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Orvient

AI-First Enterprise Technology

Engineeringwhat’s next.With AI at the core.

Websites, web applications, and mobile apps — new builds, redesigns, and migrations off ageing platforms — and the AI systems behind them. One team, engineered to enterprise standards.

Reference architecture
ERPCRMDOCSEVENTSINGESTCONTRACTSSEMANTICSRETRIEVALINFERENCETOOLSORCHESTRATIONAPPSWORKFLOWSAGENTSVERIFYSOURCE SYSTEMSDATA PLANEINTELLIGENCE PLANECONTROL PLANEEXECUTION PLANEVERIFICATION / OVERSIGHT

Trusted to solve complex technology problems

  • Website Development
  • Redesign & Migration
  • Mobile Apps
  • Web Applications
  • AI Engineering
  • Cloud & Data

01The premise

AI isn’t a feature.
It’s the new foundation.

Enterprise technology stacks were designed around a simple assumption: a person would perform each step of the workflow, and software would record it.

That assumption no longer holds. We help organisations redesign the stack around intelligence — so systems interpret, decide, and act, and people govern the outcome.

Architecture / intelligence stack
  1. L1

    Data

    Contracts, lineage, and semantics that make enterprise data legible to machines.

    • Lakehouse
    • Streaming
    • Governance
  2. L2

    Intelligence

    Models, retrieval, and reasoning grounded in the organisation's own context.

    • LLMs
    • RAG
    • Evaluation
  3. L3

    Agents

    Bounded autonomy — systems that plan, call tools, and know when to escalate.

    • Planning
    • Tool use
    • Guardrails
  4. L4

    Applications

    Interfaces where judgement happens, designed around the decision, not the form.

    • Product
    • APIs
    • Interfaces
  5. L5

    Automation

    Workflows that execute end to end, with audit trails and human checkpoints.

    • Orchestration
    • HITL
    • Traceability
  6. L6

    Business outcomes

    Cycle time, cost, risk, and revenue — measured against a named baseline.

    • Measurement
    • Attribution
    • SLOs

02Capabilities

Built for the AI era of enterprise technology.

The websites and apps your customers use, and the AI, data, and platforms behind them — from one team. Most projects touch both halves, which is exactly why they belong together.

Websites & Apps

New builds, redesigns, and migrations — web and mobile.

AI & Platforms

The intelligence, data, and infrastructure behind them.

All capabilities

03AI-native engineering

From software that follows rules to systems that understand intent.

Adding a chat box to an existing application does not make it intelligent. We redesign the workflow around what the system can now infer, decide, and verify on its own — and around where a human still has to stand behind the outcome.

Rule-following software

  • Workflows encoded as branches, written once and slowly outgrown
  • Humans move data between systems the software cannot reach
  • Unstructured content sits outside the process entirely
  • Exceptions escalate to a queue and wait
  • Change requires a release

Intent-understanding systems

  • Objectives declared; the path is planned against live context
  • The system reaches the systems of record directly, under policy
  • Documents, calls, and messages become first-class inputs
  • Exceptions are reasoned about, then escalated with a recommendation
  • Behaviour improves through evaluation, not only through releases
Agentic execution loop
↺ verification and oversight feed the next intent

AI agent

A bounded actor with a defined objective, a tool budget, and an explicit stopping condition.

What we build

Objective framing, policy constraints, budgets

Engineering position

Unbounded autonomy is a design failure, not a feature

04Industries

Sector depth, expressed as workflows.

Industry expertise is not a logo on a slide. It is knowing which workflow carries the cost, the risk, and the delay — and what has to be true before intelligence can touch it.

01

Financial Services

Credit review, onboarding, and controls

Evidence-gathering across policy documents, filings, and internal systems — assembled, cited, and routed to a human decision-maker instead of collected by hand.

02

Healthcare

Clinical documentation and prior authorisation

Structured extraction from unstructured records, with validation rules and escalation paths that keep clinicians accountable for the decision.

03

Retail

Assortment, pricing, and service operations

Demand signals, inventory state, and customer context joined into one decision surface — and acted on continuously rather than weekly.

04

Manufacturing

Quality, maintenance, and supply planning

Sensor, MES, and ERP telemetry unified into models that surface deviation early enough for the plant to respond.

05

Technology

Product engineering and support operations

AI embedded into the product itself — and into the engineering system that builds it, from code review to incident response.

06

Telecommunications

Network assurance and customer operations

Correlating alarms, tickets, and topology so operations teams triage causes rather than symptoms.

07

Public Sector

Casework and citizen services

Case handling with a full audit trail, explicit reversibility, and disclosure of where automation was involved.

05Engagement models

Four ways to work with us.

Engagements are scoped to an outcome and a decision date. Teams are senior, small, and accountable for what they ship.

01

Build

Build new AI-native products and platforms.

Dedicated product teams — design, engineering, data, and AI — from first architecture to production release.

02

Transform

Modernize existing enterprise systems.

Assess the portfolio, sequence the work, and modernize in increments that ship value before the programme ends.

03

Accelerate

Extend engineering teams with AI-first specialists.

Embedded senior engineers who raise the ceiling of what your existing teams can build and operate.

04

Operate

Continuously improve and operate intelligent systems.

Run, evaluate, and improve AI systems in production — with SLOs, drift monitoring, and cost discipline.

06Selected work

Technology that moves the business.

Platforms we designed, built, and run ourselves — the same engineering we bring to client systems, with nothing hidden behind an NDA.

Client engagements are published here only with the client’s agreement, and only with their own measurement attached.

Orvient productEducation technology

EduJha

Read the detail
  • Next.js (App Router)
  • TypeScript
  • Prisma / PostgreSQL
  • NextAuth v5
  • Anthropic, Google & Groq models
  • Vercel AI SDK
  • React Native (Expo)
Problem
Competitive exam preparation runs on volume of practice and speed of feedback. Static question banks give neither — they don't adapt to where a candidate is weak, and human evaluation can't answer while the attempt is still fresh.
Approach
A platform, not a quiz app. Timed mock tests scored in real time, an AI tutor grounded in the candidate's own performance history, and analytics that turn each attempt into the next recommendation. Model calls run across several providers behind one interface, so no single vendor sets the cost or latency ceiling.
What shipped
A production web platform across multiple exam tracks — timed mock tests, immediate scoring, an AI tutor, per-candidate analytics, and subscription billing — with a native Android and iOS app built on the same backend.
Orvient productEducation technology

IGNOU Study Hub

Read the detail
  • Next.js 15
  • React
  • TypeScript
  • Server-side rendering
  • Structured data / technical SEO
Problem
Distance-learning students search by course code, one paper at a time, across scattered sources. Discovery is the product problem here — the platform has to be findable at the moment of search or it is never used at all.
Approach
An SEO-led architecture rather than a brochure site. A course taxonomy spanning IGNOU's undergraduate, postgraduate, diploma and certificate programmes, server-rendered so every course is independently indexable, with structured data and accessible, responsive markup throughout.
What shipped
A live, indexable catalogue across IGNOU's programme range, served from server-rendered routes for search visibility and first-load performance.
All work

07Technology

Deep technology. Practical outcomes.

We are deliberate about the stack and unsentimental about it. The list below is where our teams work most often — not a claim of allegiance to any of it.

01

Intelligence

  • AI / ML
  • LLMs
  • Agentic AI
  • RAG
  • Vector search
  • Evaluation
02

Languages

  • Python
  • TypeScript
  • Java
  • .NET
  • Go
  • SQL
03

Application

  • React
  • Next.js
  • Node.js
  • Spring
  • GraphQL
  • gRPC
04

Platform

  • Kubernetes
  • Terraform
  • AWS
  • Azure
  • GCP
  • OpenTelemetry
05

Data

  • Databricks
  • Snowflake
  • PostgreSQL
  • Kafka
  • dbt
  • Iceberg

08Engineering philosophy

AI-first doesn’t mean AI-only.

A model is the smallest part of an enterprise AI system. What surrounds it decides whether the thing survives contact with production.

Most failed AI programmes are not model failures. They are missing data contracts, absent evaluation, and unclear accountability.

01

Software engineering

Models are one component. The system around them is still software, and it is held to software standards.

02

Data foundations

Lineage, contracts, and semantics — the difference between an AI demo and an AI system.

03

Security

Threat modelling for prompt injection, data exfiltration, and tool misuse from the first design review.

04

Governance

Model registries, approval gates, and decision records that survive an audit.

05

Observability

Traces across every reasoning step, tool call, and retrieval — not just request latency.

06

Evaluation

Offline and online evaluation as continuous infrastructure, not a launch checkpoint.

07

Human oversight

Explicit escalation paths, reversibility, and accountable review where the stakes justify it.

08

Responsible AI

Bias testing, disclosure, and data-handling boundaries defined before deployment.

09Enterprise readiness

Built for enterprise reality.

Pilots run on goodwill. Production runs on boundaries, evidence, and an operating model — which is where most enterprise AI work actually stalls.

Trust boundariesSchematic
ENTERPRISE BOUNDARYIDENTITY / SSOPRIVATE NETWORKEGRESS CONTROLAPPLICATIONENTITLEMENTSAI RUNTIMEPOLICY / EVALRETRIEVEREASONACTGUARDRAILS + EVALUATIONIMMUTABLE AUDIT TRAILHUMAN REVIEW
  • 01

    Security

    Zero-trust boundaries, secrets management, and adversarial testing of AI-specific attack surface.

  • 02

    Governance

    Policy as code, model and dataset registries, and traceable approval workflows.

  • 03

    Scalability

    Architectures that hold their shape from pilot cohort to enterprise-wide rollout.

  • 04

    Reliability

    SLOs, graceful degradation, and deterministic fallbacks when a model is unavailable.

  • 05

    Compliance

    Engineering that maps to your regulatory obligations and produces the evidence auditors ask for.

  • 06

    Observability

    End-to-end tracing across services, data, prompts, and decisions.

  • 07

    Data privacy

    Residency, minimisation, retention, and redaction enforced in the architecture, not the policy document.

Orvient does not list certifications it does not hold. Certification and attestation status is shared directly during procurement.

10People

You are buying judgement.

Engagements are led by the people who designed and shipped the work below — not staffed out after the pitch. Small teams, senior by default.

  • Radha Kant Jha, Co-founder, Chief Executive

    Radha Kant Jha

    Co-founder, Chief Executive

    Sets the firm's direction and stays close to delivery. Co-founded EduJha, an AI-assisted exam platform now running in production across multiple exam tracks.

    • Engagement strategy
    • Delivery oversight

  • Nisha Kumari

    Co-founder, Chief Technology Officer

    Leads architecture and the AI practice. Designed EduJha's model layer, which routes across several providers behind one interface so no single vendor sets cost or latency.

    • AI architecture
    • Platform engineering

  • Devendra Jha

    Knowledge & Evaluation

    Owns the evaluation side of AI delivery — building the datasets, rubrics, and review processes that decide whether a model's output is fit to ship.

    • Evaluation design
    • Domain modelling

  • Tarun Kumar

    Product

    Shapes what gets built and in what order. Focused on the interfaces where people exercise judgement over what an AI system proposes.

    • Product definition
    • Interaction design

11 / Next step

Ready to build
what’s next?

Let’s turn your hardest technology challenges into intelligent systems that create measurable business value.

Talk to Orvientorvient.it@gmail.com
01

A working session

Ninety minutes with senior engineers, not a sales team.

02

A written point of view

Architecture, sequencing, and what we would not do.

03

A scoped first increment

Something in production inside a quarter.