Sense7ai

Three case studies.One engineering posture.

Verixa, ArkOS, and Zita address three different engineering challenges — regulated-pharma compliance, agentic AI for the enterprise, and AI-native recruitment. The patterns across them are deliberate: built for production from day one, audited by design, gated by evaluation harnesses, and engineered to a spec a procurement team can defend.

Patterns across our work

What's consistent across all three.

The case studies look different on the surface — different industries, different stacks, different scopes. The engineering posture underneath is the same.

Production from day one

Live in production, not lab — and named, not hypothetical.

Every case study describes work that is shipped, running against real users in a real regulatory frame. No demoware. No 'pilot.' No 'roadmap-only' claims dressed as outcomes.

VerixaCompliance pipeline live in regulated-pharma deployment under FDA 21 CFR Part 11 + ICH frameworks.ArkOSFour modules live in production across customer deployments — named customers (Innovspace, Infrastride) with quantified outcomes.ZitaEight AI engines live in Zita's recruitment platform with 50% faster resume evaluation against the manual baseline.
Audited by design

Every decision is traceable — engineering, not afterthought.

Whether the decision is a compliance review, an agent action, or a candidate match, the platform captures inputs, model, tools used, output, and confidence. A reviewer or auditor can answer 'why?' without engineering escort.

VerixaCompliance controls register with audit-trail per record — designed for inspector readability.ArkOSDecision trace per agent action: inputs, model invoked, tools called, output, and confidence — non-optional spine subsystem.ZitaMatch Analysis surfaces feature-level scoring with weightage and description per criterion — readable by recruiting leadership.
Eval-gated releases

Nothing ships without passing the harness.

Every release runs through regression, fairness, and adversarial-input evaluation gates before production. CI/CD blocks the release if any gate fails. The eval suite is the line between merge and prod — not a milestone before launch.

VerixaPre-deploy validation against regulated-pharma compliance criteria; controls re-tested on every release.ArkOSEvaluation harness gates modules on regression and behavioural tests; every release passes the harness or it does not ship.ZitaThree release gates (regression / fairness / adversarial) on every PR — zero AI engines have shipped with failures.
Spec-grade engineering

Engineered to a spec a procurement team can defend.

Stack accuracy, deployment posture, framework references — every claim on every case study page is procurement-checkable. No marketing-grade abstraction; if a stack item is on the page, it is what's running.

VerixaFrameworks-in-scope named explicitly: 21 CFR Part 11, EU Annex 11, ICH E6 R3, Q9, Q10, EU GMP, GVP, ICH E2D / E2E.ArkOSStack identified to the framework level — TypeScript (React + Vite), Node.js + Express + Prisma, K8s, Anthropic, OpenAI.ZitaOn-premise private cloud deployment; stack named to ORM level (Django, scikit-learn, sentence-transformers, pgvector).
At a glance

The three case studies, side by side.

What each engagement was, where it lives, what it changed.

DimensionVerixaArkOSZita
IndustryRegulated pharma (manufacturing + pharmacovigilance)Enterprise / agentic AI business platformRecruitment / Applicant Tracking
Engagement typeAI compliance pipelineSix-module agentic AI platformEight AI engines + public REST API
StatusLive in productionFour modules live · Compliance in build · Finance on roadmapAll eight engines live
StackPython · regulated-pharma compliance frameTypeScript (React + Vite) · Node.js + Express + Prisma · PostgreSQL · Redis · Kubernetes · Anthropic · OpenAITypeScript (React) · Python (Django) · PostgreSQL + pgvector · Redis · On-premise private cloud
Headline outcomeInspector-readable compliance trail across nine regulated frameworksInnovspace + Infrastride: −60% overdue payments, +30% lead-to-client, 0% lead fall-through50% faster resume evaluation · 100% audit-readable decisions · 0 production regressions
Engineering invariantsValidated lifecycle · audit-readable · risk-scaled evidenceModule isolation on a shared spine · cognitive transparency · HITL gatingReliability · Explainability · Fairness · Eval-gating
Engagements

Three published case studies.

03 / 03 Live
01Case Study

Verixa

AI compliance pipeline for regulated pharma

Frameworks in scope
FDA 21 CFR Part 11EU Annex 11ICH E6 R3 (GCP)ICH Q9 (QRM)ICH Q10EU GMPGVPICH E2DICH E2E
02Case Study

ArkOS

Six-module agentic AI business platform

Modules
ArkOS CRMArkOS Sales & MarketingArkOS CLMArkOS OpsArkOS FinArkOS Compliance
03Case Study

Zita

AI feature surface for recruitment and applicant tracking

AI subsystems delivered
Resume parsingSemantic candidate–job matchingBias-aware rankingAI-generated job descriptionsAI-generated interview questionsCandidate profile intelligenceAnalytics & reporting

Sense7ai's published case studies are with affiliated companies inside the Aeonn Ark Group portfolio and third-party engagements: Verixa, ArkOS, and Zita. Individual case-study pages document each engagement against the six-layer evidence template in our build kit. Until the client authorises disclosure of engagement detail in writing, each per-client page is excluded from search indexing. Affiliation is disclosed openly in regulated-customer diligence — see security. Sense7ai is actively expanding into arm's-length third-party engagements.

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