Tathastha Labs
Abstract wireframe data-network installation in the brand's botanical green, evoking a clinical AI research lab
Data science Clinical AI Healthcare engineering

An impartial laboratory
for healthcare data and AI.

We design evidence-led data systems and clinical AI for hospitals, providers, and health-tech teams — engineered for accuracy, audited for trust, and tuned to the patient in front of you.

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Built for regulated environments

health_and_safety HIPAA policy HITECH verified_user SOC 2 Type II gavel GDPR shield ISO 27001 sync_alt HL7 / FHIR R4 fact_check 21 CFR Part 11 aware
01Clinical Decision Intelligence 02Medical Imaging Models 03Predictive Monitoring 04Generative Care Assistants 05Data Engineering 06LLM Integration 07Agentic Systems 08Fine-tuning & Adaptation
How we build

Edge-first, open-source-first.

Cloud AI has its place — but it's rarely the first thing we reach for. Before a workload ever touches a third-party API, we ask whether it can run entirely inside your clinical environment instead.

cloud_off

Cloud is the last resort

We start from what can run entirely inside your environment. Cloud inference is a deliberate fallback for workloads that genuinely need it — never the default.

code

Open-source before proprietary

When a task calls for an LLM, we reach for an open-weight model first. Proprietary cloud models only enter the picture when no open model can do the job well enough.

dataset

We don't assume data you don't have

Fine-tuning needs real, representative clinical data most organizations don't have on day one. We design around that reality with retrieval, prompting, and evaluation — not a training set that doesn't exist.

lock

Edge compute, by belief

Processed on-device, inside your private network, or in your own private cloud — PHI never has to leave it. It stays strictly yours, by architecture, not by policy promise.

Artificial Intelligence

AI built around clinical reality,
not benchmarks.

Every model we ship has to earn its place in a workflow. We start from the bedside, the chart, and the constraints — then bring the math to meet them. Models arrive with their assumptions documented, their failure modes mapped, and a human in the loop wherever it matters.

eco Evidence-led
Abstract visualization of interconnected clinical-data nodes, representing a healthcare data architecture
A translucent panel in a clinical lab showing a healthcare risk-scoring model with condition-level contributions and an overall risk score
01

Clinical Decision Intelligence

Risk-stratification and recommendation engines built on graded evidence, with the reasoning surfaced so a clinician can agree, override, or audit at a glance.

  • checkCohort-level risk scoring with calibration reports
  • checkGuideline-grounded treatment suggestions
  • checkExplainability built into every output
  • checkHuman-in-the-loop review surfaces inside the EHR
A radiology workspace with a desktop scanner and a monitor displaying chest imaging and detection overlays
02

Medical Imaging Models

Detection, segmentation, and triage models trained on DICOM-native pipelines, with drift monitoring for the populations you actually serve.

  • checkModality-aware preprocessing for CT, MR, X-ray, US
  • checkDe-identification and PHI scrubbing at ingest
  • checkContinuous recalibration against population drift
  • checkUIs co-designed with reading radiologists
A sleek dashboard showing real-time predictive patient-monitoring signals and confidence intervals
03

Predictive Patient Monitoring

Early-warning signals on streaming vitals — sepsis, deterioration, readmission risk — delivered at the right place in the workflow with the right confidence interval.

  • checkStreaming inference at edge and cloud
  • checkClosed-loop alerts wired into Epic, Cerner, or custom EHRs
  • checkAlert-fatigue budgets baked into model thresholds
  • checkOutcome dashboards for the quality team
A minimalist speech-to-text medical interface with a live waveform, used for ambient clinical documentation
04

Generative Care Assistants

Ambient documentation, multilingual patient education, and triage copilots — grounded in your sources, evaluated against clinical safety rubrics before they ever ship.

  • checkAmbient scribe drafting SOAP, H&P, and discharge notes
  • checkSymptom triage with retrieval over your own protocols
  • checkPatient-facing copy in 30+ languages
  • checkRed-team and hallucination evaluation suites
05

Healthcare Data Engineering

The unglamorous foundation: pipelines that handle HL7, FHIR, CDA, and X12 without silent data loss, with audit logs on every transformation and feature store tuned for the way clinical features actually behave.

  • HL7v2, FHIR R4, CDA, X12 ingest
  • De-identification and synthetic cohort generation
  • Feature stores for time-varying clinical signals
  • Lineage and provenance on every transform
  • Real-time and batch hybrid architectures
  • BAA-ready cloud reference deployments
A high-end workstation showing a large language model integration running inside a private clinical environment
06

LLM Integration

Frontier and open-source large language models deployed safely inside your environment — grounded in clinical sources, evaluated against accuracy rubrics, and privacy-preserving by design.

  • checkOn-prem and VPC inference for PHI-safe deployments
  • checkRAG pipelines over clinical guidelines and formularies
  • checkStructured output extraction from unstructured notes
  • checkEvaluation suites benchmarked against clinical accuracy
A glass panel diagram of an agentic system: an autonomous workflow routed through human-review checkpoints
07

Agentic Systems

Multi-step autonomous agents that navigate EHR workflows, coordinate care tasks, and handle prior-auth — with human-in-the-loop guardrails and a full audit trail on every action.

  • checkTool-using agents for EHR navigation and clinical coding
  • checkMulti-agent orchestration (LangGraph, AutoGen, custom)
  • checkCare-coordination workflows with escalation logic
  • checkExplainable action logs for compliance and review
08

Fine-tuning & Adaptation

Domain-specific model adaptation on your clinical data — from efficient LoRA runs to full RLHF alignment — so the model reflects your protocols, your population, and your standards of care.

  • PEFT / LoRA fine-tuning on clinical corpora
  • RLHF and clinician-feedback alignment
  • Synthetic data generation for training augmentation
  • Evaluation frameworks for fine-tuned medical models
  • Continual learning pipelines with drift detection
  • Model cards and bias reports for every release
From whiteboard to pilot

A working pilot in about eight weeks —
or a clear, honest answer that it isn't the right time.

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Healthcare Engineering

Software the rest of the health system actually wants to use.

We build the connective tissue that keeps care moving — interoperable, compliant, and quietly fast. From EHR modernisation to revenue-cycle intelligence, our work fits inside what you already run.

A

EHR & EMR Modernisation

Refactor legacy charting flows, extend Epic / Cerner / Athena with safe custom modules, or build a focused EHR for a specialty practice.

B

Remote Patient Monitoring

Device-agnostic RPM platforms, chronic-care workflows, and clinician dashboards that turn signal into action — without alert fatigue.

C

Interoperability

HL7v2, FHIR R4, CDA, X12, DICOM — engines, mappers, and conformance suites that let your data move where it's needed.

D

Telehealth & Virtual Care

Low-latency video, asynchronous messaging, e-prescribing, and scheduling — packaged for both new programs and existing networks.

E

Revenue Cycle Intelligence

Denial prediction, prior-authorisation copilots, and value-based-care analytics that translate the contract into a daily workflow.

F

Population & Public Health

Registries, screening programmes, and outcome dashboards built on de-identified data and reproducible cohort definitions.

Selected outcomes

Numbers from work we've shipped — directional, measured, and verifiable on request.

38%
less documentation time for clinicians at a 220-bed network using our ambient scribe
51%
earlier sepsis flagging vs. rule-based baselines on a pilot ICU dataset
4×
faster claims adjudication for a value-based-care plan after RCM copilot rollout
27%
fewer no-shows after deploying intelligent scheduling and patient reminders
6 wk
from kickoff to working MVP for a chronic-care RPM pilot
99.99%
measured uptime across the HL7 ingestion fleet we operate

Figures are drawn from anonymised engagements. Reference architectures and methodology notes available under NDA.

Specialties we've worked in

Deep enough to talk shop with the clinicians who'll actually use it.

Process

Four steps. No surprises in any of them.

01

Discover

We sit with the data, the workflow, and the constraints — and write down what we think before we touch a line of code.

02

Design

Architecture, model selection, evaluation rubric, and the UI — designed together so they don't fight each other later.

03

Deliver

Two-week cadences, demo every Friday, production environments behind your firewall or in ours.

04

Steward

Drift monitoring, periodic re-evaluation, and a clear path to hand the work back when you're ready to own it.

Frequently asked

A few of the questions we hear most.

How do you handle PHI and de-identification? add

All engagements operate under a BAA. PHI never leaves your environment unless explicitly authorised, and we use Safe-Harbor and Expert-Determination methods for de-identification with documented re-identification risk assessments.

Can you integrate with our existing EHR? add

Yes — we've shipped integrations with Epic (App Orchard / FHIR), Cerner, Athena, eClinicalWorks, and home-grown systems via HL7v2 and FHIR R4. We can also stand up an interoperability layer if you don't have one.

Do you train your own models, or wrap third-party APIs? add

Whichever is right for the problem. We fine-tune and train where data and risk justify it; we use frontier APIs (with proper data agreements) where they're the better tool. Either way you get the evaluation evidence to back the choice.

What does a typical engagement look like? add

A two-week Discovery, an 8–12 week pilot, and — if outcomes hold — a Pod engagement that scales the program. We commit to a no-fault exit at the end of each phase.

Do you publish or open-source your work? add

We publish methodology and tooling where it's ours to publish, and we open-source the parts that benefit the broader ecosystem — never client data or differentiated IP. We'll always tell you what we'd like to share before we share it.

Talk to the team

Have a problem worth getting right?

Send the one paragraph you'd normally write to a colleague. We'll reply within two working days with whether we think we can help — and what we'd do first if we could.

or email hello@tathastha.com directly