Applied products

Three products. Three risk profiles.One engineering system.

NyayKavach, Spectra+, and ClarovaOS show what changes when AI leaves experimentation and becomes part of legal work, health research, and commerce operations.

Explore the portfolio Apply EAINE to one workflow

01 · LegalHuman authorityNyayKavach

02 · HealthEvidence-aware signalsSpectra+

03 · CommerceOperational economicsClarovaOS

Product delivery informs EAINE. It does not independently validate the framework or establish promised outcomes.

Why this portfolio matters

The discipline is informed by products that must work under different kinds of pressure.

These are separate live products created by the founder behind EAINE. Together they expose recurring engineering needs: explicit authority, observable behaviour, trustworthy evidence, secure operations, and controlled learning.

What you can concludeEAINE is grounded in the practical difficulty of shipping and operating AI-enabled products.What you cannot conclude yet: that EAINE independently caused product effectiveness, compliance, or commercial outcomes.

Product environments

Inspect what is live, what it demonstrates, and what evidence comes next.

Each product is presented with an explicit maturity and evidence boundary so credibility grows with the evidence rather than the marketing claim.

01Legal operations

Live product

NyayKavach

Turn complex legal work into an accountable operational system.

A litigation-operations platform connecting matters, hearings, documents, research, deadlines, and client communication so legal teams can work with context while qualified people retain legal authority.

What it demonstrates

Structured matter operations instead of a general-purpose legal chatbot
AI assistance around professional judgment rather than replacement of it
Continuity across evidence, hearings, tasks, citations, and communication

EAINE lensHuman authority · traceable work · evidence continuity

Inspectable today: Public product experience, workflow design, and an inspectable operating model.
Evidence to build next: Matter-preparation time, deadline exceptions, review and override patterns, professional acceptance, and client-service signals.

02Health AI research

Live research preview

Spectra+

Make every AI-generated health signal reveal its evidence.

A camera-based physiological-signal research platform with signal-quality gates, evidence classifications, on-device processing by default, exportable history, and a built-in reference-device validation workflow.

What it demonstrates

Measured, derived, estimated, screening, and experimental outputs kept visibly separate
Signal-quality gating and abstention before a reading is presented
Validation workflow embedded into the product rather than added as a report later

EAINE lensConfidence gating · evidence classes · validation in the product

Inspectable today: Working scan console, methodology guide, evidence labels, local history controls, and validation protocol.
Evidence to build next: Paired reference-device studies, bias and limits of agreement, subgroup performance, device coverage, and independent review.

03Commerce operations

Live product ecosystem

ClarovaOS

Connect fragmented marketplace operations into one controllable workflow.

A commerce-operations ecosystem spanning marketplace reconciliation and recovery workflows through InvisibleDukan, alongside Clarova.in as a live retail environment.

What it demonstrates

Operational AI applied to high-volume marketplace exceptions
A shared platform serving both merchant operations and a live commerce environment
A path from activity metrics toward reconciled, accepted business outcomes

EAINE lensOperational exceptions · platform reuse · measurable economics

Inspectable today: Public product experiences across marketplace operations and commerce.
Evidence to build next: Cohort definitions, reconciled-value methodology, precision of identified exceptions, accepted recoveries, cycle time, and audit coverage.

What repeats across domains

Different products reveal the same enterprise engineering gap.

The specific controls change with consequence, but the operating discipline remains recognisable.

The AI does not own the consequence.

Legal judgment, health interpretation, and commercial acceptance remain with accountable people.

Every important output needs a status.

Source, quality, confidence, evidence level, exception state, and review responsibility must remain visible.

A working product is the start of evidence.

Deployment proves that something can run. Outcome, safety, fairness, reliability, and economic claims require additional measurement.

IntentEngineeringEvidenceHuman decisionOperational learning

Your starting point

Do not copy a product. Copy the discipline of making one workflow inspectable.

Choose a consequential workflow, name the outcome and authority, expose the evidence boundary, and measure what changes before deciding to scale.

Build a 90-day starting path Review the evidence model