- Deployment, monitoring, observability and cost controls
- Security hardening and compliance controls
- Documentation, runbooks and team handover
- Weekly working software, not weekly status decks
- Everything delivered into your repositories and your cloud accounts
Operate & Extend
Optional, never assumed
$6,500–$20,000per month- Monitoring, incident response and uptime ownership
- Model and prompt evaluation on a schedule, as providers change underneath you
- Cost optimization as usage scales
- Feature development on an agreed roadmap
- Security patching and dependency management
- Month to month. You can take the system in-house at any point and we will train your team into it.
Cloud, model API and third-party platform costs are billed directly to your accounts by those vendors, at their price. We do not resell infrastructure, mark up token spend, or take a commission from any provider in the stack.
Questions we get
Can you just build a quick proof of concept?
We can, and we will tell you honestly whether it is worth the money. A proof of concept is useful when the technical risk is genuinely unknown — whether the model can do the task at acceptable accuracy on your real data. It is a waste when the answer is already known and the real risk is engineering, adoption or economics. Discovery is designed to tell you which situation you are in.
We have an internal team. Can you work alongside them?
Yes, and it is often the better structure. We architect and build the hard parts, your team owns the domain logic and the long-term roadmap, and the handover is designed from day one rather than negotiated at the end. Say so during discovery and we will shape the engagement around it.
What happens when the model providers change everything again?
They will, roughly every quarter. This is exactly why evaluation harnesses are built in from week one — when a provider ships a new model or silently changes behavior, you run the evals and get an answer instead of a hunch. Systems built without evals cannot be safely upgraded, which is why so many of them are frozen on a model from two years ago.
Do you sign a non-disclosure agreement?
Yes, before discovery, as a matter of course. We also do not use client work as a case study without written permission, and we do not work with two directly competing products at the same time.
How is this different from hiring an agency?
Most agencies subcontract the hard layers. The model work goes to one vendor, the application to another, the infrastructure to a third, and the client becomes the integrator by accident. One team owns all four layers here, and the person who designed the architecture is in the build. That is also why we take fewer projects than an agency would.
What if we also need the revenue side?
Then say so early. A number of clients start here with a product build and end up running a Revenue Office engagement alongside it, because shipping the software turns out to be the easier half of the problem. The Revenue Office is here.
Start with a technical conversation.
Thirty minutes with the person who would architect the system. Bring the problem, not a specification — we will ask what it has to do, what data exists, what it connects to, and what breaks if it goes down. At the end you will get a straight answer about whether this is a four-week build, a twelve-week build, or something you should not build at all.
DigitalOS, LLC · 9436 W. Lake Mead Blvd., Ste 5 #1159, Las Vegas, NV 89134 · Response inside one business day.