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Shipped, measured, still running

Four engagements written up honestly — including the parts that took longer than the pitch suggested.

Trusted by teams shipping to production

SaferNufeedAarna EnterprisesSuvetoAlpine HikesSandhya PublicationsKuchipudi Dilip
Case studies

Real-world delivery, measured

Every engagement below is in production and operated by the client's own team. The numbers are theirs, measured against their own baseline.

Safer, an AI-powered injury management platform

InsurTech

Safer

Migrated client case data into Safer, an AI-powered injury management platform for Australian teams, and built out its Rehab and Injury product areas.

2

product areas shipped: Rehab and Injury

  • Next.js
  • Data migration
  • Product engineering
Nufeed, an AI-personalised feed builder, showing its feed-design interface

Consumer AI

Nufeed

A personalised news feed that lets people design their own algorithm instead of inheriting one — taken from spec to a public test-flight in weeks.

<2s

per-topic feed rewrite latency

  • Next.js
  • LLM pipelines
  • Content
Suveto, the veterinary hospital network Veazie Veterinary Clinic belongs to

Healthcare & veterinary

Suveto

A room-by-room time-tracking system for Veazie Veterinary Clinic, one of Suveto’s network hospitals — every patient and staff room change logged, with wait times measured daily.

2.9 min

average front-lobby wait, tracked daily

  • Next.js
  • Operations analytics
  • Veterinary
Aarna Enterprises, a technology reseller, showing its product catalogue homepage

B2B commerce

Aarna Enterprises

A catalogue and quote-request site for a Chennai technology reseller, replacing a phone-only sales process with a structured way to browse, ask and buy.

4

product lines on one structured catalogue

  • Next.js
  • Catalogue
  • Lead generation
What clients say

What the teams say afterwards

Every quote below is from an engineering leader who still runs the system we built.

  • We were sceptical about the AI-first pitch — we had been burned by a vendor who shipped a generated codebase nobody could maintain. Azenvoc instrumented our DORA metrics in week one and then let the numbers argue. Lead time went from eleven days to under two.

    Lead time: 11 days → 1.8 days

    Priya RaghavanVP Engineering
  • The evaluation gate was the part that changed my mind. Generated code has to clear a scored threshold before a human even sees it. That is a higher bar than we held our own pull requests to.

    Daniel OkonkwoCTO
  • They refused to touch the model until the harness could measure it. At the time it felt slow. It is the only reason we could ship into a clinical setting at all.

    3.1× lower retrieval error

    Dr. Elena VasquezHead of Engineering
  • Speed was the headline; reliability was what we actually kept. Nine months on, change failure rate is still under one percent and our on-call rotation is quiet.

    Sarah LindqvistStaff Engineer
  • Our own engineers came out of it faster. Half of what the team learned that year came out of review threads on those pull requests.

    Marcus ChenChief Product Officer
  • Observability arrived with the first service instead of after the first incident. I have never had that on a vendor engagement before.

    Tomás FerreiraSRE Lead
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