Your AI is only as good as the data under it.

We audit what your data can actually support before anyone writes a prompt — and we'll tell you plainly if the answer is ‘not yet.’

Data-Layer FirstGrounded & EvaluatedSenior Engineers Only

See how we work→

Chennai, India — delivering for clients across the US, Canada and India.

Why most enterprise AI fails

The model isn't the problem. The data is.

Two years of enterprise pilots have proved that a convincing demo means almost nothing. They stall in the same place every time: the demo ran on a clean sample, and production runs on the real thing.

Duplicate records
Systems that disagree
Undocumented schema drift
Permissions nobody mapped

You cannot prompt your way out of a broken data layer.

So we invert the usual order. We audit what your data can actually support — and tell you plainly if the answer is “not yet.” Then we fix the foundation, and only then build on it. Slower to start, dramatically cheaper to finish.

Audit before build.

Fixed scope, fixed price. You get a decision, not a sales pitch.

Grounded and evaluated.

Every answer traceable to a source. Faithfulness measured before go-live.

Scores, not opinions.

“It works” isn't a deliverable. A benchmark is.

Engagement structure

Three steps. No open-ended discovery, no free pilots.

Step 1Start here

Data & AI Readiness Audit

Fixed scope, fixed price, 2–3 weeks.

Pricing: Quoted on enquiry, after a short scoping conversation.

You get

a data landscape map
a security & access review
2–3 ranked AI use cases with ROI estimates
a reference architecture
a costed proposal for the pilot

Why it exists

Discovering a logic mismatch after the AI is built costs multiples of what it costs to find now. The audit is the cheapest risk you will ever buy.

Step 2

Paid Pilot

6 weeks. One use case. Capped scope.

Acceptance criteria defined as evaluation scores agreed up front — not “it works.”

Step 3

Production & MLOps Retainer

Ongoing monthly retainer.

Monitoring, evaluation regression, retrieval and prompt maintenance, model upgrades. Models and vendors change constantly; the retainer is what keeps a live system from quietly degrading.

We never build before an audit, and we never run a free pilot. Both exist to protect your budget as much as our margin.

How we work

We never build before an audit.

We never run a free pilot.

Acceptance criteria are scores, not opinions.

We'll tell you if your data isn't ready.

You own the code — including the day you leave us.

What we do

Three layers. The bottom one decides whether the top one works.

Data engineering first, AI on top of it, and the platform work that keeps both running in production.

Data Engineering & AI Readiness

Ingestion and pipelines
Data-quality remediation and deduplication
Unified data models and lakehouse architecture
Schema and integration mapping across systems that don't talk to each other
Data-quality monitoring
Security, encryption, and permission mapping

Architecture //

Data Engineering & AI Readiness

from pipeline import Source

// 1. Ingest & Reconcile

src = Source.ingest(raw)

.deduplicate(key="entity_id")

.map_schema(to=unified)

.assert_quality()

src.load(lakehouse)

Applied AI & Retrieval Systems

Retrieval-augmented generation with hybrid search and reranking
Mandatory citations
Permission-aware retrieval
Agentic workflows with human-in-the-loop gates
Evaluation harnesses and faithfulness measurement
Observability and cost-per-query modelling
Model-agnostic architecture, so you're never locked to one vendor

Architecture //

Applied AI & Retrieval Systems

import langchain

// 2. RAG Pipeline

def query_kb(q):

ctx = vector_db.similarity(q)

prm = PromptTemplate(ctx)

return llm.invoke(prm)

Platform & Product Engineering

Golang, Python, Node microservices
AWS and Azure
Postgres, pgvector, MongoDB, Redis
Docker, Kubernetes, CI/CD
Next.js and React
Flutter
QA automation

This is the layer that makes the first two shippable. Most AI vendors can prototype. Fewer can operate the thing at 3 a.m.

Architecture //

Platform & Product Engineering

package main

// 3. Ship & Operate

func deploy() {

svc := docker.NewContainer()

db := postgres.ConnectPool()

await svc.Scale("auto")

}

Track record

Selected work.

Every figure below was measured on a live deployment. Active engagements carry no numbers yet, and where a client's confidentiality limits what we can publish we describe the work and leave the numbers out rather than approximate them.

Custom licensed apparel · Live commerce platform

Active

CollegeWear

Fifteen years of platform debt, cleared in the order we argue for: stabilise, remediate, re-architect — then AI.

WooCommercePHPAWSRedis

Problem

CollegeWear has made custom graduation stoles and officially licensed campus apparel since 2010, serving students, organisations and universities nationwide across two platforms — direct retail and institutional partners. A made-to-order configurator generates data continuously: every design a customer starts creates records and assets, whether or not the order is ever completed. Fifteen years of that, against a traffic peak concentrated into graduation season, is a demanding foundation to run a business on.

What we’re doing

A phased engagement that follows exactly the sequence we argue for everywhere else on this page.

First, stabilise — controlled deployment, staging parity, and a validated rollback path, so no change reaches production without a way back.

Second, remediate the data — governed archival with historical records preserved, and automated cleanup so the platform stops accumulating what it will never use again.

Third, re-architect — isolated environments per platform, metric-driven auto-scaling for seasonal peaks, in-memory caching, and asynchronous ERP processing so a customer's checkout never waits on an external system.

The data and AI layer comes after that foundation is sound. Not before it.

EdTech · multi-modal AI

Live

Owlo — 24/7 AI Tutor

One-to-one tutoring that scales, because the tutor talks back.

Postgres (pgvector)OpenAI WhisperGPT-4o

Problem

Scaling one-to-one tutoring is economically impossible with humans, and static tools lose students because they can't hold a conversation.

What we built

A multi-modal learning ecosystem with a two-way voice tutor (Whisper + GPT-4o), four flashcard types, six note formats, and gamified progression.

Results

Eliminated separate vector-database cost entirely by unifying embeddings in pgvector
Sub-120ms similarity search
3x increase in session duration after voice launched

Heavy industry · operations integration

Live

AppsInfy — Enterprise Work Intelligence Platform

Boardroom strategy and daily execution, connected as one structure instead of three disconnected tools.

GolangFlutterWindows Active Directory

Problem

Asset-intensive enterprises — oil, gas, mining — running disconnected legacy systems and spreadsheets, where strategy set in the boardroom never reaches the daily workflow. KPIs live in slide decks, progress is chased through emails and weekly check-ins, and compliance exposure sits in every gap between systems.

What we built

A unified digital operations platform (Go backend, Flutter hybrid app) tying fragmented workflows into a single interface, secured against Windows Active Directory.

Tri-layer synchronisation (KPI → Project → Task). What matters, how we get there, and who does what today, connected as one structure rather than three disconnected tools — so strategy and execution stop drifting apart.
Automated escalation engine. When a deadline slips or a metric drops, the system flags and routes it to the right stakeholder without anyone chasing a status update.
Silo-free rollup. Granular daily task data natively aggregates into leadership dashboards — no re-keying, no parallel reporting spreadsheet.

Results

Audit preparation reduced from three weeks to automated generation
API latency down 65% via Go concurrency models
Manual-entry anomalies reduced 98%

Lab supplies · Dual-channel commerce

Active · early stage

ScienceAide

Two commerce channels that share products and almost nothing else — prototyped in full before a line of production code.

Problem

A supplier of lab coats, safety goggles and lab kits selling through two channels that share products and almost nothing else — retail customers who check out with a card, and institutional buyers who need quotes, negotiated pricing, purchase orders and tax exemption. The same operational data has to serve both, plus the back office behind them.

What we’re doing

The product exists today as a validated interactive prototype covering the full commerce and back-office flow — a specification proven by clicking through it rather than by argument. We're building the production platform behind it, with the data layer and AI query interface designed in from the start rather than retrofitted later.

Staffing operations · complex business logic and data integrity

Live

PulseOps — Global Workforce Operations & Pre-Payroll Engine

One day of work has to come out correct three times over — client invoice, payroll processor, leave balance.

Problem

A staffing company running employees across branches, timezones, and concurrent client contracts had attendance data that nobody could fully trust. The difficulty isn't the timer — it's that one day of work has to come out simultaneously correct for three different consumers: the client invoice, the payroll processor, and the employee's leave balance. Get a single rule wrong and you over-bill a client, under-pay an employee, or both — and nobody notices until month-end.

What we built

A requirement-first workforce operations platform that acts as a pre-payroll data engine — computing hours, exceptions, and deductions, then handing clean, tamper-proof data to an external payroll processor and a draft invoice generator.

Split timezone evaluation. Every timestamp stored in UTC, but tardiness is evaluated against the client's timezone while holiday boundaries evaluate against the employee's local branch. Monthly payroll cut-offs run per branch, never at a global UTC midnight.
Simultaneous billing. An employee can hold concurrent active sessions across multiple clients, and both are billed — while overtime is computed strictly from physical desk time, so shared-service agents never trigger phantom overtime.
Audit and correction hub. Every override is logged with admin identity and before/after state, and approving a correction triggers cascading recalculation of every dependent metric.
Automated evaluation engine. Daily completion percentage drives attendance status, with punitive handling that voids payroll hours and adjusts leave balances automatically.

This is what data integrity looks like in practice. Automated decisions are only trustworthy when the rules underneath are exhaustively specified and consistently applied — the same discipline we bring to AI systems, where an ungrounded answer is just a data problem wearing a friendlier interface.

Enterprise RAG · digital twin

Live

BeyondAsk — Enterprise RAG Infrastructure

Generic AI writing sounds generic — unless the system learns one specific person's voice.

PythonLangChain

Problem

The goal was a system that writes in one specific person's voice — their phrasing, rhythm, and vocabulary.

What we built

The core Python intelligence engine using LangChain RAG, plus a central gateway with smart routing to select the right model per request. We also provided technical leadership to the client's internal application team.

Results

99.99% uptime via multi-provider failover
40% reduction in token waste through routing
Handles 10,000+ concurrent enterprise requests without degradation

Delivered under NDA · Supply chain and manufacturing

Active

Data Engineering & ML — Partner Engagements

Data engineering and machine learning delivered for a technology partner, on solutions built for their clients.

Stack withheld — NDA

What we’re doing

Two live engagements — data platform engineering for a supply-chain technology client, and machine learning for early defect prediction with a manufacturing client.

On the supply-chain engagement we own the data layer: every source that lands in the platform, and the pipelines that keep it flowing, so the analytics and forecasting built on top have a foundation they can trust. On the manufacturing engagement we work on the data analysis and predictive modelling. The partner holds the client relationships; we deliver the engineering.

We describe this engagement at capability level only. The architecture, data models, and solution design remain the confidential property of our partner and their client — which is exactly the discretion we'd extend to your work.

Engineering Guarantees

Transparency by Default

We don't just build software; we guarantee the quality, transparency, and maturity of our engineering process.

Engineering First

We prioritise architectural integrity over quick fixes.

Architecture Before Implementation

Every line of code starts with a design document.

Radical Transparency

You see what we see. No hidden backlogs.

Our Code Is Your Code

Direct access to Git, Jira, and Slack. We operate as a transparent extension of your engineering pod.

Git Repos

Jira Boards

Slack Channels

Internal Docs

How We Define Senior

System Design Defence

High-concurrency system design under real constraints

Code Review Challenge

Identify security vulnerabilities and architectural anti-patterns

The ‘Why’ Test

Technology decisions justified using first principles

Sync Guarantee

Timezone Alignment

Minimum four-hour daily overlap with your core engineering hours (EST, PST, or CET) to ensure no blocking delays.

EST

IST

We Don’t Just Ship — We Refactor

Mandatory linting and CI/CD quality gates

Dedicated refactoring capacity each sprint

Documentation-first delivery (Swagger, ADRs)

Automated dependency & vulnerability scanning

Zero-warning policy on production builds

Operational standards

Agile process with weekly reporting
Mandatory senior code reviews
Strict QA & UAT protocols
Business-hour aligned availability

Who we are

Architect-led. Credentialed in exactly what we sell.

Our leadership holds postgraduate specialisations in data engineering and machine learning from BITS Pilani — acquired mid-career, deliberately, as the industry moved. Hands-on delivery reinforced by formal architectural training.

Engineering Leadership

Nandha Kumar

Strategic Delivery Lead

6+ years engineering and delivery leadership

Product vision, client architecture, scoping, and delivery governance. Owns the translation between business outcome and technical execution — and the commercial terms that make it accountable.

Lalitha Varadarajulu

Chief Data Architect

M.Tech, Data Science & Engineering · BITS Pilani

Enterprise data lifecycles, complex ingestion, and data security. Architects platforms that leave data genuinely ML-ready, with encryption and access control designed in rather than retrofitted.

John Felix

Forward Deployed Engineer

4+ years Software, Data & AI Engineering

Full-stack engineering across software, data, and AI. Works close to the client, turning ambiguous requirements into systems that reach production rather than stopping at a prototype.

Maaran

Senior Backend Engineer

API design · Security · Distributed systems

API design, API security, and microservices architecture. Builds the distributed backend systems everything else depends on — designed to stay secure and maintainable as they scale.

Saran Kumar

Consulting

ML Systems Architect

M.Tech, AI & Machine Learning · BITS Pilani

Model orchestration, vector search, and backend scale. Builds distributed ML platforms that parse large, complex datasets under production load.

The full team

Seven full-time engineers and four consulting specialists.

Beyond the leadership above, delivery is carried by specialists in FastAPI gateways, UI/UX design, cross-layer feature pods, Next.js workflows, and AI edge-case validation.

Full-time

Ram Kumar
Sabimozhi
Senthilvel

Consulting

Shiva
Hari
Vignesh

Our story

Built by people who went back to school for this.

We don't ship demos. We ship systems that hold up after we leave.

Vision

To be the technical partner visionary companies trust with their hardest data and AI problems — combining academic depth with production discipline.

Mission

We turn fragmented operations into unified, trustworthy digital ecosystems — and we stay long enough to be accountable for them.

Our journey

2019–2021

Academic roots and first deployments

Began engineering ML algorithms at university and delivered our first commercial automation tools, while Saran joined Zoho to master large-scale, industry-standard software delivery.

2022–2023

Products and long-term partnerships

Built digital products including Yatn and i4option. Nandha joined Instrive Softlabs to deepen B2B expertise and expand our network, securing our first multi-year retention in San Francisco for LMS and HRMS platforms, while Lalitha built her career as a data engineer.

2024–Present

Enterprise AI and global delivery

Grew to seven full-time engineers and four consulting specialists. Delivered AppsInfy's operations ecosystem and the BeyondAsk AI suite. Currently engineering platforms for retained clients across Canada, the US, and India.

Also delivered

Mobility & Parking Network

High-Concurrency Payments & Real-time QR Validation

FlutterMERNServerlessStripe Connect

i4option Trading Interface

Real-Time Visualisation & Handle Payments

Flutter (Stream Architecture)WebSocketsNative Channels

Enterprise Learning Management System

Multi-Role Curriculum Delivery & Real-Time Student Analytics

Next.js (SSR)Firebase (Real-Time Sync)GCP Cloud Functions

Therapeutic Operations System

Patient Lifecycle Automation & Digital Health Records

Flutter (Multi-Tenant)FirebaseCloud Storage

Registration & Compliance

Inzeph is a GST-registered Indian firm — GSTIN 33CEUPN7008B1ZJ. MSME registered. Compliance documentation available on request.

FAQ

The questions we actually get asked.

If yours isn't here, ask us directly — we'd rather answer it properly than guess at it in advance.

For general knowledge, often yes. For questions about your data, a general model has no access to it, no permission model, and no way to cite a source. That's the gap we build into.

Let's talk about your data.

Tell us what you're trying to build and what your data looks like today. If the honest answer is “not ready yet”, we'll tell you — that's what the audit is for.

Free 30-minute architecture conversation

We reply to every enquiry within one business day.

What to expect: a 30-minute call, no deck, no pressure. We'll ask what you're trying to build and what your data looks like today. If we're not the right fit, we'll say so on that call.

Careers

Work with us

We're a small team doing production data and AI work — the kind that has to survive contact with real systems. If that's the experience you want, tell us what you've built.

Get in touch

Contact Information

reachus@inzeph.com

Phone

+91 95854 47986

Location

Chennai, India

Inzeph

Your AI is only as good as the data under it.

The model isn't the problem. The data is.

Audit before build.

Grounded and evaluated.

Scores, not opinions.

Three steps. No open-ended discovery, no free pilots.

Data & AI Readiness Audit

You get

Why it exists

Paid Pilot

Production & MLOps Retainer

Three layers. The bottom one decides whether the top one works.

Data Engineering & AI Readiness

Applied AI & Retrieval Systems

Platform & Product Engineering

Selected work.

CollegeWear

Problem

What we’re doing

Owlo — 24/7 AI Tutor

Problem

What we built

Results

AppsInfy — Enterprise Work Intelligence Platform

Problem

What we built

Results

ScienceAide

Problem

What we’re doing

PulseOps — Global Workforce Operations & Pre-Payroll Engine

Problem

What we built

BeyondAsk — Enterprise RAG Infrastructure

Problem

What we built

Results

Data Engineering & ML — Partner Engagements

What we’re doing

Transparency by Default

Engineering First

Architecture Before Implementation

Radical Transparency

Our Code Is Your Code

How We Define Senior

System Design Defence

Code Review Challenge

The ‘Why’ Test

Timezone Alignment

We Don’t Just Ship — We Refactor

Operational standards

Architect-led. Credentialed in exactly what we sell.

Engineering Leadership

Nandha Kumar

Lalitha Varadarajulu

John Felix

Maaran

Saran Kumar

The full team

Full-time

Consulting

Built by people who went back to school for this.

Vision

Mission

Our journey

Academic roots and first deployments

Products and long-term partnerships

Enterprise AI and global delivery

Also delivered

Mobility & Parking Network

i4option Trading Interface

Enterprise Learning Management System

Therapeutic Operations System

Registration & Compliance

The questions we actually get asked.

Can't we just use ChatGPT / Copilot for this?

Our data is a mess. Should we clean it before calling you?

How do we know the AI won't make things up?

What does this cost, and how long?

Who owns the code and the data?