AI Assisted Software Development Company India
Every engineer at GegoSoft uses Claude Code as standard daily tooling. That changes what we can quote, not what we claim: scaffolding, test generation, static analysis triage, refactoring and documentation move from days to hours, so the estimate you receive reflects engineering judgement rather than typing time.
We work two ways — extending our six production products for your requirement, or building SaaS platforms from our own Laravel and NestJS libraries. Both routes start from code that already runs in production somewhere.
GegoSoft in brief
- Legal entity
- GegoSoft Technologies (OPC) Private Limited, Madurai, Tamil Nadu, India
- Founded
- 2017. Founder and CEO, Karthick Kumar A.J.
- Engineering team
- 18 engineers, all using Claude Code as standard daily tooling across every project
- Primary stack
- Laravel, NestJS, React, Next.js, MongoDB, Flutter, Kotlin
- Two service lines
- Product enhancement of six in-house platforms, and SaaS development from internal code libraries
- Core business areas
- Healthcare, education, financial services, enterprise collaboration, construction and ERP
- Engagement models
- Dedicated remote developers part-time or full-time, fixed-price project delivery, and co-funded product development
- Where AI stops
- AI does not approve merges, choose architecture, or deploy to production. Named engineers do all three.
Most agencies start from zero. We start from something that already runs.
A new build carries risk that has nothing to do with your requirement — schema decisions, auth, roles, billing, reporting, the parts every system needs and nobody wants to pay to discover twice. We have already paid for them, twice over, in six shipped products and eight years of SaaS work.
Route one
Product enhancement
Six of our platforms run in production today. If one is close to what you need, we extend and customise it for you rather than rebuilding the same foundations. You get the parts that already work on day one, and pay only for the difference between our product and your requirement.
- Existing roles, permissions and audit trails carried over intact
- Customisation scoped against a running system, not a specification
- Demo environments available before commercial terms are agreed
Route two
SaaS development
Where no product fits, we build from internal Laravel and NestJS libraries developed across eight years of multi-tenant work — tenancy, subscription billing, role hierarchies, notification pipelines. AI assistance compresses the scaffolding around those libraries, which is why an MVP arrives in weeks rather than quarters.
- Multi-tenant architecture decided by an engineer, not generated
- Test suites written alongside features, not retrofitted before launch
- You own the repository from the first commit
Claude Code is standard tooling here, not a pilot programme
All 18 engineers use it every day, on client work and on our own products. What follows is the specific mechanism — where AI enters the workflow, what it does, and what it is not permitted to touch.
01 · Plan
Architecture stays human
An engineer chooses the data model, the tenancy strategy and the integration boundaries. AI is used to enumerate edge cases and failure modes against that decision, not to make it.
02 · Build
Scaffolding and tests
Migrations, resource controllers, DTOs, factories and test suites are generated and then read line by line. The engineer's attention goes to business logic, which is where the defects that matter actually live.
03 · Review
Two passes, one gate
Every pull request gets an AI review pass for N+1 queries, unvalidated input, missing authorisation checks and dead code. A named engineer then reviews it again and is the only one who can approve.
04 · Ship
Documentation that exists
API docs, changelogs and handover notes are drafted during the sprint instead of the week before delivery. Most projects fail their second year on missing documentation, not missing features.
The boundary, stated plainly
AI does: scaffolding, test generation, static analysis triage, refactoring, documentation, and first-pass code review.
AI does not: approve merges, choose architecture, or deploy to production. Those three remain with a named engineer who is accountable for the outcome.
We publish this boundary because the alternative — a vendor implying AI does more than it does — is the thing that goes wrong eighteen months later, when nobody on either side can explain why a system was built the way it was.
Systems we have shipped, described honestly
Each of these is written up in full — the constraint, the decision, the trade-off we accepted. Where a client is under a confidentiality agreement, the client is anonymous and the engineering is not.
Healthcare · India
AI-First EMR
An electronic medical record built around clinical note capture rather than billing codes, with DPDP Act data-processor obligations designed in from the schema up.
Read the case studyFinancial services · USA
Microfinance banking platform
A multi-branch lending and collections system where the hard problem was not the ledger but reconciling field-agent activity recorded offline across intermittent connectivity.
Read the case studyEnterprise UX · Australia
Enterprise collaboration tool
Interface design for a managed service provider's collaboration suite, delivered before the API existed — screens and states agreed first, backend contract written against them.
Read the case studyEducation · Our own product
GegoK12 school ERP
Eight years of running a school management platform for institutions in more than twenty countries, and what multi-country academic calendars do to a data model.
Read the case studyAI product · Confidential client
AI photo delivery platform
Face-matched photo distribution at event scale, where the engineering constraint was inference cost per image rather than accuracy.
Read the case studyInternal tools · Confidential client
Spreadsheet replacement
Migrating an operation running on shared workbooks into a real application, including the part most rebuilds skip — matching the workflow people had actually invented around the spreadsheet.
Read the case studySix platforms we build, sell and support ourselves
Running our own products is what keeps the agency honest. We carry the maintenance burden of our own architectural decisions, which is a discipline you cannot get from client work alone.
GegoK12
School ERP
Admissions, attendance, examinations, fees and staff administration for K-12 institutions. In use by schools across more than twenty countries.
ChurchCMS
Church management
Membership records, small groups, giving and facility scheduling for congregations, including multi-campus structures.
BOQ Manager
Construction estimation
Bill of quantities, estimation and project cost management for contractors, tying rate analysis to live project spend.
ChemTracker
Chemical labelling
Chemical inventory and compliant label generation for laboratories and manufacturers handling regulated substances.
eTender
Tender management
Multi-company, multi-vendor tender publishing, bid submission and evaluation with a full audit trail.
Daybook
Small business accounts
Day-to-day income, expense and cash position tracking for small businesses that have outgrown a notebook but not yet reached full accounting software.
Also in development: Blood Bank Management Software — donor registry, inventory and cross-match tracking for hospitals in emerging markets.
DentalPro.cloud and iDoc
These two healthcare platforms are products of SmartDocx AI Systems Private Limited, a healthcare technology venture co-founded by GegoSoft's founder. They are not GegoSoft client work and not GegoSoft products. We list them because our engineers built them, and because the case studies are instructive.
What we are asked for most
Each of these has a page setting out how we approach the work, what it costs in effort, and when we would tell you to hire someone else.
SaaS development
Multi-tenant platforms from MVP to scale
Custom Laravel development
Our core framework since 2017
Web application development
Admin systems, dashboards, internal tools
Healthcare software
EMR, HMS, pharmacy, DPDP Act obligations
Mobile app development
Flutter and Kotlin, shipped to both stores
User experience design
Screens agreed before the API is written
TALL stack development
Tailwind, Alpine, Laravel, Livewire
WordPress development
Custom themes, plugins, headless builds
WooCommerce
Stores that outgrew off-the-shelf plugins
Responsive web design
Corporate and product marketing sites
Digital marketing
Search, content and AI answer-engine visibility
All services
The complete list
When to call us, and when not to
We turn down work every quarter. Saying so here saves both of us a discovery call.
We are a good fit when
- You want a long engagement with the same engineers, not a fixed-scope handover
- Your requirement is close to one of our six products and you would rather extend than rebuild
- You need a multi-tenant SaaS platform and want the tenancy model decided by someone who has maintained one for years
- Another developer has left you with a codebase you cannot deploy or extend
- You are building healthcare software in India and need DPDP Act obligations designed in rather than bolted on
- You want interface design finished and agreed before backend work starts
We are the wrong firm when
- The project is an MLM, HYIP, crypto exchange, ICO or speculative investment scheme. We do not accept this work on any terms.
- You need a team on site in your office. We work remotely from Madurai, with scheduled overlap hours.
- You want the lowest hourly rate available. There are cheaper firms and you will find them quickly.
- You need embedded firmware or medical device software. Our scope is application and integration software only.
- The requirement is a brochure site that a template would serve better. We will tell you so.
- You expect AI to remove the need for engineering review. That is not how we work and not what we sell.
Questions buyers actually ask
What does “AI assisted software development” mean in practice?
It means the mechanical portion of software engineering is automated and the judgement portion is not. At GegoSoft, AI generates scaffolding, test suites, migrations and documentation, triages static analysis output, and performs a first-pass code review. Engineers decide architecture, approve merges and deploy. The distinction matters commercially: it is why our estimates compress on well-understood work and do not compress on novel work.
Does AI-generated code end up in production?
Yes, after a human has read it. Treating AI output as trusted is the failure mode that produces unmaintainable systems, so we treat it as a draft from a fast but unaccountable colleague. Every line passes through an engineer who can explain it, and that engineer's name is on the merge. If a defect ships, a person is accountable — which is not true of a workflow where AI approves its own work.
Is AI-assisted development cheaper?
It is faster on the predictable eighty per cent and unchanged on the difficult twenty. A CRUD module, an admin panel or an integration against a documented API arrives materially sooner. A novel data model, a hard performance problem or an ambiguous requirement takes exactly as long as it always did, because the bottleneck there is thinking, not typing. Any firm claiming a uniform reduction across all work is describing a discount, not a method.
Who owns the code, and what happens to our source?
It depends on what we build, and we put it in writing before work starts. Built from scratch — a platform, a module, a standalone application — you own the copyright on final payment. Extending one of our products splits it: we keep the underlying product, you own the customisation and hold a perpetual licence to run it. The engagement document names the boundary between our existing libraries and your deliverable, because a contract that transfers “all code” without that carve-out is one neither side can honour.
Repositories sit on your infrastructure, or on ours and transfer to you at completion. At contract close you receive full source and documentation, we revoke our own access, and we keep no credentials to your systems. Our engineers use Claude Code under Anthropic's commercial terms, under which code sent to the tool is not used to train models.
Engagement and delivery models differ by project — a plugin, a UX engagement, a new module and a full build are not the same commercial arrangement. Talk to our team about the one that fits yours.
Can you take over a project another developer abandoned?
This is a large share of the work that reaches us, and we accept it after an audit rather than before. The audit establishes whether the existing codebase is worth continuing or cheaper to replace — a judgement that turns on test coverage, framework version and how far the schema has drifted from the domain. We will tell you if the honest answer is that the code should be discarded, including in cases where continuing would be more profitable for us.
Where is GegoSoft located, and how does that work across time zones?
We are in Madurai, Tamil Nadu, and have run long engagements with clients in Australia, the United States, Europe and Dubai since 2017. The arrangement that works is scheduled overlap rather than a promise of full availability: a fixed daily window when the team is reachable live, with everything else handled asynchronously in writing. Firms that promise to match your hours entirely are usually rotating staff to do it, which costs you continuity.
Tell us what you are building
Or what your last developer could not finish. We will tell you which of the two routes fits, roughly what it takes, and whether we are the right firm for it.