Tathastha Labs
Abstract wireframe data-network installation, rendered in the brand's botanical green, evoking an AI research lab
Data science Applied AI Domain engineering

An impartial laboratory
for data and AI.

We design evidence-led data systems and applied AI for regulated, high-stakes industries — engineered for accuracy, audited for trust, and tuned to the domain in front of you. We started in healthcare; the same engine travels.

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Built for regulated environments

verified_user SOC 2 Type II policy ISO 27001 gavel GDPR description Data processing agreements lock Encryption at rest & in transit
01Decision Intelligence 02Computer Vision & Imaging 03Predictive Monitoring 04Generative 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 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 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 your device, inside your private network, or in your own private cloud — your data never has to leave it. It stays strictly yours, by architecture, not by policy promise.

Capabilities

AI built around operational reality,
not benchmarks.

Every model we ship has to earn its place in a workflow. We start from the front line, the record, 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
A data workstation in a bright office, with dashboards for applied AI and domain engineering on a wide monitor
A framed glass display in a lab showing a decision-intelligence framework: data ingest, model flow, and a verification log
01

Decision Intelligence

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

  • checkSegment-level risk scoring with calibration reports
  • checkPolicy-grounded recommendation engines
  • checkExplainability built into every output
  • checkHuman-in-the-loop review surfaces inside your systems of record
A professional workspace with a desktop scanner and monitor used for computer-vision and imaging pipelines
02

Computer Vision & Imaging

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

  • checkModality-aware preprocessing for structured and unstructured imagery
  • checkDe-identification and sensitive-data scrubbing at ingest
  • checkContinuous recalibration against population and data drift
  • checkUIs co-designed with the specialists who'll read the output
A sleek dashboard showing real-time predictive-monitoring signals and confidence intervals
03

Predictive Monitoring

Early-warning signals on streaming operational data — anomalies, degradation, 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 the systems you already run
  • checkAlert-fatigue budgets baked into model thresholds
  • checkOutcome dashboards for the quality and ops team
A clean workspace showing a generative assistant drafting structured notes and workflow copy
04

Generative Assistants

Ambient documentation, multilingual support copy, and workflow copilots — grounded in your sources, evaluated against safety rubrics before they ever ship.

  • checkAmbient assistants drafting structured records and summaries
  • checkRetrieval-grounded triage and support over your own protocols
  • checkAudience-facing copy in 30+ languages
  • checkRed-team and hallucination evaluation suites
05

Data Engineering

The unglamorous foundation: pipelines that handle your industry's data standards without silent data loss, with audit logs on every transformation and feature stores tuned for the way your signals actually behave.

  • Standards-native ingest, whatever your domain's format
  • De-identification and synthetic cohort generation
  • Feature stores for time-varying signals
  • Lineage and provenance on every transform
  • Real-time and batch hybrid architectures
  • Compliance-ready cloud reference deployments
A high-end workstation showing a large language model integration running inside a private environment
06

LLM Integration

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

  • checkOn-prem and VPC inference for privacy-safe deployments
  • checkRAG pipelines over your guidelines, policies, and reference material
  • checkStructured output extraction from unstructured documents
  • checkEvaluation suites benchmarked against domain 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 line-of-business workflows, coordinate cross-team tasks, and handle approvals — with human-in-the-loop guardrails and a full audit trail on every action.

  • checkTool-using agents for system navigation and coding or classification tasks
  • checkMulti-agent orchestration (LangGraph, AutoGen, custom)
  • checkCross-team coordination workflows with escalation logic
  • checkExplainable action logs for compliance and review
08

Fine-tuning & Adaptation

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

  • PEFT / LoRA fine-tuning on domain corpora
  • RLHF and expert-feedback alignment
  • Synthetic data generation for training augmentation
  • Evaluation frameworks for fine-tuned domain 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.

Book a discovery call
Industries

One engine, applied where the stakes are highest.

We built our practice inside healthcare because it rewards rigor and punishes shortcuts. The same engine — evidence-led data systems, audited AI, quiet reliability — travels. Here's where we're active, and where we're headed.

Active

Healthcare & Life Sciences

EHR modernisation, clinical decision support, imaging, interoperability, remote monitoring, and revenue-cycle intelligence — for hospitals, providers, and health-tech teams.

Explore healthcare arrow_forward
In development

Your industry, next

We're scoping our second vertical now. If your industry runs on regulated, high-stakes data — finance, insurance, energy, public sector, or something else entirely — we'd like to hear the problem.

Tell us about your industry arrow_forward
Selected outcomes

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

Our outcomes to date are all from healthcare engagements, our first and currently only fully-built vertical. See the full breakdown →

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
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 data privacy and de-identification? add

All engagements operate under a data processing agreement suited to your regulatory environment — a BAA in healthcare, equivalent frameworks elsewhere. We use industry-standard de-identification methods with documented re-identification risk assessments regardless of domain.

Can you integrate with our existing systems? add

Yes — we integrate with the systems of record you already run, whatever the domain: EHRs in healthcare, or their equivalents elsewhere. If you don't have an interoperability layer, we can stand one up.

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