AI Evaluation and Training Data
Create expert datasets, annotation workflows, quality rubrics, and human-review systems for models, RAG applications, and AI agents.
Training data · Annotation QA · Human feedback · Quality gatesFounded by an IIT alumnus and former Amazonian
Reliable AI and data systems for operations teams
Evaluate AI. Build reliable data. Improve operational decisions.
From expert training data and model quality to production-ready infrastructure and analytics, we build systems designed for measurable work.
See how human evaluation improves AIThree connected capabilities
Start with the problem that matters now.
AI quality, reliable data, and operational decisions belong to one system. We can begin with one focused need and build from there.
Data Infrastructure and Engineering
Turn fragmented operational data into dependable pipelines and governed data layers that teams can monitor, maintain, and use with confidence.
Data pipelines · Quality checks · Infrastructure · MonitoringForecasting and Operational Analytics
Build forecasting, inventory, supply-chain, and decision systems that help teams understand what is changing and decide what to do next.
Forecasting · Inventory signals · Supply chain · Decision analyticsOne connected delivery model
From expert input and operational data to measurable outcomes.
We combine human judgment, data engineering, and analytical discipline to help teams reduce risk, act faster, and make clearer decisions.
A clear working rhythm
A clear path from problem to operation.
- 01
Define
Agree on the workflow, users, risks, inputs, and the measures that will define a useful result.
- 02
Build and measure
Build the quality system, data foundation, or analytical workflow and test it against realistic conditions.
- 03
Deploy and hand off
Deliver the working system with monitoring, documentation, ownership, and a practical next-step plan.
A defined way to begin
AI Quality Baseline
A focused engagement for teams that need to understand model, RAG, or agent quality before adding more automation or moving into production.
You bring
A model, RAG system, or agent workflow, representative examples, and the business decision the system needs to support.
We build
A realistic evaluation set, calibrated reviewer rubric, failure taxonomy, and clear acceptance criteria.
You receive
A quality baseline, prioritized fixes, and a decision memo covering what to improve before production.
Typical window
Two to four weeks, adjusted to system access, evaluation scope, and reviewer requirements.
Example deliverable set
What the engagement produces
04
Core outputs
Adapted to the system and risk level 01
Evaluation design
Representative tasks, rubric, quality dimensions, and pass criteria.
02
Review system
Reviewer guidance, calibration method, and agreement checks.
03
Failure map
Recurring issues grouped by retrieval, instruction, content, or safety.
04
Decision memo
Prioritized fixes, acceptance criteria, and the recommended next step.
Inside an AI quality engagement
Human expertise where model quality breaks down.
Domain specialists and trained reviewers apply calibrated judgment, resolve disagreement, and trace failure patterns so teams know exactly what to improve.
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.