AI development
AI built on your data, not a demo
Custom AI applications and AI features inside software you already run — grounded in your own documents and records, with the failure paths designed before the happy path.
A 30-minute call about the problem. If AI is the wrong tool for it, we will say so.
Quick answer
What does an AI development company actually build, and what does it cost to run?
Mana Studio builds software where a model does part of the work — custom AI applications, and AI features inside systems a business already runs. In practice that means grounding the model in the client's own documents and records, deciding what it is allowed to do unsupervised, designing the failure paths before the happy path, and integrating it with existing systems; the model itself is the smallest part of the job. Mana Studio does not train foundation models, and says so: builds run on established commercial model APIs, with embeddings and vector search over the client's own content where retrieval helps. Accuracy is measured on a real evaluation set before launch, and anything below a confidence threshold routes to a person rather than to the customer. The build fee is one-time and quoted to scope; ongoing model and infrastructure usage depends on volume, is estimated in the proposal, and is billed to the client's own provider accounts rather than marked up. Commercial API tiers are used with training disabled, so client data is not used to train anyone else's model. Mana Studio is based in Warangal, Telangana, India, works with clients in India, the United States, the United Kingdom and the UAE in English, Telugu and Hindi, and replies to enquiries within one business day.
Last reviewed
- Pricing
- Fixed price against a written scope, after a call
- Running cost
- Billed to your own provider accounts, not marked up
- Models
- Commercial APIs with training disabled — no foundation models trained
- Accuracy
- Measured on an evaluation set before launch
- You own
- Code, servers, domain and every key, in your name
- Based in
- Warangal, Telangana, India
Most AI projects that go wrong go wrong the same way: someone builds a chat box on top of a general-purpose model, it answers confidently from the internet instead of from the company's own information, and the business quietly stops using it. The useful version is narrower and less exciting — a model grounded in your documents, your records and your rules, with a clear boundary around what it is allowed to decide.
That is what we build. Retrieval over your own content so answers are traceable to a source. Extraction from the documents that currently get typed into a system by hand. AI steps inside a process where the judgement call is real but the volume makes a human doing it every time absurd. And an escalation path for everything the system is not confident about, because a wrong answer delivered confidently is worse than no answer at all.
We are a build team, not a research lab. We use the established model APIs rather than training foundation models, and we will tell you when a problem does not need AI — a rule, a query or a properly indexed search is often the correct and much cheaper answer.
What we build with AI
Retrieval over your own content (RAG)
Search and question-answering grounded in your documents, policies, product data or ticket history — with citations back to the source so an answer can be checked rather than trusted.
Document and data extraction
Invoices, purchase orders, forms and PDFs turned into structured records, with a confidence threshold that routes anything uncertain to a person instead of guessing.
AI features inside existing products
Summarisation, classification, drafting and semantic search added to software you already run, without a migration or a rebuild.
AI chatbots grounded in your knowledge base
Website and support bots that answer from your material and hand over to a human when they are out of scope. Published as packaged products for both markets.
AI inside automated workflows
The judgement step in a business process — triage, routing, qualification, tone-checking — sitting inside an automation rather than being its own product.
Evaluation and guardrails
A test set of real cases, measured accuracy before launch, logging of what the system actually answered, and approval gates on anything touching money or customers.
How it is built
Mainstream, well-supported technology — chosen so your next developer can pick the project up, not so we are the only ones who can maintain it.
Models
- Commercial LLM APIs
- Embeddings
- Vector search
Patterns
- RAG
- Structured extraction
- Classification
- Human-in-the-loop review
Application
- Next.js
- Node.js
- MongoDB
- REST APIs
Operations
- Evaluation sets
- Confidence thresholds
- Audit logging
- Failure alerts
How we work
How an AI project runs
Find the actual problem
A working session on where the time or the errors are. We come back with what is worth building, what is not, and what could be solved without AI at a fraction of the cost.
Prove it on real cases
A small evaluation set drawn from your own data, and a measured accuracy number before there is a product to ship. If it does not clear the bar, you find out in week one.
Build with the failure path first
Confidence thresholds, escalation to a person, and approval gates designed at the same time as the feature — not added after the first bad answer reaches a customer.
Launch, log and tune
Deployed with logging of what it was asked and what it answered, so tuning is based on real usage. Ongoing model costs are billed to your own accounts, never marked up.
What it costs
Custom AI work is quoted to scope after the first session — there is no honest package price for “an AI project”. Where the work fits one of the productised builds, the price is already published:
Products we have built
Platform work from the same team — the engineering an AI feature has to sit inside.
Algo Trading
AlgoEdge
Automated PNL reporting platform for Tradetron Creators — real-time MTM tracking, drawdown alerts, and Telegram summaries
Tradetron API · Telegram Bot
Job Portal
Sarkari Job Samachar
Comprehensive job portal for government job seekers with real-time updates and notifications
Next.js · Appwrite · SEO · Content Management
E-commerce
Rami Silks
Full-stack e-commerce platform for traditional Indian clothing with AWS integration and advanced analytics
Next.js · Node.js · MongoDB · AWS S3
AI Development by industry
The sectors we have shipped work in, and what changes about the build in each one.
Related services
AI Development — frequently asked questions
What does an AI development company actually do?
For us: build software where a model does part of the work. That means grounding it in your data, deciding what it is allowed to do on its own, handling the cases it gets wrong, and integrating it with the systems your business already runs on. The model itself is the smallest part of the job.
Do you train your own models?
No. We build on established commercial model APIs and, where it helps, embeddings and vector search over your own content. Training a foundation model is not the right answer for the problems businesses bring us, and claiming otherwise would be dishonest about what a small build team does.
What is RAG and do I need it?
Retrieval-augmented generation means the model answers from documents you supply rather than from its general training. You need it whenever the correct answer depends on your own information — pricing, policies, product details, past tickets. You do not need it for tasks like rewriting or classification where the content arrives with the request.
How do you stop it making things up?
Three things, in order of importance: ground it in retrieved source material so there is something to answer from; measure accuracy on a real evaluation set before launch; and route anything below a confidence threshold to a person rather than to the customer. Anything touching money or a customer commitment also gets an explicit approval gate.
What does it cost to run each month?
The build fee is one-time. The ongoing cost is model and infrastructure usage, which depends entirely on volume — we estimate it in the proposal and bill it to your own provider accounts rather than marking it up.
How is this different from AI automation?
AI automation is about removing repeated manual work from a process you already have. AI development is about building software where the AI is a feature of the product. They overlap, and most projects are honestly a bit of both — the call at the start sorts out which one you actually need.
Will my data be used to train someone else's model?
Not through us. We use commercial API tiers with training disabled, and the data stays in infrastructure held in your name. If you have a specific compliance requirement, raise it at scoping — it changes architecture decisions, and it is much cheaper to design for than to retrofit.
Discuss your AI project
A 30-minute call about the problem. If AI is the wrong tool for it, we will say so.