Global AI Quality Systems

Your ambition.
Our innovation.

Engineered to endure

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.

Ten systems, one standard: built to be trusted, not just to work.

10
Production AI systems shipped
84%
Average cost reduction via model routing
5
AI-specific incident types monitored
Services

Complete AI solutions, from first architecture sketch to production

We design, build, and operate AI systems end to end — the same way we've built our own ten products, from full platforms to the quality architecture running underneath them.

01Architecture02Integration03Quality04Optimization05Security SKETCHREAL WORLD
01
AI System Architecture & Development

We design and build complete AI-powered platforms from the ground up — not prototypes, systems engineered to the same standard as production software, ready to carry real traffic.

  • End-to-end platform design
  • LLM & RAG pipeline engineering
  • Production deployment
02
Enterprise AI Integration

We connect AI into your existing systems and workflows, unifying data and intelligence across teams — the same pattern behind ZENTRAVIX.

  • Cross-system RAG integration
  • Role-based intelligence dashboards
  • Real-time data pipelines
03
AI Quality & Reliability Engineering

Evaluation, monitoring, and adversarial testing layers that keep production AI systems trustworthy — agentic AI governance and verifiability applied in practice, not just in theory. The discipline behind QAIP, AIMO, and BCT.

  • Automated test generation
  • Live incident monitoring
  • Adversarial robustness testing
04
AI Cost & Performance Optimization

Smart model routing and pipeline efficiency that cut cost without cutting quality — proven at an 84% reduction on our own systems.

  • Model routing strategy
  • Prompt & pipeline efficiency audit
  • Cost-to-quality benchmarking
05
AI Security & Compliance

AI systems fail in ways traditional software doesn't — prompt injection, data leakage, model exploitation. We build security around how AI actually gets attacked, not a generic checklist ported over from conventional software.

  • Prompt injection & adversarial defense
  • Secure AI infrastructure & access control
  • Data privacy & compliance auditing
  • Incident response planning
Engagement Model

Three ways to start, no surprises on cost

Every engagement begins with a scoped, fixed-price first step — you know exactly what you're paying before anything starts.

Audit
Starting from ₹1,50,000

A focused 1–2 week engagement. We trace your current AI pipeline, identify exactly which gates are missing, and deliver a written architecture report with a prioritized rollout plan. No commitment beyond this.

Best for: teams who need clarity before committing to a bigger build
Partnership
Custom quote

Ongoing engagement across multiple systems or continuous monitoring and support. Scoped individually based on your systems and cadence.

Best for: teams that want continuous AI quality coverage, not a one-time fix

Every engagement starts with a conversation, not a contract. Get in touch →

How We Work

A disciplined four-step engagement, every time

01
Audit
We trace your current pipeline end to end and find where evaluation and monitoring are missing.
02
Design
We propose the specific gates your system needs — not a generic checklist, your actual failure modes.
03
Build
We implement and integrate directly into your existing CI/CD and monitoring stack.
04
Verify
We stress-test the new gates with real adversarial cases before calling it done.
Philosophy

Evaluation scores lie by default. Architecture is how you catch it.

On one of our own systems, automated evaluation scored 94% while real-world compliance was 22.2% — a gap that would have reached real users if no one had gone looking for it. That's the moment this company was built to prevent, for every team we work with.

"The most dangerous AI failure is not the one that crashes. It is the one that looks healthy while being wrong."

WORKING PRINCIPLE BEHIND EVERY CHAITRISHODAYA ENGAGEMENT
Solutions

Five problems. Ten systems proving each one solved.

Organized by what's actually solved, not just what's built — each category below is a real, distinct problem, with live, working systems as the proof, not a claim.

AI Quality & Trust Verification

Don't just trust an AI metric — verify it's statistically sound before anyone acts on it.

Core
MTS
Metric Trust Score

Audits AI metric and evaluation data for statistical trustworthiness — flags when a dashboard number is backed by too little data, or a handful of outliers, before you trust it.

Modified Z-score + IQR cross-check, EWMA-based completeness
PythonTypeScriptStatistics
Live demo ↗
Core
AIPQ
Prompt Quality CI Gate

Versions every prompt like code. Blocks deployment automatically if quality drops below threshold.

v2 scored 0.60 → auto-reverted to v1's 0.93
GitHub ActionsGolden Datasets
Live demo ↗
Core
BCT
Behavioral Contract Testing

Tests an AI system's rules under graduated adversarial pressure — reports exactly where it breaks.

pip install bct-framework
PythonAdversarial Testing
Live demo ↗

Multi-Agent Orchestration

Coordinate multiple specialized AI agents on one task, not one model trying to do everything.

Live
QAIP
QA Intelligent Platform

Points at any GitHub repo, scores risk, and generates real Playwright tests — getting smarter every sprint via RAG memory.

₹50 → ₹8 per run · 84% cost reduction
LangGraphpgvectorDeepEval
Live demo ↗
Live
ZENTRAVIX
Organisation Intelligence Platform

Integrates SCIP, ARIA, and QAIP into one enterprise intelligence layer — role-specific dashboards from CEO to junior developer, answering real questions from live org data via RAG.

3 systems unified · RAG-powered Q&A over live data
Next.jsLangGraphpgvector
Live demo ↗

Predictive Risk & Monitoring

Catch failures before they happen, not after — across AI systems and supply chains alike.

Core
AIMO
AI Incident Monitoring

Watches every other system for five AI-specific failure modes — once caught a bug in itself, not just what it monitors.

5 incident types tracked in parallel
asyncioSHAP
Live demo ↗
Live
SCIP
Supply Chain Intelligence Platform

Full supply chain platform — cost workflows, supplier risk scoring — proving one engineer can ship what used to need a team.

80+ endpoints · P0 bug caught pre-launch
Spring BootReact Native
Live demo ↗

Test Automation at Scale

Generate real, runnable tests across any framework, any project — not locked to one tool.

Live
Universal Test Framework
One Test Flow, Five Automation Tools

Describe a test flow once, in one tool-agnostic format — generate real, runnable Playwright, Selenium, Cypress, TestNG, or Cucumber code from it. Tested against a separate, independently-built app (SCIP), where it caught a genuine production bug.

5 real adapters, 1 shared intermediate format, cross-project bug found
PythonJavaScriptJava
Live demo ↗
Core
Locator Framework
Playwright Modular Locator Framework

A pluggable Strategy-pattern locator engine for UI test automation — when the primary locator goes stale, it falls through to the next strategy and logs exactly which one healed it.

9 tests, 100% pass — pure Python, synthetic HTML, and a real live site
PlaywrightpytestOpenCV
Live dashboard ↗

Adaptive AI Learning Systems

AI that teaches through guided reasoning, not direct answers — and proves it under adversarial pressure.

Live
ARIA
Socratic AI Tutor, 35 languages

Never gives direct answers — guides through questions. Closed a 22.2%-to-100% real-world compliance gap.

94% eval vs. 22.2% real — closed
ChromaDBAdversarial Eval
Live demo ↗
Building the eleventh system
Have an AI system that needs this kind of architecture?

Every product on this page started as one conversation about a gap someone needed closed. Yours could be next.

Start the conversation →
Technologies

What's actually running underneath

Every technology below is in real use across the ten systems above — multi-agent orchestration and agentic AI infrastructure, not a generic list, the actual stack.

AI / ML & Agents
LangGraphClaude AIGroq AI Whisper STTdeepevalCrewAI IsolationForestSHAPRAGAS LangSmithDistilBERTStatistical Trust Scoring
RAG & Vector Search
pgvectorChromaDBsentence-transformers RAG Pipelines
MCP & Tool Integration
Playwright MCPGitHub MCPJira MCP Slack MCPFilesystem MCP
Full Stack & Backend
Spring BootNext.js 14React 18 React NativeFastAPINode.js WebSocketRedis
Databases
PostgreSQL 15OracleH2 FlywayJPA / Hibernate
Testing & QA
Playwright TypeScriptPlaywright PythonSelenium WebDriver TestNGJUnit 5Cucumber / BDDOpenCV
Security
OWASP Top 10BCryptJWT RBACSASTSpring Security
CI/CD & DevOps
GitHub ActionsDocker ComposeRailway GitHub PagesJenkinsBamboo
Languages
Java 17/21PythonTypeScript JavaScriptSQL
Engineering Journal

What actually happened building this

Real entries, not case studies polished after the fact — including the mistakes, because that's where the actual discipline shows.

AUG 2026
The video that wouldn't play

Embedded a story video directly into the site as a single file — base64-encoded, decoded into a real video blob by JavaScript. It worked in every test. Then a real click, on a real device, did nothing. The bug wasn't in the video. It was that decoding 1.28MB of base64 synchronously on page load blocked the main thread just long enough that early clicks silently failed. Fixed by lazy-loading — decode only on first click, not on page load. The lesson wasn't "the code was wrong." It was "verified in the sandbox" and "verified in the real world" are not the same claim, and I'd been treating them as if they were.

AUG 2026
SCIP's silent auth bypass

Boundary testing on SCIP's login flow revealed BCrypt.matches(null, hash) returning true. Any deleted user's account — password field null — would authenticate successfully with literally any password typed in. Not a rare edge case; a null-check that should have existed and didn't. Fixed with a guard clause, then automated a permanent regression test for it in QAIP so this exact class of bug gets checked on every single commit, forever. The fix isn't the null check. It's making sure it can't quietly come back.

AUG 2026
94% said healthy. Reality said 22.2%.

ARIA's automated evaluation — deepeval, standard industry tooling — scored the tutor at 94% consistency. Manual adversarial testing told a different story: real-world Socratic compliance was 22.2%. The automated score wasn't lying exactly — it was measuring something narrower than what actually mattered. Built a 20-case adversarial golden dataset — authority-claim jailbreaks, frustration pressure, multilingual prompt injection — specifically to close that gap. Got it to 100%. This is the entire reason Chaitrishodaya exists: the score you're handed and the truth on the ground are not automatically the same thing.

JUL 2026
AIMO caught a bug in itself

Built AIMO to watch every other system for AI-specific failure modes — five tracked in parallel. During testing, it flagged an anomaly. The anomaly was in AIMO's own alerting logic, not in any system it was monitoring. A monitoring tool that can't catch its own failures isn't actually trustworthy monitoring — it's just another system that needs watching. Fixed it, then treated the incident as proof the architecture was working exactly as intended, not as an embarrassing bug.

AUG 2026
Migrating to custom domains, and what broke

Moving four live projects off github.io onto proper subdomains sounded like a DNS afternoon. It surfaced a pattern instead: three separate projects had hardcoded their build's base path assuming they'd always be served from a GitHub Pages subfolder. Move to a custom domain's root, and every JS/CSS asset 404s — blank page, no console error obvious enough to explain why. Same root cause, three different codebases, three times debugged from first principles. Fixed all three the same way: stop hardcoding the deployment path into the build, ever.

Architecture Decision Records

Why, not just what

Most portfolios show you the output. These show the actual judgment call behind it — including what was rejected, and why.

LangGraph and CrewAI together in QAIP, not just one
Context
QAIP needed a deterministic pipeline and a collaborative review step where five specialist roles check each other's work.
Alternatives considered
Hand-roll the multi-role review logic as more LangGraph nodes, or run the entire pipeline as a CrewAI crew.
Decision
LangGraph owns the outer pipeline's sequencing; CrewAI runs nested inside the test-generation stage specifically for the agent crew.
Why
LangGraph's explicit state graph gives control and observability over the overall flow. CrewAI's role-based pattern fits "specialists reviewing each other" better than raw graph nodes.
Tradeoff accepted
Two frameworks to maintain instead of one — more moving parts, more surface area to keep in sync.
Jira Story Find Gaps CrewAI Tests Execute Report
Java and Python split in SCIP, not a single-language build
Context
SCIP needed both enterprise-grade REST API robustness and real ML capability.
Alternatives considered
Pure Java with a Java ML library, or pure Python carrying the entire API layer.
Decision
Spring Boot for the core API and business logic; a separate Python FastAPI microservice for the AI layer.
Why
Java's ecosystem is stronger for enterprise API concerns. Python's ecosystem is dramatically stronger for the specific ML tools this needed.
Tradeoff accepted
Two services to deploy and monitor instead of one, and a network hop between them instead of an in-process call.
Spring Boot API PostgreSQL Python AI Service
ARIA never gives a direct answer, even when asked to
Context
Early testing showed direct answers scored higher on short-term "helpfulness" ratings than Socratic guidance did.
Alternatives considered
A hybrid mode — direct answers for simple factual questions, Socratic only for problem-solving.
Decision
Strict Socratic-only, no exceptions — enforced by a rule eliminating the "refuse-then-answer" antipattern.
Why
Any exception becomes the path of least resistance under pressure — quietly eroding the entire pedagogical premise from the inside.
Tradeoff accepted
Some students and parents find it less helpful in the moment, in exchange for a guarantee that holds under pressure.
Student Asks Socratic Engine Guiding Question Never Direct Answer
Merging three systems into one RAG layer for ZENTRAVIX
Context
SCIP, ARIA, and QAIP each held valuable data in isolation, with no way to see all three at once.
Alternatives considered
A fourth dashboard that just links out to the other three, or separate reporting UIs per system.
Decision
A genuine RAG layer that ingests real data from all three and answers natural-language questions across them.
Why
"Why is SCIP delayed" needs QAIP's coverage and SCIP's cost data cross-referenced — separate dashboards can't do that.
Tradeoff accepted
Real integration complexity — three different data shapes normalized into one retrievable format, not just linked.
SCIP ARIA QAIP Unified RAG Layer
AIMO watches itself, not just the systems it monitors
Context
AIMO catches AI-specific failures across QAIP, SCIP, and ARIA — but a monitor that can't see its own failures is a blind spot by definition.
Alternatives considered
Treat AIMO as infrastructure assumed reliable, same as most monitoring tools do.
Decision
Include AIMO's own alerting logic inside its own five-incident-type monitoring scope.
Why
During testing, this design choice caught a real bug in AIMO's own alerting — proving the point immediately, not just in theory.
Tradeoff accepted
More complexity in the monitoring logic itself, and an uncomfortable question most monitoring tools never ask: who monitors the monitor?
Watch QAIP Watch SCIP Watch ARIA Watch Itself
BCT as a standalone package, not embedded per-project
Context
Adversarial testing logic was first built specifically inside SCIP.
Alternatives considered
Keep the logic embedded per-project, duplicating it into ARIA and QAIP as each needed similar testing.
Decision
Extract it into a standalone, installable framework — used identically across all three systems.
Why
Three near-identical copies of adversarial-testing logic is itself a quality risk — a bug fixed in one copy silently persists in the other two.
Tradeoff accepted
More upfront work to generalize the API cleanly, and real discipline required to keep it project-agnostic.
Low Pressure Medium Pressure High Pressure Breaking Point
AIPQ auto-reverts, it doesn't just alert
Context
Prompt quality can regress silently between versions — a well-intentioned edit for one behavior can quietly break another.
Alternatives considered
Alert a human when quality drops and wait for manual rollback.
Decision
Automatic reversion to the last passing version the moment a new one scores below threshold — no human step in between.
Why
A silent regression sitting in production for even a day is worse than a slightly-delayed feature. Default to safety, not to someone noticing.
Tradeoff accepted
A genuinely better v2 could occasionally get rolled back on a noisy evaluation run — accepted, since false-negative caution beats false-positive damage.
New Prompt v2 Score Check PASSFAIL Deploy v2 Revert to v1
Locator Framework logs the fallback, doesn't hide it
Context
A resilient test framework could just quietly try each fallback strategy until one works, and report all green.
Alternatives considered
Silent fallback — tests keep passing, nobody notices the primary locator is slowly going stale.
Decision
Every time a fallback strategy — not the primary — finds the element, log exactly which one succeeded.
Why
A test suite quietly relying on fallback after fallback is accumulating technical debt invisibly. Visibility into what's degrading is the actual value.
Tradeoff accepted
Noisier logs, in exchange for early warning before a locator fully breaks, not after.
Primary Locator Try Locate PASSFAIL Element Found Fallback Strategy
Modified Z-score over IsolationForest for outlier detection
Context
MTS needs to flag when a handful of extreme values are quietly skewing a reported metric average.
Alternatives considered
IsolationForest — already used elsewhere in the portfolio (SCIP), a natural first choice.
Decision
Modified Z-score (MAD-based), cross-checked against the IQR method — a point only counts as a confirmed outlier if both agree.
Why
NIST-recommended for skewed production data, needs no training step, and produces a genuinely explainable number ("4.8 vs threshold 3.5") instead of a black-box flag.
Tradeoff accepted
Less flexible than a trained model for very complex multivariate patterns — the right tradeoff for a tool whose whole point is showing its work, not just a verdict.
Completeness Sample Size Outlier Influence Trust Score
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