Custom AI SaaS Build
Your AI product idea taken from concept through architecture, build, billing and launch — as a real business, not a demo.
Result: A product you can charge for on day one.
10 · AI Web Apps & SaaS
Ship the product, not the prototype
There is a large gap between an AI demo that works once and a product that handles a thousand users, bills them, stays up and does not leak data between accounts. We build the second kind.
- Starting at
- ₹6,00,000
- Typical timeline
- 10–20 weeks
- Services in this line
- 7
The problem
Most AI prototypes die in the gap between 'it worked in the notebook' and 'it survives real users'. Auth, billing, rate limits, evaluation and cost control are the actual work.
What we build
7 services in AI Web Apps & SaaS
Each one is a page of its own — pick the closest match, or tell us your problem and we'll point you at the right one.
AI Search for Your Website
Search that understands meaning, so 'something for a leaking tap' finds the right product even with no keyword match.
Result: People find things, which is most of what conversion is.
Recommendation Engine
Personalised product, content or service recommendations based on behaviour, similarity and context.
Result: Higher basket size and time on site, measurably.
Natural-Language Data Dashboard
Ask your business data questions in plain English — 'which product lost money last quarter' — and get the chart and the answer.
Result: Analytics without waiting on anyone.
AI Form Filling & Validation
Pre-fills long forms from uploaded documents and validates entries in real time against your rules.
Result: Form abandonment falls sharply.
Resume Screening & ATS
Ranks applicants against the actual job requirement, summarises each candidate and drafts interview questions.
Result: Shortlisting in an hour instead of a week.
Contract Review Tool
Reads contracts, extracts obligations and dates, flags unusual or risky clauses, and compares against your standard template.
Result: First-pass review in minutes with nothing missed.
What you get
Included in every engagement
- Production architecture with multi-tenancy and proper data isolation
- Auth, roles, billing and subscription handling
- Cost controls and rate limiting so a single user cannot bankrupt you
- Evaluation harness so you know when quality regresses
- Admin dashboard and usage analytics
- Deployment, monitoring and handover documentation
How it's built
Typical stack
- Next.js
- TypeScript
- PostgreSQL + pgvector
- LangGraph / agent frameworks
- Stripe / Razorpay
- Vercel / AWS
We pick tools per project rather than forcing every client onto the same stack. If something in your business already works, we build around it instead of replacing it.
In depth
AI Web Apps & SaaS: what you should know before you commit
AI software has a cost of goods that ordinary software does not. One more customer on a conventional SaaS costs you nothing; one more heavy customer on an AI product costs you money every day they use it. Everything in this category follows from that.
The margin problem nobody mentions in the pitch deck
Traditional SaaS runs high gross margins because marginal cost is near zero, which is why pricing can be simple and unlimited plans are safe.
AI products do not work that way. A customer on a flat ₹2,000 plan can cost ₹6,000 a month in model usage, and you find out when the invoice arrives. We have been brought into businesses where this ran unnoticed for two quarters.
So per-tenant metering is not a reporting feature to add later. It is a load-bearing part of the architecture, and retrofitting it into a product with live customers means a migration plus a period where the numbers cannot be trusted.
The seven builds here
| Build | The problem it addresses | What decides whether it works |
|---|---|---|
| Custom AI SaaS | Your product idea, built as a business | Unit economics per customer |
| Semantic site search | Searches returning nothing | Quality of your product data |
| Recommendation engine | Generic 'also bought' widgets | Incrementality, not click rate |
| Natural-language dashboard | Questions queued behind one analyst | The semantic layer definitions |
| AI form filling & validation | Long forms people abandon | User confirms every extracted field |
| Resume screening & ATS | Keyword filters rejecting good people | Active bias control and testing |
| Contract review tool | Agreements nobody has fully read | Comparison against your own template |
Where the effort actually goes
Clients consistently expect the AI capability to be most of the build. In practice it is the smaller part.
Multi-tenancy and data isolation, authentication and team accounts, usage metering, billing and dunning, admin tooling for your support team, observability and per-tenant cost tracking — these are what stand between a working demo and something you can charge for, and together they outweigh the model work.
A demo is roughly twenty per cent of a sellable product. Being clear about that at quoting stage is the difference between a project that ships and one that runs out of budget at the interesting bit.
Controlling what each customer costs you
Once metering exists, the levers become available and all of them matter more than they sound.
- Model routing by task. Cheap models for classification and simple generation; expensive ones only where the quality difference is real.
- Caching. Repeated and near-identical requests answered from cache. In several products this alone has cut spend substantially.
- Context discipline. Sending an entire document on every turn is the most common source of runaway cost and is usually avoidable.
- Hard limits per plan. A ceiling that degrades gracefully, rather than an invoice that surprises you.
- Outlier alerting. One tenant at ten times the median needs attention that week, not that quarter.
Your data quality is the ceiling
This runs through the whole category. Semantic search cannot reason about products described only by a title. A dashboard cannot answer correctly when "revenue" means three different things in three departments. A recommendation engine cannot find similarity in a catalogue with no attributes.
Which means a meaningful share of most engagements is data work — normalising a supplier feed, defining business terms once and centrally, enriching a catalogue, cleaning duplicate customer records.
It is the least exciting part of every proposal and the strongest predictor of whether the result is any good. Where a client's data is not ready, we say so, and occasionally we have recommended spending three months tidying it before commissioning anything.
Scope, cost and ownership
The smaller builds — search, recommendations, form processing — run four to fourteen weeks from ₹2,00,000 to ₹3,50,000. A full SaaS product is sixteen to twenty-eight weeks from ₹12,00,000.
Everything deploys on your own cloud accounts and your own domains. You hold the model provider keys, the billing relationship and the customer data.
We will build an MVP first where that makes sense — a focused version proving one workflow for one segment, at ₹5,00,000 to ₹8,00,000, answering whether anyone pays and what a customer costs to serve before the full commitment.
FAQ
Questions people actually ask
Do we own the code?
Yes. Full source ownership transfers to you on final payment, with the repository, documentation and deployment access handed over. We do not hold your product hostage.
How do you control AI running costs?
Model routing (cheap models for easy work), caching, prompt compression, per-user rate limits and a hard budget ceiling with alerting. We model unit economics before we build.
Can you take over an existing half-built AI project?
Often yes. We start with a paid technical audit so both sides know what is salvageable before anyone commits to a rebuild.
Other AI service lines
Next step
Let's work out what's worth building.
A 30-minute call. We'll tell you what we'd do, roughly what it costs, and whether it's worth doing at all.