AI development
Custom AI applications 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 businesses actually ask for
Almost nobody arrives asking for “an AI project”. They arrive with one of these, and the interesting part of the first call is deciding whether AI is even the right tool for it.
“The answer is somewhere in our documents and nobody can find it.”
Policies, contracts, product specs, past tickets — information that exists but is unsearchable in practice. Retrieval over your own content, with citations back to the source document so an answer can be checked rather than believed.
“Somebody types these PDFs into a system by hand every day.”
Invoices, purchase orders and forms turned into structured records. The engineering is not the extraction — it is the confidence threshold that sends the uncertain ones to a person instead of writing a wrong number into your database.
“We already have software and want AI inside it.”
Summarisation, classification, drafting or semantic search added to a product you already run, over its existing API and database. No migration, no rebuild, no second system for your team to learn.
“Everything that comes in has to be read and sorted first.”
Triage and routing on enquiries, tickets or applications, where the rule is genuinely a judgement call and the volume makes a person doing it every time absurd. This one often turns out to be automation rather than a build — we will say so.
“We built something with AI and it is confidently wrong.”
Usually an ungrounded model answering from general training instead of from your data, with no evaluation set and no escalation path. The fix is retrieval, a measured accuracy baseline, and a route for anything the system is unsure about.
“Legal want to know what happens to our data.”
A reasonable question that changes architecture, and much cheaper to design for than to retrofit. Raise it at scoping and it shapes where data sits, what is logged and which provider settings are used.
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.
AI development, automation, agents or a chatbot?
Four adjacent services with four different shapes, and picking the wrong one is expensive. This is how we separate them on the first call — the row you are on decides which page you should actually be reading.
| If this is the situation | What it usually is | Why |
|---|---|---|
| You want a feature inside software — search, extraction, summarising, drafting | AI development | The AI is part of a product. There is an application around it, and it is the thing on this page. |
| A fixed process already exists and the AI is one judgement step inside it | AI automation | The value is in removing repeated manual work, not in building new software. Usually cheaper and faster. |
| The next action depends on what the system finds during the previous one | AI agent development | Multi-step work against real systems, which needs approval gates and a human it can hand back to. |
| Customers or staff will talk to it in a chat window | AI chatbot development | A conversational front end over the same retrieval engineering, published as a packaged product. |
| The hard part is the dashboards, roles and workflow, with AI as a small piece | Custom software development | Most of the budget belongs to the application. Add the AI feature once the system it lives in exists. |
| You are selling the AI product to many customers on subscription | SaaS development | Multi-tenancy, billing and onboarding sit on top of the AI, and they are most of the work. |
| Two systems need to exchange data and no judgement is involved | API integration — no AI needed | If the rule can be written down, a rule is cheaper, faster and correct every time. We will tell you that. |
Most real projects are a bit of more than one of these. The point of the first call is to name the primary one, because that is what decides the architecture and the number.
How a production AI application fits together
The model is one box in this diagram, and usually not the one that decides whether the project works. These are the layers that go into a build, in the order a request passes through them.
The user, in your own product
A screen inside software your team or your customers already use — not a separate AI tool nobody remembers to open.
Application and business logic
What this user is allowed to ask, which data they are allowed to see, and what the system is permitted to do with the answer.
Retrieval over your data
The relevant passages, records or documents fetched first, via embeddings and vector search, so the model has something real to answer from.
The model call
A commercial LLM API, given the retrieved material, the task and a constrained output shape. Swappable: the application does not depend on one provider.
Validation and guardrails
Is the output the right shape, does it cite something that exists, is it within policy, is the confidence above the threshold this task requires?
Human escalation
Anything uncertain, unsafe or touching money or a customer commitment goes to a person with the context attached, instead of going out.
Write to the database or API
The structured result lands in your systems — a record created, a field filled, a ticket routed — rather than in a chat log nobody reads.
Logging and evaluation
What was asked, what was retrieved, what was answered and what a reviewer corrected. This is what makes tuning possible after launch instead of guesswork.
Roughly one box in eight is the model. That ratio is why “we added AI” and “we shipped an AI feature that a business relies on” cost such different amounts.
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 test it before it goes live
A demo proves a model can do something once. It says nothing about the hundredth case, which is the one that reaches a customer. So the accuracy question gets answered with a number, on your own data, before there is a product.
A real evaluation set
Cases drawn from your own documents and records, with the expected answer written down by someone in your business who knows what right looks like.
A measured baseline
Accuracy on that set, reported before the build commits. If it does not clear the bar the task needs, you find that out in week one rather than at launch.
The failure cases, on purpose
Missing source material, ambiguous questions, adversarial phrasing and out-of-scope requests are tested deliberately — those are where systems embarrass people.
Confidence thresholds per task
The bar is not one number. Reading an invoice total and drafting an internal summary carry very different costs of being wrong, so they get different thresholds.
Regression checks on changes
Prompts, retrieval settings and model versions all change behaviour. The evaluation set is re-run so an improvement in one place is not a quiet break somewhere else.
Logging that makes tuning possible
What was asked, what was retrieved, what was answered and what a human corrected — so post-launch improvement is based on real usage rather than opinion.
We do not publish an accuracy percentage here, because a number from someone else’s project tells you nothing about yours. You get one measured on your data, in writing, before the build is committed.
Where your data goes, and what happens when the AI is wrong
Two questions that decide whether an AI feature is allowed to exist inside a real business. Both are answered at scoping, because both change the architecture.
Commercial API tiers, training disabled
We use provider tiers where submitted content is not used to train the model, and the setting is confirmed rather than assumed. Provider terms change; it gets re-checked at scoping.
Accounts and infrastructure in your name
Model provider accounts, hosting, database and keys are held by you. The build does not stop working if you stop working with us, and there is no platform fee to us.
Access control on retrieval
Retrieval respects who is asking. A user cannot get an answer assembled from a document they would not be allowed to open directly.
Approval gates where it matters
Anything touching money, a customer commitment or an irreversible action waits for a person. That gate is designed with the feature, not bolted on after an incident.
An audit trail
Inputs, retrieved sources, outputs and human overrides are logged, so “why did it say that” has an answer months later.
Secrets and transport
Keys in environment configuration rather than in the codebase, HTTPS throughout, and retention decided deliberately instead of defaulting to keeping everything forever.
What we will not tell you: that your data can never be exposed, or that we are certified against a standard we have not been audited for. If you have a specific compliance obligation, name it at scoping — it is an architecture input, and it is far cheaper to design for than to retrofit.
Not sure which of these your problem is?
That is the call. Thirty minutes on what you are trying to fix, and a straight answer on whether it needs AI, automation, or a rule you could have written in an afternoon.
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”. What moves the number: how many features, how much and how messy the data is, whether retrieval is needed, how many systems it integrates with, how strict the accuracy bar is, and how much of the surrounding application has to be built. Ongoing model usage is separate, estimated in the proposal and billed to your own provider accounts. Where the work fits one of the productised builds, the price is already published — those are not the price of a custom build:
Products we have built
Platform and product work from the same team. These are shown as engineering proof, not as AI case studies — they are the kind of system an AI feature has to live inside, and we would rather label that accurately than imply an AI project we did not do.
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.
Sources & further reading
The primary documentation behind the claims on this page.
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.
How long does it take to build an AI application?
The evaluation stage — a real test set and a measured accuracy number on your data — is typically one to two weeks, and it happens before you commit to the build. A focused first version with one AI capability, its failure path and one integration is usually six to ten weeks after that. Anything with several capabilities, multiple data sources or strict approval workflows runs longer and is delivered in stages, so something is in real use well before the last piece lands.
Can you add AI to software we already use?
Usually yes, and it is often the cheaper route. If the system has an API or a database we can reach, features like summarisation, classification, drafting and semantic search can be added without a migration or a rebuild — your team keeps working in the software they already know. Where it is not possible is closed third-party products with no API; in that case the honest answer is a separate tool alongside it, or nothing.
What is the difference between an AI agent and an AI application?
An AI application answers or produces something and the surrounding software decides what happens next — the steps are known in advance. An AI agent chooses its own next step based on what it found in the previous one, and acts on real systems while doing it. Agents are more capable and considerably riskier, which is why they need approval gates and a human to hand back to. If your steps are known ahead of time, you do not need an agent, and we build those on a separate page.
How much does custom AI development cost?
It is quoted to scope after the first session, because the honest range for “an AI project” spans two orders of magnitude. The drivers are the number of features, the volume and cleanliness of the data, whether retrieval is needed, how many systems it integrates with, how strict the accuracy bar is, and how much of the surrounding application has to be built. The build fee is one-time; ongoing model and infrastructure usage depends on volume, is estimated in the proposal and is billed to your own provider accounts rather than marked up. Packaged chatbot, agent and automation prices are published, but those are not the price of a custom build.
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.