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Arixent

AI. Data. Product engineering.

Stop piloting. Start shipping.

We turn AI ambitions, data chaos and half-built products into software your customers actually use.

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See how we work
  • AI agents
  • Data platforms
  • SaaS products
  • Cloud & DevOps
  • Modernization
  • Analytics

Trusted by teams at

  • Chase
  • Starbucks
  • Micron
  • GoAudits
  • Pronto
  • LoopIQ
  • Bajaj Finserv
  • Nokia
  • Samsung
  • Verizon
  • Randstad
  • Life360
  • Intelliswift

Not sure which practice you need? Describe the problem and we will tell you.

Talk to an engineer

01 / Services

From the first workshop to software in production.

AI product engineering services across three focused practices. Twelve ways to move from a defined problem to software that runs.

AI Engineering

Strategy, generative AI, agents and machine learning, run with production discipline.

01

AI Consulting, Strategy & Governance

Use-case discovery, readiness, a roadmap and the governance that keeps AI defensible.

02

Generative AI & LLM Applications

LLM applications, RAG, conversational AI and AI features inside the products you already run.

03

AI Agents & Intelligent Automation

Agentic systems and automation that complete multi-step work under human oversight.

04

Machine Learning, Vision & MLOps

Forecasting, vision and edge models with the MLOps that keeps them reliable after launch.

Explore AI Engineering

Data Science & Engineering

Platforms, pipelines, analytics and models that make data usable.

01

Data Strategy, Architecture & Governance

Target architecture, operating model and the governance that makes data trusted and AI-ready.

02

Data Engineering & Platform Modernization

Batch and streaming pipelines, lakehouses and migrations off legacy warehouses.

03

Analytics, BI & Customer Insights

Semantic layers, dashboards and customer analytics people actually use.

04

Data Science & Predictive Modeling

Forecasting, churn, risk and optimization models, delivered by embedded data science teams.

Explore Data Science & Engineering

Product Engineering

Discovery, build, cloud, quality and the long run after launch.

01

Product Discovery, UX & MVP

Validate the idea, design the experience and ship a first version customers can use.

02

Custom Software, SaaS & Mobile

Web platforms, multi-tenant SaaS and mobile apps built to your requirements.

03

Cloud, DevOps & Quality Engineering

Cloud architecture, CI/CD, reliability and test automation that let teams release with confidence.

04

Modernization & Managed Engineering

Re-platform, re-architect and keep shipping, with a team that runs the product after launch.

Explore Product Engineering

02 / Why Arixent

Why teams pick us, and stay

01

Built by product people

Our founders built and scaled SaaS products, so we think in products and roadmaps, not tickets.

02

AI with production discipline

Evaluation, guardrails, observability and cost controls are part of every AI build.

03

Three time zones, one team

Our engineers overlap your working day and keep delivery moving overnight, so momentum never depends on a single office.

04

Senior engineers on your work

Small senior teams are led by architects who stay on the engagement.

05

Security and IP by default

Your code, data and models stay yours, under the confidentiality terms your legal team requires.

06

Engagement models that fit

Dedicated teams, outcome-based delivery pods, fixed-scope projects or build-operate-transfer.

03 / Find your path

Tell us what you are trying to do.

Start with the business problem. The right engineering path follows from it.

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04 / How we work

How we run an engagement

See engagement models
  1. 01

    Discover

    Workshops, use-case scoring, feasibility checks and architecture decisions before anyone writes code.

  2. 02

    Design

    Experience, data and solution architecture, prototypes and the evaluation criteria that define done.

  3. 03

    Build

    Short sprints, working software each sprint, automated testing and delivery pipelines from day one.

  4. 04

    Run

    Deployment, monitoring, model and product operations, and continuous improvement after launch.

05 / Industries

We know your industry, not just the technology.

Domain context decides whether AI pays off. We bring engineers who have built in your sector.

Engineering team examining silicon wafers in a lab

01

Semiconductors & Hi-Tech

Yield analytics, visual inspection, test data platforms and engineering tools for chip and hardware companies.

Financial charts layered over a blue city skyline

02

Banking, Financial Services & Insurance

Document intelligence, risk models, fraud detection and modern digital banking experiences.

Clinician in scrubs using a tablet in a hospital corridor

03

Healthcare & Life Sciences

Clinical documentation, patient engagement, imaging AI and compliant data platforms.

04Retail & E-commerce
05Logistics & Supply Chain
06Manufacturing & Industrial
07Energy & Utilities
08Telecom & Media
09Education & EdTech
10Real Estate & PropTech
11Travel & Hospitality
12Legal & Professional Services
13HR Tech & Staffing
Industries

Working in a regulated industry? Ask us how we handle evidence and audit trails.

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06 / Work

Problems we take on

Representative engagements that show how we frame a problem before we build.

Engineering team examining silicon wafers in a lab

01 / Semiconductors & Hi-Tech

Catching wafer defects earlier with computer vision

A visual inspection pipeline that prioritizes defect patterns and gives yield engineers the process context behind every flag.

Read the engagement
Financial charts layered over a blue city skyline

02 / Banking, Financial Services & Insurance

Document intelligence for faster customer onboarding

A controlled document pipeline that extracts, checks and routes onboarding information with evidence for every review decision.

Read the engagement
Colleagues shaking hands after a successful interview

03 / HR Tech & Staffing

A recruiter copilot inside a staffing platform

Matching, screening and outreach assistance embedded in the workflows recruiters already use, with people controlling every decision.

Read the engagement
Work

07 / Insights

What our engineers are learning

View all insights
Engineers comparing notes in front of code on large screens

01 / Engineering Teams

How to choose a product engineering partner in India

The signals that separate a dependable product engineering partner in India from an interchangeable vendor, and the questions that surface them.

Read the note
Neural network model of glowing nodes and connections on black

02 / Agentic AI

Where AI agents actually earn their keep

A practical guide to choosing agent work, setting boundaries and earning trust before autonomy expands.

Read the note
Processor chip glowing amber on a circuit board

03 / Generative AI

RAG is not a demo: what production retrieval takes

Production retrieval depends on freshness, evaluation and clear refusals, not just embeddings and a prompt.

Read the note

08 / Engagement models

Ways to work with us

Pick the shape that fits the problem and the budget cycle you are working inside.

01

Dedicated teams

Capacity that feels in-house.

02

Delivery pods

One outcome, owned end to end.

03

Fixed-scope projects

A clear finish line.

04

Build-operate-transfer

Your own center in India.

Compare engagement models

Join the engineers doing this work: Careers.

09 / FAQ

Questions we hear often

We take responsibility for building and running software products: discovery, design, engineering, quality, cloud and ongoing operations. Arixent adds AI and data engineering to that core so the products we build can learn from the data they generate.

Both. Growth-stage companies use us to build and scale products quickly. Enterprises and global capability centers use us for AI, data and modernization programs that need senior engineers and predictable delivery.

We begin with a short discovery conversation, then propose a team, timeline and engagement model. Ramp-up depends on the skills you need, and we tell you exactly what to expect before you commit.

Our engineers are spread across three time zones, so there is overlap with your working hours and progress while you sleep. Every engagement has one delivery lead accountable for the whole cycle.

You own the code, data and models produced on your engagement. We work inside your environments when required, follow secure development practices, restrict access to what each person needs and sign the confidentiality and IP terms your legal team asks for.

Yes. AI integration is one of our most common engagements: adding LLM features, agents, recommendations or predictions to an existing product without disrupting what already works.

Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.

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