AI writes a great deal of our code. None of it ships unread.
GegoSoft builds SaaS platforms and extends six products we run ourselves. All 18 engineers use Claude Code as daily tooling. What that buys you is not a discount on judgment — it is engineers spending their week on your data model instead of on boilerplate.
- Company
- GegoSoft Technologies (OPC) Pvt. Ltd., Madurai, Tamil Nadu, India. Founded 2017.
- Two services
- Enhancement of six products already in production, and new multi-tenant SaaS builds.
- AI tooling
- Claude Code, used daily by all 18 engineers as standard tooling rather than a trial.
- What AI does
- Scaffolding, test generation, documentation, static-analysis triage, refactoring, first-pass review.
- What AI does not do
- Approve merges, choose architecture, or deploy to production. A named engineer does each of those.
- Stack and reach
- Laravel, NestJS, Next.js, React, MongoDB, Flutter, Kotlin. Clients in India, the USA, Australia, Europe and Dubai.
The useful question is not how fast, it is who signs off
Every firm selling AI-assisted development is selling speed. Speed is easy to claim and hard to check. The thing worth asking a supplier is narrower: which decisions has AI been allowed to make, and whose name is on the merge when one of them turns out to be wrong.
Here is where the line sits at GegoSoft. It is the same on every project, and you are welcome to test it against any engineer you speak to.
Handled with AI
- First version of controllers, migrations, DTOs and API scaffolding, once the schema is agreed
- Unit and integration tests written alongside the feature, including the cases people skip when tired
- API documentation and inline comments kept in step with the code as it changes
- Triage of static-analysis and dependency-scanner output, so engineers see findings rather than noise
- Mechanical refactors across a codebase — renames, extractions, framework version upgrades
- A first read of every diff, flagging what looks wrong before a person opens it
Handled by a person
- The data model, and the architecture that follows from it
- What the product should do, and what it should refuse to do
- Reading every diff and approving every merge, under their own name
- Anything touching money, permissions or personal data, which gets a second reviewer
- Conversations with your team about scope, trade-offs and bad news
- The production deployment
We draw the line there for one reason. Our measure of a good build is whether it is still worth maintaining in five years. AI is very good at producing working code quickly. It has no stake in whether that code will make sense to the engineer who inherits it. That judgment is the part you are paying for.
Extending software that already runs
Six of our products are in production with live users. If one is close to your requirement, starting there puts you past the problems a new build has to discover for itself. The data model has already been argued about. The edge cases were found by users rather than guessed at.
Features on top of a working base
Payment gateways, reporting, additional API surface, mobile clients, integrations. The new work has somewhere to stand.
Your rules, your branding
Multi-tenancy, custom workflows, role hierarchies and interface changes, without forking away from the maintained line.
Load the original design did not expect
More users, heavier transaction volume, and refactoring where the first design has stopped fitting the traffic.
Connections to what you already run
Your ERP, CRM or accounting system, including the sync logic and the reconciliation for when a sync fails.
Access and data protection
OAuth 2.0, SAML, two-factor, encryption at rest and in transit, audit logging, and the data-protection controls your jurisdiction requires.
Getting off a legacy system
Database engine upgrades, migration of live data, and bringing old code into patterns a current engineer can read.
Building a new SaaS product
Every SaaS product needs the same foundation: tenancy, authentication, roles, billing, an admin surface, audit trails. We have built that eight times over and keep it as internal libraries. New projects start from there, so the early weeks go into your domain rather than into plumbing that has no competitive value.
Stack
Already solved, not rebuilt per project
- Multi-tenant data isolation
- Authentication and role-based authorisation, including SAML for enterprise buyers
- Subscription and payment handling on Stripe and Razorpay, including the failure paths
- Admin panels and CRUD scaffolding
- Monitoring, structured logging and error reporting from the first deploy
How a build runs
Requirements and data model
Features, roles and the model underneath them. This is the decision everything later is stuck with, so it is made slowly and written down.
Tenancy and access
Isolation and authentication assembled from the libraries, then reviewed specifically against your model rather than accepted as-is.
The features you exist for
Wireframes reviewed with your PM before API contracts are fixed. Mobile follows once the web behaviour has settled.
Billing
Gateway, subscription logic and invoicing — plus declined cards, retries, downgrades and refunds, which is where most billing implementations actually fail.
Testing and hardening
Coverage, dependency scanning and a security pass. Findings are triaged by an engineer, never auto-closed.
Launch and operate
Production deploy with monitoring already in place, then iteration against what real usage reveals.
Which of the two fits
If an existing product covers most of the requirement, extend it. If it does not, building new is cheaper than bending something that was designed for a different problem. We will tell you which one we think it is before you commit to either.
| Aspect | Product enhancement | New SaaS build |
|---|---|---|
| Fits when | One of the six products already does most of what you need | The requirement has no close match in the suite |
| Starting point | A codebase with live users and known behaviour | Internal libraries plus a domain model built for you |
| Architecture | Established; changes are incremental and constrained by it | Designed for this product, with no inherited constraints |
| Main risk | Regression in behaviour existing users depend on | Requirements shifting before the data model settles |
| Ownership | Your customisations; the core product line stays ours | Yours, including the code we write on top of the libraries |
| Typical team | 3–5 engineers | 5–8 engineers |
When we are not the right firm
Saying this early is cheaper for both of us than discovering it in month three.
You want AI instead of engineers
If the appeal is a team of two shipping what a team of eight used to, we are the wrong call. AI changes what our engineers spend the day on. It has not reduced how many of them read the code.
The brief is MLM, HYIP or speculative investment
We no longer accept this work, whatever the framing. Earlier blockchain and fintech engagements are historical engineering work and are described that way.
You need someone to decide what to build
We will challenge a specification and say when we think it is wrong. We cannot own the product decisions on your behalf.
You want a fixed price on an undefined scope
We will quote a fixed price against a defined scope. Against a vague one, any number we give you is a guess dressed as a commitment.
Sectors we have shipped into
Domains with production systems behind them, not areas we would be willing to explore.
Questions buyers ask us about this
Does AI-assisted development mean lower code quality?
It depends entirely on what the AI is allowed to decide. At GegoSoft, AI generates code and a human approves it — the review standard is the same one applied to code an engineer typed by hand, and the reviewer's name goes on the merge either way. What changes is that engineers spend less of the day on boilerplate and more of it reading and thinking. Quality problems in AI-assisted projects almost always trace back to review being skipped, not to the code being generated.
Who is accountable if AI-generated code causes a production failure?
GegoSoft is, in exactly the same way we would be for hand-written code. Every merge has a named engineer who approved it, and every production deployment is performed by a person. There is no category of defect where the answer is that the AI wrote it. Origin of a line of code has no bearing on who is responsible for it having shipped.
Will AI-assisted development make my project cheaper?
Some of it, in the parts where the work is mechanical — scaffolding, test writing, documentation, large refactors. It does not reduce the cost of the parts that need judgment: understanding your domain, choosing a data model, deciding what happens when a payment half-fails. If a supplier quotes you a flat percentage saving from using AI, ask which specific activities the saving comes out of. Any honest answer will be a list, not a number.
Can you work on our existing codebase rather than one of your products?
Yes. Several long-running client engagements work this way, with GegoSoft acting as the client's engineering team on their repository, their conventions and their release process. The AI tooling comes with us; the working practices are whatever the client already has.
What does GegoSoft deliberately not use AI for?
Three things: approving a merge, choosing an architecture, and deploying to production. Beyond those, anything touching money movement, permission boundaries or personal data gets a second human reviewer regardless of how the code was produced.
How is client source code handled by these tools?
[NEEDS YOUR CONFIRMATION — this should state the plan tier you are on, the data-retention and training settings that apply, whether any client contracts require a specific arrangement, and what happens for clients who ask for AI tooling to be excluded entirely. Do not publish a claim here that procurement cannot verify.]
Do you work with clients outside India?
Yes. GegoSoft is based in Madurai, Tamil Nadu, and works with clients in India, the USA, Australia, Europe and Dubai, including engagements running across several years. The open-source products, GegoK12 and ChurchCMS among them, are used by institutions internationally.
Tell us what you are building
Send the problem, the constraints and the deadline. We will tell you whether extending an existing product or building new is the better route — and say so plainly if the answer is neither.
GegoSoft Technologies (OPC) Pvt. Ltd. — Madurai, Tamil Nadu, India. Established 2017.