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.
Founded by builders, not just strategists
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.
AI engineering leader working at the intersection of AI research and production engineering — exploring emerging techniques, then proving them by shipping real systems, not papers or prototypes. 13+ years in enterprise and software engineering, with AI-focused work carried out throughout.
Architected an autonomous AI automation platform that reduced manual regression by 70% across 2,350+ CI builds in a prior enterprise engineering role. Independently designed and built ten AI systems end to end — including a prompt-quality gate with automatic rollback, a Socratic AI tutor tested to 100% adversarial compliance, and a supply-chain risk platform that caught a real production auth bypass before launch.
Technology leader, enterprise architect and transformation strategist with 20+ years of experience spanning IBM, PwC, Oracle, TCS, HCL and Wipro — working across business strategy, technology architecture, engineering and innovation, from boardroom vision through to production.
Connects the full transformation lifecycle: Business Vision → Strategy → Architecture → Modernization → Engineering → AI → Operations → Business Value. Specializes in Cloud and AI Transformation, Agentic AI, Enterprise Architecture and Platform Engineering. As Co-Founder, building Chaitrishodaya into a trusted partner helping organizations move confidently from legacy to modern, intelligent, and autonomous enterprises.
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.