Engineering ambitious ideas
into production-ready AI
Chaitrishodaya designs, builds, and operates complete AI systems that turn ambition into production-ready reality — engineered for real-world scale, built to earn trust from day one.
Eleven systems, one standard: built to be trusted, not just to work.
Confidence at every stage of the AI lifecycle
Eleven systems, each built for one stage of shipping AI you can trust — and wired to work together, not just sit side by side.
Ship real AI systems
Multi-agent pipelines, retrieval and decision models — engineered to production standard, not prototypes.
Test what evaluations miss
Statistical trust checks, adversarial pressure and cross-framework tests that surface what clean-question evals never see.
Block regressions before deploy
Every prompt versioned like code and scored against a golden dataset — below threshold, it doesn’t ship.
Catch failures in production
Five AI-specific failure modes watched in parallel, with root-cause lookup back into the quality gate.
AI that proves itself, not just performs
Every capability below exists because a real system failed in a way ordinary testing didn’t catch.
Catch regressions before they ship
Prompts are versioned like code and scored against a golden dataset before they can reach users.
Prove behaviour under pressure
Graduated adversarial testing finds the exact point a rule breaks — where clean-question evaluations see nothing.
See failures before your users do
Five AI-specific failure modes watched in parallel — including inside the monitor itself.
Built for high-stakes domains
Places where an AI system being wrong costs money, trust or compliance — and where we’ve already built something real.
Banking & financial services
Document intelligence where access control runs before retrieval, with a tamper-evident audit trail.
BDIS →Supply chain & manufacturing
Supplier risk scoring with explainable IsolationForest and SHAP, on a full-stack platform.
SCIP →Education
A Socratic tutor in 35 languages that guides instead of answering — and holds under pressure.
ARIA →Software engineering & QA
From Jira story to runnable tests, with pipeline cost cut from ₹50 to ₹8 per run.
QAIP →Enterprise operations
Role-specific intelligence from CEO to engineer, answered from live Jira and sprint data.
ZENTRAVIX →AI governance & compliance
Every prompt decision recorded — who, what and why — mapped to EU AI Act Articles 9, 12 and 14.
AIPQ →The impact, in real numbers
No testimonials yet. Just what actually happened.
We’re early, so instead of quotes, here are three real incidents — each one measured and documented.
A prompt edit labelled “sped up responses” dropped quality to 0.60 against a 0.90 threshold. Drift detection flagged it and rolled back to the last good version in four minutes.
Read the AIPQ story →Automated evaluation reported 94% compliance with “never give the answer”. A 20-case adversarial suite found 22.2%. Prompt hardening, calibration and temperature tuning closed it to 100%.
Read the ARIA story →Boundary testing found BCrypt.matches(null, hash) returning true — any password would unlock a deleted account. Fixed with a null guard, then locked into CI so it can’t return.
The thinking behind the systems
Why, not just what
Architecture decisions — including the alternatives that were rejected, and why.
Build notes
What happened while building, written as it happened — failures included.
Work in progress
Ongoing research, clearly labelled by stage — never presented as finished.
Company profile
Every product and service in one document, ready to share.
See a system live, one-on-one
Tell us which system you're interested in, and we'll follow up personally with access and a walkthrough.
Every AI system has a blind spot it hasn't shown you yet
Tell us about yours — before your users find it for you. We typically reply within one business day.