Founded by an IIT alumnus and former Amazonian

AI evaluation, data infrastructure, and operational analytics for small and mid-market operations teams

Evaluate AI systems. Build reliable data infrastructure. Improve operational decisions.

From expert training data and AI evaluation to production-ready data infrastructure and analytics, we build systems designed for measurable business outcomes.

Start with a Reliability Baseline Explore the three capabilities
Explore our work

Three connected capabilities

Start with the problem that matters now.

AI capability

AI Quality and Training Data

  • Evaluation datasets
  • Annotation QA
  • Human review
  • Quality gates

Data capability

Data Infrastructure and Engineering

  • Reliable pipelines
  • Data contracts
  • Quality checks
  • Monitoring

Operations capability

Forecasting and Operational Analytics

  • Demand forecasting
  • Inventory signals
  • Seller operations
  • Decision support
Hetanor AI

A clear working rhythm

A clear path from problem to operation.

  1. 01

    Define

    Clarify whether the first priority is AI quality, data reliability, or operational decision support.

    Output Scope, stakeholders, success metrics, and first operating baseline.
  2. 02

    Build and measure

    Build the evaluation workflow, data foundation, or forecasting layer and validate it against real-world scenarios.

    Output Working version, test evidence, and measurable reliability gaps.
  3. 03

    Deploy and hand off

    Deliver the workflow with monitoring, documentation, knowledge transfer, and a practical next-phase roadmap.

    Output Handoff pack, monitoring view, and next improvement path.

One baseline, three entry points

Reliability Baseline

A focused engagement for small and mid-market teams deciding whether the immediate problem is AI quality, data pipeline reliability, or forecasting and operational analytics before scaling.

You bring

An AI workflow, data pipeline, forecast, or operational decision that needs more confidence before scaling.

We build

The right baseline for the priority: evaluation rubric, data-quality checks, or forecasting and analytics validation.

You receive

Evidence-backed gaps, prioritized fixes, and a decision memo for model quality, data reliability, or operational decisions.

Typical window

Two to four weeks, adjusted to system access, data availability, review needs, and decision complexity.

Example deliverable set

What the engagement produces

2–4 weeks
AI quality Data reliability Operational decisions
04

Core outputs

Adapted to AI, data, or operations scope
01
Baseline design

Inputs, success metrics, checks, and acceptance criteria for the selected service area.

02
Quality checks

Evaluation review, data validation, or forecast sanity checks depending on the project.

03
Reliability map

Issues grouped by model behavior, data pipeline, signal quality, or decision risk.

04
Decision memo

Prioritized fixes, acceptance criteria, and the recommended next step.

Example scope only. Final outputs depend on system access and agreed acceptance criteria.

Reliability workflow across AI, data, and operations

Human expertise where systems need measurable confidence.

Each engagement follows one focused workflow. AI quality, data engineering, and forecasting are separate tracks, but each track moves from raw input to evidence, controls, and a usable operating decision.

Three independent delivery tracks From unclear work to usable evidence.
01

AI quality workflow

Evaluate model behavior with human judgment and domain rubrics.

  1. Prompt set Tasks, context, expected behavior
  2. Rubric Correctness, completeness, safety
  3. Human review Annotation, scoring, disagreement checks
  4. Failure map Patterns, gaps, priority fixes
  5. Quality baseline Evidence for model improvement
02

Data engineering workflow

Convert scattered sources into governed data that teams can use.

  1. Sources Files, apps, APIs, business events
  2. Ingest Batch, incremental, validation checks
  3. Transform Clean, join, model, metric definitions
  4. Govern Quality rules, contracts, lineage
  5. Trusted layer Monitored data for analytics and AI
03

Forecasting and analytics workflow

Turn demand signals into decisions for operations teams.

  1. Question Inventory, supply, seller, BFSI need
  2. Drivers History, seasonality, constraints
  3. Forecast Scenarios, confidence, exception flags
  4. Decision view Dashboard, alerts, review cadence
  5. Action rhythm What to adjust, monitor, and improve

Company and confidentiality

Built for serious AI and data work.

Hetanor AI is based out of Bangalore and founded by an IIT alumnus with former Amazon experience. We help teams build dependable AI evaluation, training data, data infrastructure, and operational analytics systems.

Brand meaning

HET
Human evaluation and training data
AN
Annotation
OR
Operational reliability

Human judgment.
Better models.
Reliable systems.

01 / Founder Shivanand Kumar

IIT alumnus and former Amazonian leading Hetanor AI's evaluation, data, and analytics work.

View LinkedIn
02 / Company HETANOR DATA & AI PRIVATE LIMITED

Registered company operating as Hetanor AI for AI evaluation, data infrastructure, and operational analytics engagements.

03 / Working model Confidential by default

NDA available before sensitive access. Client data is never reused without permission.

AI evaluation For model quality, annotation QA, human review, rubrics, failure analysis, and training data programs. Data engineering For pipelines, data quality checks, governed layers, monitoring, and production-ready data foundations. Forecasting For supply chain forecasting, seller services analytics, BFSI analytics, inventory signals, dashboards, and decision support workflows. Other / not sure For early-stage ideas, unclear scope, mixed AI/data problems, or a first conversation to define the right path.
[email protected]

Begin with one clear challenge

Tell us what needs to become more reliable.

Share the AI system, data workflow, forecast, or operational decision you want to improve. We will help define a sensible first step.

Confidential by default. NDA available before sensitive material is shared; client data is never reused without permission.