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Applied AI, from strategy to production

AI, built into your business.

Knowing AI matters is the easy part. Knowing what to build, when, and for which corner of your business is harder. That's the work Aproksha does. Strategy, engineering, deployment, and the long tail of keeping it running.

The promise

Most AI work dies in proof-of-concept. Ours doesn't.

We build AI that fits inside a real business. That means starting with your workflow instead of a demo, scoping for what actually moves a number, and shipping something your team can use on Monday morning. The fun parts of AI are easy. The unglamorous parts (evaluation, integration, the hundred small fixes after launch) are what we're quietly good at.

See it in action

A scoping call, in 30 seconds.

The conversation on the right is the kind of back-and-forth we have with new clients on a first call. It cycles through three different problems we have actually shipped. Watch a couple of rounds to get a feel for how we scope.

4 weeks

is the usual time from scope to a working v1

Multi-lang

native voice and chat in Telugu, Hindi, English

0 leaks

your data stays exactly where you tell it to

Aproksha agent / live

demo / loops

Script 1 of 3

What we build

The AI should live where the work already happens.

The building blocks we ship to production. Most engagements combine a handful of these into one system that fits the way your team already works.

01

AI Agents

Autonomous, tool-using agents that plan, act across your systems, and actually finish work, not chatbots that only talk. They ship with guardrails, evaluation, and human handoff where it matters.

02

Chat Agents

Web chatbots, in-app assistants, and embedded widgets trained on your own data so they actually know what your business sells.

03

WhatsApp Agents

Customers reach you on WhatsApp at 11pm in Hindi and you reply instantly. Built on the WhatsApp Business Platform.

04

Voice Agents

Phone agents that sound like a person, not a phone tree. Useful for inbound support, outbound sales, appointment booking, and the dozen calls a day nobody wants to make.

05

Video Avatars

On-screen presenters that explain a product or walk someone through onboarding. Useful when you want a face talking instead of a wall of text.

06

Email Responders

On-brand replies that close loops automatically. Routes incoming mail, drafts the response, and chases the open threads nobody got back to.

07

Internal Copilots

AI that sits inside your team’s workflow, searches across Notion and Slack and Drive, drafts docs, and takes the notes everyone forgets to write.

08

Workflow Automation

Agents that finish work instead of just chatting about it. Multi-step, tool-using, and tested against the kind of edge cases that break demos.

09

MCP & Integrations

We connect AI to the tools you already run, from CRMs and databases to Slack, calendars, and internal apps, using the Model Context Protocol (MCP) and native APIs, so your agents can securely read and act on your real data.

10

Video Intelligence

Watching video frame by frame the way a human would, but faster. Useful for action recognition, anomaly detection, and asking questions of a CCTV archive.

11

Computer Vision

Object detection, OCR, quality inspection, and visual search, applied to anything from a CCTV feed to a production line camera.

12

Document AI

Pulls structured fields out of PDFs, invoices, forms, and contracts so your team stops typing them by hand.

13

Local AI Systems

Runs on a server in your office or a device on the factory floor. The data never leaves the building, there is no per-token bill, and the latency is whatever your local hardware can manage.

14

Custom Models

Open-source bases like Llama, Mistral, and Qwen fine-tuned on your domain. You keep the weights, the evaluation suite, and the right to switch providers any time.

The full stack

A model on its own is not a product.

Plenty of vendors will sell you a fine-tuned model and call it a day. We build the rest of it too. One team across all four layers, which means exactly one phone number to call when something breaks.

04

Surface

Your product

The application your customers actually open. Web, mobile, embedded, and the dashboards your team uses to trust the system.

Web appSaaS platformMobile appMarketing site
03

Intelligence

How it thinks

The reasoning layer. Multi-step agents, retrieval over your own data, careful orchestration, and evaluation that actually catches regressions.

AgentsRAG pipelinesOrchestrationEvaluation
02

Models

The brains

Frontier and open weights, picked per task. We stay model-agnostic so the system improves as the field does.

GPTClaudeLlamaMistralQwenCustom-tuned
01

Infrastructure

The plumbing

Cloud architecture across AWS, GCP, and Azure. Vector storage, CI/CD, monitoring, and the runbooks for 3 AM.

AWSGCPAzureVector DBsCI/CDObservability

Top to bottom, one accountable team

Where we work

Industries we have actually shipped in.

Most of the AI work that ends up in production is not novel research. It is roughly half a dozen well-understood patterns applied carefully to a specific domain. The patterns we know cold. The domain is where we rely on you, which is why every engagement starts with us asking embarrassingly basic questions about your business.

Healthcare

Clinical intake, follow-ups, scribing, document automation. HIPAA-aware and DPDP-aware.

Financial services

KYC, fraud, customer support, compliance assistants. Audit-trail-first by design.

Retail & D2C

Conversational commerce, recommendation, support deflection, post-purchase agents.

Real estate

Lead qualification, site bookings, document processing, multilingual buyer agents.

Education

Tutors that adapt, admissions agents, content generation, evaluation at scale.

Manufacturing

Vision QC on the line, predictive maintenance, document AI for compliance and safety.

Legal

Contract review, redlining, clause search, jurisdiction-specific assistants.

Logistics

Routing, document processing, customs paperwork, exception handling at scale.

Don't see yours? Tell us what you're building.

Why Aproksha

Numbers worth quoting back to us.

The studio runs on a few honest commitments. Hold us to these the next time we hop on a call.

Time to launch

0wk

From the first scoping call to a system live in production.

Breadth

0

Service lines, from chatbots to on-prem models you own.

Who builds it

0%

Senior engineers on every project. Never juniors on your time.

Reply window

0h

Longest you should wait for a real human reply on a weekday.

Aproksha Labs

We also build things just because we want them to exist.

Aproksha Labs is our R&D arm. It is a growing collection of AI products we ship publicly, partly to prove a technical idea and partly because the studio work would be more boring without it. Anything you see in Labs is yours to use, license, or have us adapt for your own business.

Who this is for

Aproksha is built for the people who have to make the AI actually work.

Founders shipping weekly.

If your release cadence is measured in days, an AI vendor who needs a three-month discovery phase is the wrong fit. We slot into your sprint instead of running parallel to it.

Operators who have seen enough demos.

By the time you reach us, you have probably been pitched twenty AI products that looked great in a video and fell apart in a pilot. We get it, and we will be honest about which parts of your problem actually need AI.

Teams that want code, not slides.

A consulting deck telling you what to build is worth roughly nothing. Someone who can sit down and write the code on Monday morning is what moves the work forward.

Last thing

Ready to put AI to work?

It starts with a free 30-minute discovery call. No preparation needed on your side. Here's exactly what it covers.

Honest scope

We will tell you which part of your problem actually looks like an AI project, and which part is a regular piece of software with a fancier name.

Just a conversation

Thirty minutes about your stack, your team, and the smallest thing that would prove this is worth doing.

A real answer

By the end of the call you will know whether to start a scoped project, sit tight for a quarter, or skip it entirely. Sometimes the answer is skip, and that is fine.

Engagements start atProjectFrom ₹2L / $2.5KRetainerFrom ₹1L per monthEmbedded engineerFrom ₹5L per month