SOLUTION · HEALTHCARE
Healthcare AI: Safe Automation Under Professional Oversight
Deploy AI where clinical accountability can't be waived. We start with low-risk administrative work, keep human verification on every clinical-adjacent output, and de-identify every record before it reaches the model.
6–8 weeks
to first scenario live
100%
of clinical-adjacent output human-verified
VPC
data never leaves your environment
Start with the paperwork, not with patient-safety risk
For most provider organizations, the bottleneck isn't a shortage of AI ambition. It's that physicians, nurses, and admin staff lose hours every day to record summarization, patient education materials, prior authorizations, and forms that never end. This work is high-volume and repetitive, and it eats the time that should go to patients. Yet because it touches PHI (protected health information) and clinical judgment, most teams are right to hesitate before handing it to AI.
We split the problem in two: low-clinical-risk work and clinical-adjacent work. We prove results in the first, then extend with care into the second behind human verification. Our Forward-Deployed Engineers embed on-site and use RAG to connect your EMR, knowledge bases, and policy documents inside your VPC or private cloud, so the AI works only within a controlled, auditable data boundary.
We don't sell black-box diagnostic tools. Every clinical-adjacent output carries source citations, confidence flags, and a human review gate, and every record entering the model is de-identified first. What we deliver is a real system that ships in weeks and that your compliance and security teams will sign off on.
How it works
Deploy AI where clinical accountability can't be waived. We start with low-risk administrative work, keep human verification on every clinical-adjacent output, and de-identify every record before it reaches the model.
Record pipeline
Routing
Capabilities
Medical-record summarization
Condense lengthy hospital courses, lab reports, and consult notes into structured summaries where every conclusion carries a citation back to the source record, so clinicians can verify in one click rather than trust the model blindly.
Patient education materials
Generate customized instructions tuned to the patient's health literacy level based on diagnosis, medications, and post-op guidance, with multilingual versions; clinical-adjacent content is always reviewed by a clinician before it reaches the patient.
Prior-authorization support
Automatically match payer criteria against chart evidence, draft prior-authorization requests, and flag missing items, shifting staff from manual hunting to review and submission and cutting the back-and-forth resubmission cycle.
De-identification and privacy pipeline
Detect and mask names, MRNs, and other PHI before data ever reaches the model, run entirely inside your VPC, and retain a full audit trail that satisfies local privacy law and healthcare security requirements.
Human verification and confidence flags
Every clinical-adjacent output passes through a mandatory human-in-the-loop gate; the model proactively flags low-confidence or unsupported content so judgment is never quietly offloaded onto patients or frontline staff.
Administrative automation
From appointment reminders and referral documents to discharge summaries and internal form-filling, we automate the high-repetition, low-risk work so clinical staff can return to the care that actually needs their expertise.
Delivery cadence
- 01
WEEK 1–2
Scope and data-boundary design
FDEs embed and work with clinical, compliance, and security teams to map workflows, select one or two low-risk, high-ROI scenarios, and design the de-identification pipeline and in-VPC data-access boundary.
- 02
WEEK 3–4
MVP build with human-verification gates
Inside your environment, use RAG to connect records and knowledge bases, stand up a first working system, embed the mandatory human review and confidence-flagging mechanisms, and put it in front of a small group of clinical users.
- 03
WEEK 5–6
Pilot and measurement
Run the system in real workflows, capture accuracy, hours saved, and user-trust data, and tune prompts, citations, and review steps based on clinical feedback.
- 04
WEEK 7+
Go live, audit, and expand
After passing compliance and security review, go live with a continuous audit mechanism in place, then expand to more departments and clinical-adjacent scenarios as results warrant.
Use cases
01
Resident sign-out summaries
Before handoff, automatically compile the last 24 hours of course, medication changes, and abnormal labs into a structured summary with citations, so the incoming team can confirm quickly and miss less.
02
Post-op and home-care instructions
Generate home-care instructions tailored to the procedure and the patient's medications, route them through nursing review, then print or push to the patient's phone. Callbacks and phone questions caused by gaps in education go down.
03
Prior authorization and denial appeals
Auto-draft prior-authorization and appeal letters for frequently denied procedures, flagging the clinical evidence that needs strengthening, so billing and admin teams spend time reviewing rather than writing from scratch.
04
Rapid chart review at the point of care
As the patient is called in, generate a summary of their history, chronic-disease medications, and recent lab trends, letting the clinician spend limited visit time on the clinical decision.
05
Patient-services and admin correspondence
Draft replies to patient inquiries about billing, scheduling, and lab results with the relevant policy basis attached, then have admin staff confirm and send. Response times drop.
FAQ
No. We deliberately steer clear of automated diagnosis. Every clinical-adjacent output passes through a mandatory human review gate; the model only summarizes, drafts, and compiles, while the final judgment and accountability always rest with a qualified clinician. We also surface confidence levels and citations so reviewers can quickly spot what needs a closer look.
Data passes through an automated de-identification pipeline before reaching the model, masking names, MRNs, and other identifiers. The whole system runs inside your VPC or private cloud, so data never leaves your environment. We retain a complete audit trail that satisfies local privacy law and healthcare security requirements.
Our whole design premise is that the AI never decides alone. Clinical-adjacent output only takes effect after human verification, which means we add a faster draft source into your existing professional review process. Liability follows the same path it does today. Citations and confidence flags make it easier for reviewers to catch and stop errors.
Because administrative work is high-volume, low-risk, and clearly ROI-positive. It shows value in weeks while compliance and security teams build trust in the system. Validating the data pipeline and review mechanisms in low-risk scenarios before extending into clinical-adjacent work is the responsible path for patient safety and institutional accountability.
No. We integrate with your existing EMR, knowledge bases, and policy documents through RAG and run inside your environment without requiring a core-system swap. FDEs connect directly to your data and workflows, keeping the impact on your existing IT architecture to a minimum.
Yes, and it's required. We bring compliance and security in from week one to co-design the data boundary and de-identification pipeline, and every output is auditable and cited. The system must pass your organization's compliance and security review before going live. We never bypass your existing governance.
AI workflows,
built into your operations
We deploy forward — FDE and FDM — to build the AI agents and workflows your team runs on. Live in weeks, not quarters.