We get stalled healthcare AI projects into production — without compromising the patient on the other end. Last engagement: three months from stuck to live, ~$2M/yr in projected savings. One founder — biomedical and computer engineering — embedded with your team at the last mile.
Teams fail at production AI when they optimize one pillar and forget the other five. Our engagements begin and end with this scoreboard — it sits on the wall of every sprint review.
Quality. Cost. Latency. Correctness. Adaptability. Testability. Every architectural trade-off is argued against these six — in writing, and in front of the people paying for it.
The output has to be good — measured against clinical ground truth, expert review and the outcomes your users actually care about. Not vibes, not leaderboards.
Every token, every GPU-hour, every vendor line item accounted for. We model unit economics per query at day one — not after the invoice lands.
P50, P95, P99 — on your hardware, your network, your worst day. Streaming, caching and model cascading are tools, not afterthoughts.
Does it do the right thing on inputs you have never seen? Formal constraints, grounded retrieval and verifier loops — engineered, not hoped for.
Models, regulations and your own taxonomy will change. Architecture is the craft of making that change cheap instead of catastrophic.
If you cannot measure it, you cannot ship it. Eval harnesses, regression suites and shadow-traffic replay are built in from sprint one — not bolted on.
Healthcare is our focus. Inside that focus we do the six things below. If your project is stuck, the odds are good it is stuck on one of them.
Reference architectures for retrieval, agents, evaluation and inference — tailored to your data, latency budget and compliance footprint. We write the spec your engineers can build from on Monday.
From data ingestion through fine-tuning to the last pixel of the UI. We embed with your team or ship turnkey — whichever lets you sleep at night.
Regression suites, offline evals, prompt versioning and live telemetry. The boring infrastructure that separates a toy from a product your customers trust.
Frontier, open-weight or private — chosen against cost, latency, governance and quality on your actual task. Fine-tuned, distilled and served where it belongs.
Policy scaffolding, red-teaming, PII handling, audit trails and sign-off workflows. Built for SOC 2, HIPAA and the regulator you'll meet next year.
For mid-market leadership teams without a Chief AI Officer. Quarterly roadmap, weekly standups, direct-line Slack. Real leverage at a fraction of the cost.
Every Zalattria engagement follows the same arc. It is what keeps weeks from becoming quarters and quarters from becoming sunk cost.
We audit the data, the stack and the politics. You receive a written brief: what is actually feasible, what is theater, and what nobody has told you.
A signed-off system design — components, contracts, evals, failure modes and three cost scenarios. The document your CTO will defend in the boardroom.
We build. In your repo, on your cloud, with your engineers in the room. Two-week sprints, demo-able every Friday, no vendor lock-in by design.
After launch we stay on as stewards — on-call, monitoring drift, reviewing PRs, shipping the second version. Month-to-month. Leave any time.
Two clients across two industries — TiberHealth in education technology and Giving Home Health Care in healthcare. Deep relationships beat wide rosters.
MDTutor was a medical-student AI tutor that had stalled in development. We joined as solutions architect, re-scoped the system around what would actually ship, rewrote the pieces blocking release, and got it into production for real medical-science students in three months.
MDTutor needed to ground answers in the exact textbooks students were being taught from — content locked behind ClinicalKey, LWW Health Library, and McGraw-Hill. The hard part was Silverchair's anti-scraping; we beat it. End-to-end we automated authentication, navigation, discovery, extraction, post-processing, chunking, and loading into Pinecone. Then we went multi-modal: diagrams and figures stored as MySQL MEDIUMBLOBs and mapped back to the text chunks that reference them, so the tutor can surface the right image mid-conversation.
We authored the instructional-design spine for a graduate certificate in AI-driven drug discovery — eight program learning outcomes spanning computational programming, large-scale biochemical data analysis, structure-based and generative drug design (VAEs and GANs), medicinal chemistry, protein structure prediction with ColabFold, an NVIDIA BioNeMo track, and the regulatory and IP landscape for AI-designed therapeutics. PLOs, course-level objectives, assessment activities, and a credit-hour model, delivered as a teachable, auditable curriculum.
For a home-health-care provider serving former nuclear-energy and Department of Energy workers, a HIPAA-compliant intelligent document processing pipeline on Azure — taking the forms, faxes and PDFs that pile up between patient and clinician and turning them into structured, auditable data. We build inside the customer's tenant, encrypt at rest and in transit, audit every touch of PHI, and return hours of administrative time back to the people who entered medicine to practice medicine.
A former weapons-complex worker, decades off the job, is owed care for an illness the government already conceded it caused. Between him and that care sits a fax machine.
His medical records and nursing assessments arrive the way they always have — as faxed PDFs, hundreds of pages, unstructured and unsearchable. Today a person reads each one by hand, retypes what matters, and assembles the case. The pages pile up. The patient waits.
We are building the pipeline that cracks every document, vectorizes it, and files it into Salesforce and SharePoint on its own — then assembles the Letter of Medical Necessity bound for the Department of Labor. The same engagement that returns thousands of administrative hours to the provider is the thing that gets a sick worker his benefits sooner.
A showstopper ingest problem. A model that works in notebooks but not in production. A pilot that never left the pilot. That is our lane.
Six questions — one for each pillar of the Scoreboard. Answer them honestly and we will show you where your project is strong, where it will stall, and what to bring to a call. Nothing is sent anywhere; the readout is yours.
Every engagement begins with a fixed-fee week of reconnaissance — so both sides know what we are buying into before the meter starts running.
I started Zalattria because I kept watching good healthcare-AI projects die in the gap between a promising demo and something a clinician could actually trust. I came up through biomedical and computer engineering — so I read a stalled clinical system the way an engineer reads a failing patient: find the real cause, not the loudest symptom.
Today, when you hire Zalattria, you get me — writing the spec, building the system, answering your messages myself. No sales engineer, no offshore handoff. That is the honest version, and it is also the point.
I am building toward a small, senior team — people who hold the same line on patient safety and plain dealing that I do. When that team grows, the promise will not change: the person who designs your system is the person who builds it.
A thirty-minute call with the architect who would actually be on your project. No sales engineer. If we are not the right fit, we will say so on the call and, where we can, point you to someone who is.