An AI orchestration engine, inside the software you run.
We install the intelligence layer and chat interface that interconnect your systems, then schedule, automate, and operate the work between them. Built on an orchestration foundation we design and maintain ourselves, wired into your stack rather than running beside it — governed, evaluated, and yours.
- 01An AI orchestration engine running inside your stack
- 02Evaluation, governance, and observability wired in
- 03Architecture, code, and working methods you own
First deliverable
An AI Implementation Blueprint — a working proof using synthetic data and mocked integrations, plus the architecture, evaluation plan, and delivery scope. Proprietary records are not required for the proof.
What “AI engineering” actually means here.
The disciplines between a model that works and a system your business can depend on. We design our own architectures and maintain the orchestration foundation these run on — the same one deployed in hospitals for doctors. Roughly 80% of the effort in getting AI to production is this work, not model development.
- 01
Orchestration architecture
The engine that interconnects your systems and coordinates work across them — scheduling, routing, and running multi-step agentic flows under rules you set.
- 02
Retrieval architecture
RAG designed around your data model — chunking, indexing, and retrieval that hold up on real corpora rather than a demo set.
- 03
Evaluation harnesses
Automated evals wired into CI, so prompt and model changes are measured before they ship instead of after users find the regression.
- 04
Agent and tool design
Scoped permissions, approval gates, and tool orchestration — agents that act inside boundaries your risk function can sign off on.
- 05
LLM infrastructure
Serving, routing, caching, and cost control across models and providers, sized to your traffic and your budget.
- 06
Observability and tracing
Request-level tracing, quality monitoring, and drift detection, so you know how the system behaves in production and why.
- 07
Model governance
Versioning, audit trails, and decision logging — the record internal QA and regulators ask for, produced as part of the build.
- 08
Data and integration engineering
The pipelines, contracts, and integration work that account for most of the effort and most of the failures.
Design. Prove. Build. Improve.
- 01
Design
Scope the challenge. Design the solution.
Map your data layer and quality gaps, then design the AI integration around your workflows and success criteria.
- 02
Prove
Test the design in simulation.
Our proprietary healthcare data generation suite creates documents, databases, and detailed cases for your workflow. We test end to end against agreed criteria, without patient data.
- 03
Build
Build and validate in your environment.
Connect your data and AI layers, optimize quality and latency, and validate the complete workflow with representative data and users before release.
- 04
Improve
Maintain reliability. Extend capability.
Monitor live performance, turn findings into regression tests, and evaluate each change before rollout. Your team owns what runs.
- 01
STEP 01
Technical discovery
We map your workflows and data layer: sources, structure, quality, access, and freshness. Together we define how AI will use that data, the build scope, and acceptance criteria.
- 02
STEP 02
The Implementation Blueprint
Architecture, integration design, evaluation strategy, governance plan, and a delivery sequence. A build-ready engineering document that could be executed without us, which is the point of paying for it separately.
- 03
STEP 03
Prove the design in simulation
Our proprietary healthcare data generation suite creates documents, populated databases, and detailed scenarios for your use case. We test the design end to end through mocked integrations, without patient data, and record results and coverage limits. This is part of the Blueprint; validation in your environment follows before release.
- 04
STEP 04
Build it in your stack
Implementation inside your repository, to your review standards — and alongside your engineers if you have them. Integrated with the systems people already depend on rather than running beside them.
- 05
STEP 05
Govern, measure, hand over
Observability, audit trails, and approval gates in place; the evaluation suite running in CI; documentation and a working handover. Your team carries it forward — or we keep operating it with you.
You stay the owner. We bring the depth.
We work inside your repository, your review process, and your release cadence. If you have engineers, we build alongside them and hand over; if you do not, we operate it with you — either way the system is yours.
Your side
- Owns the domain, the codebase, and the product decisions
- Reviews the work at whatever depth your team allows
- Holds the relationships with data owners and internal QA
- Chooses to operate the system or have us run it with you
DotCross
- The orchestration engine, plus architecture, retrieval, evaluation, agents, serving
- Implementation inside your repository, not a parallel one
- Governance, observability, and the validation evidence regulators expect
- Pairing and documentation so the knowledge stays on your side
Where our products run
Regulated. Production. Multi-tenant.
Production AI under a regulated bar
Our products — Markov and Kaplan — run in U.S. hospital systems and clinics, under the access control, governance, and audit requirements those environments impose. Engagements draw on those components rather than starting from zero. Kaplan, our patient CRM for clinics that run research, runs referral intake, scheduling and patient follow-up in 14 clinics today.
Source: DotCross product deployments and client engagements.
This fits when you already run the software, and the gap is the AI layer on top of it.
This is you
- You run your own software product or platform
- You have a platform or defined workflow to extend
- A specific workflow or decision would measurably benefit from AI
- The result has to be governed, evaluated, and auditable
This isn’t
- You are shopping for hourly staff augmentation
- You want a proof of concept with no intended path to production
- There is no plan to obtain the data or access needed for eventual production use
QUESTIONS FOR THIS ENGAGEMENT
You do. Architecture, code, evaluation suites, and documentation are assigned to you, and the handover is designed so your team can operate and extend the system without us. You can stop after any stage and keep what was produced.
Data stays in your environment. Access is scoped, encrypted, and audit-logged, with incident response agreed up front, and a BAA is available where PHI is in scope. This is the standard our healthcare deployments already run under.
Evaluation is engineering. We build an eval harness against your data and wire it into CI, so prompt and model changes are measured before they ship. In production it is paired with request-level tracing, quality monitoring, and human approval gates wherever an error would be expensive.
If you have engineers, they review the work as it lands, so the system is familiar before it is theirs, and the handover includes the eval suite, runbooks, and architecture documentation. If you do not, we keep operating it with you under Embedded AI Engineering. Either way you own the system; ongoing support is a choice.
If you can wait, hiring is the right long-term answer and we will say so. The practical problem is timing: senior AI roles take roughly 90 to 120 days to fill against a market running about ten senior openings per qualified engineer, and the AI work usually waits for the req to close. We are the option that starts now, on an orchestration foundation already running in production, and leaves you more capable.
You work directly with senior healthcare AI engineers who design and build the system. From $8,000, the Blueprint delivers working code tested with generated data and mocked integrations, plus the architecture and build scope. We stay accountable through implementation.
Yes. It changes how we document. Most validation tooling was written for deterministic software and fits stochastic models poorly, so we design the evidence — intended use, risk assessment, evaluation records, decision logging — around GAMP 5 expectations, with data handled to ALCOA+ standards.
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