AI product discovery
One workflow, one success metric and a written check on whether your data is ready. If an off-the-shelf tool already solves it, we will say so.
From prototype to product · Issue #12
AI App Development Company in India
We take an AI idea from a promising demo to an app people actually use: retrieval over your own data, a real interface, measured accuracy, cost control and a launch plan. Prototypes start at USD 3,000.
We usually reply within one business day. No obligation, no sales sequence.
At a glance
- Projects from
- USD 3,000
- Typical timeline
- 3–10 weeks
- Based in
- Pune, India
Fixed scopeWeekly demosYou own the code
SINCE
2021
2021
- Weeks to first release
- 3–10Weeks to first release
- Prototype from
- USD 3kPrototype from
- AI products live
- 3AI products live
- Code & data ownership
- 100%Code & data ownership
The honest version
Why teams hire us for AI apps
Most AI apps fail in the gap between the demo and the product. The model answers a question beautifully once, then someone asks about a document it has never seen, grants it a permission it should not have, or discovers a monthly bill nobody budgeted for. Our work is the engineering that closes that gap.
We start with one workflow and a small set of real documents or records. We build a working prototype, measure whether it is accurate enough to trust, and only then invest in accounts, billing, admin, mobile clients and the operational work a launch requires.
Because we also build conventional software, the AI is not bolted onto a slide deck. The app around it — sign-in, roles, data model, payments, dashboards, notifications — is built to the same standard as the rest of the product. That is why the prototypes survive contact with real users.
What we deliver
The work, specifically
Every engagement is scoped to your situation, but this is the shape most of them take.
Working prototype
A usable app over your documents or records in three to five weeks — enough to judge quality with real users, not a video.
App & product engineering
Web and mobile clients, authentication, roles, billing, admin and notifications around the AI feature, not as an afterthought.
RAG & model integration
Retrieval, citations, prompt design and a thin provider layer, so swapping models is a decision rather than a rewrite.
Evaluation & guardrails
A test set from your real data, refusal behaviour, confidence thresholds, logging and per-workflow cost caps.
Launch & monitoring
Store submission or production deploy, usage dashboards, drift checks and a documented handover to your team.
The difference
Why not just the cheapest quote?
Because the cheapest quote is usually an estimate of the easy version. These are the things we do that make the second year cheaper than the first.
Proof before production
A tight prototype answers the only question that matters before a large budget: does this workflow work well enough to use? Stop there if the answer is no.
Price bands on the page
Prototype from USD 3,000, production assistants USD 8,000–25,000, full products scoped individually. You can budget before you enquire.
Model-agnostic by design
OpenAI, Anthropic, Gemini or open-weight models on your own infrastructure. Your app is not locked to one vendor's pricing or policy.
You leave with the keys
Repository, prompts, evaluation set, indexes and cloud accounts are yours at handover, with documentation a new team can follow.
Proof, not adjectives
Recent missions
Real products with real screenshots, the challenge, the stack and the outcome.
AI & Automation2025
AI RFP Proposal Platform
A retrieval-augmented proposal generator that drafts tailored RFP responses from a company's own past proposals and client requirements.
ReactFastAPIPythonMySQL
Read the issueAI & Mobile2026
Private AI Assistant
A free, open-source Android app that records meetings, transcribes them with on-device Whisper, summarises them with a local Llama model and answers questions about your meeting history — without sending a single byte to the cloud.
KotlinAndroidWhisperLlama 2.5
Read the issueQuestions, answered
Before you get in touch
The things clients ask us most often at this stage. If yours is not here, email and we will answer personally.
A focused prototype starts at USD 3,000. A production assistant with permissions, evaluation and integrations usually lands between USD 8,000 and USD 25,000. A full product with mobile clients, billing and admin is scoped individually. Our cost guide breaks down what moves the number.
A prototype takes three to five weeks. A production assistant typically takes six to ten weeks after the prototype. Full products take longer, mostly because of mobile release, billing and approval workflows.
Yes, in Flutter or React Native, with inference either in the cloud or on the device. On-device models suit privacy-sensitive meetings and offline use; cloud models suit heavy document work.
You need enough real material to test the workflow — contracts, tickets, manuals, listings. It does not have to be tidy on day one; preparing it is part of the prototype. If the data does not exist, the AI cannot answer reliably.
You do, on final payment. That includes the repository, prompts, retrieval indexes and the test set used to measure accuracy — the parts that make the system reproducible.
Yes. A retrieval layer, summariser, extraction step or drafting assistant can be added without replacing the rest of the stack. We start by checking whether the existing data model can support it.
Keep exploring
Related reading
Custom AI assistantsRetrieval, permissions and private deployment for assistants over your documents.Read moreAI RFP case studyA drafting workflow with human approval, built for a proposal team.Read moreOn-device AI case studyMeeting notes and answers running entirely on a phone, with no cloud call.Read more
Guides from the journal
What an AI app costs in India — Prototype, production and running costs, with the questions to ask every vendor.
RAG, explained without the maths — How retrieval grounding works and why it beats fine-tuning for changing facts.
AI agents vs chatbots — When a read-only assistant is enough and when tool use is worth the risk.
Seven workflows worth automating — Where small teams recover hours first, and where a human must stay in the loop.
AI document automation — How to pick the first document, design the pipeline and measure field accuracy.
AI for real-estate teams — Lead response, listings and document work, with the guardrails that matter.
Kaboom!
Ready to start your project?
Tell us what you want to build. We will reply within one business day with honest advice and a clear next step — no pressure, no jargon.