Augurs · reading the signs
Augurs › Software

Custom software,
built at two speeds.

We build line-of-business systems to a mandate, not from a template. Where a client needs to move quickly, our engineers use AI models to compress the parts of the work that compress well — and hold the line on the parts that do not.

3 weeksTo working prototype
2Delivery tracks
2013Building since
CMMI 3ISO 27001 & 9001:2015
8–16Typical team size
How we deliver

Pick the clock. The standard does not move.

Track A

Accelerated build

For founders, product teams and internal sponsors

You need something real in front of users or a board while the opportunity is still open. We start with a working prototype rather than a document, demo weekly, and let scope move as you learn.

A working prototype in three weeks, on scope that would previously have taken us ten.

  • Working prototype before the full build begins
  • Weekly demos against real screens, not status decks
  • AI used heavily in scaffolding, UI, tests and docs
  • Same merge gate and code review as any other project
Track B

Assured build

For banks, missions, colleges and institutions

The system has to survive an audit as well as a launch. Same engineers and the same gates, with the documentation trail, traceability and sign-offs that statutory and concurrent audit expect.

  • Requirement traceability from spec to test case
  • Evidence pack for statutory and concurrent audit
  • AI used where it leaves the trail intact, and nowhere else
  • India-hosted deployment — AWS Mumbai or on-premise

Same engineers. Same review gate. What changes is the paperwork and the clock.

The delivery line

Where the model helps, and where a person signs.

Ten stages in a build. AI does real work in four of them and assists in two. The decisions that carry risk stay with the engineer whose name is on the merge.

AI-accelerated AI-assisted, human-owned Human-owned
What we use Claude Code OpenAI Codex Microsoft Copilot
01

Discovery & domain modelling

Your exceptions, not a generic model

Human-owned
02

Architecture & security design

Decided by people who sign for it

Human-owned
03

Scaffolding & environments

Set up in hours, not days

AI-accelerated
04

Screen build from approved designs

Design system in, working UI out

AI-accelerated
05

Business logic

Drafted with AI, owned by an engineer

AI-assisted · human-owned
06

Test generation & coverage

More cases, written faster

AI-accelerated
07

Code review & merge gate

A named senior engineer signs

Human-owned
08

Security & dependency scan

Tooling flags, a person decides

AI-assisted · human-owned
09

Integration & UAT

Against real systems and real users

Human-owned
10

Documentation & handover

Written as the code is written

AI-accelerated
Honestly

What actually gets faster — and what does not.

Genuinely faster with AI

  • Project scaffolding and environment setup
  • Boilerplate integrations and API clients
  • Screen build from an approved design system
  • Unit and integration test generation
  • Data migration and clean-up scripts
  • Technical documentation and handover notes
  • Clickable prototypes for early feedback

Not faster, and we do not pretend

  • Understanding your domain and its exceptions
  • Security architecture and threat modelling
  • Integration with a legacy core system
  • Compliance mapping and audit evidence
  • Performance tuning under real load
  • Getting sign-off from people who are busy
  • Any decision that needs someone accountable
Before you ask

The questions procurement asks first.

Who reviews the code?

Our development team reviews every merge before it lands, on both tracks. AI assists the engineer writing the code; it does not review its own work, and “the model wrote it” is not an answer we will ever give you.

Where does our code go?

To the model providers we work with, and nowhere else. We use Claude Code, OpenAI Codex and Microsoft Copilot on business plans whose terms exclude customer content from model training. If your rules do not permit that, we agree the boundary before the first line is written and work without external model access.

Is our data used to train anything?

No. Nothing you give us is used to train a model — not ours, and not our vendors’.

Who owns the result?

You do. Intellectual property in the delivered work is assigned to you, on the same terms whether or not AI was used in producing it.

Where is it hosted?

India, if that is what you need — AWS Mumbai or on your own hardware. The same options apply to both tracks.

How does this sit with your certifications?

AI-assisted work follows the same SDLC, the same review gates and the same evidence requirements as any other code we ship under CMMI Level 3 and ISO 27001.

Limits we hold

What we will not do, however fast you need it.

  • No unreviewed code in critical paths. Nothing AI-generated reaches authentication, payment or ledger logic without line-by-line human review.
  • No silent dependencies. A model does not get to add a library. A person checks its licence and provenance first.
  • No speed at the cost of the gate. If the review queue is the bottleneck, the date moves — the review does not shrink.
  • No hidden method. If you want to know where AI was used in your build, we will tell you, stage by stage.
Built this way

Four systems delivered on these tracks.

Accelerated · IndiaSAMPADA

Asset, inventory and licence platform for institutions, now in use at District Co-operative Bank, Lucknow. Eight lifecycle stages, seven modules and five AI models.

Assured · GermanySecuMetrix

A German cyber risk platform. Through the joint venture with Vertriebssoftware24 GmbH, Augurs adapted it for the Indian market and supports local implementation.

Accelerated · SaaSMika

B2B platform for insurance and cyber-risk advisors — .NET Core and React on SQL Server, with automation and AI services behind it.

Product buildNiko AI

Case notes on request.

What we build

Systems, not sites.

Line-of-business platforms, portals and workflow systems; web and mobile as one product family; applied AI inside the product where it earns its place; and the integration layers that connect any of it to what you already run.

See delivered systems →
Web & backend
C# · .NET · Laravel · Python · Node · Java
Front end
React · Angular · Vue · Three.js
Mobile
Swift · Kotlin · Flutter · React Native
Data
MS SQL · PostgreSQL · MySQL · Db2 · MongoDB
Applied AI
Forecasting · anomaly detection · document intelligence
Immersive
Unity 3D · AR/VR · HoloLens · Magic Leap
Next step

Send a spec. Get a build plan.

Tell us what you need and we will come back with scope, a timeline, and which stages we would accelerate — and which we would not.

Start a build → See a product we built