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.
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.
0wk
From the first scoping call to a system live in production.
0
Service lines, from chatbots to on-prem models you own.
0%
Senior engineers on every project. Never juniors on your time.
0h
Longest you should wait for a real human reply on a weekday.
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.
Chat Agents
Web chatbots, in-app assistants, and embedded widgets trained on your own data so they actually know what your business sells.
WhatsApp Agents
Customers reach you on WhatsApp at 11pm in Hindi and you reply instantly. Built on the WhatsApp Business Platform.
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.
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.
Email Responders
On-brand replies that close loops automatically. Routes incoming mail, drafts the response, and chases the open threads nobody got back to.
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.
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.
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.
Computer Vision
Object detection, OCR, quality inspection, and visual search, applied to anything from a CCTV feed to a production line camera.
Document AI
Pulls structured fields out of PDFs, invoices, forms, and contracts so your team stops typing them by hand.
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.
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.
Surface
Your product
The application your customers actually open. Web, mobile, embedded, and the dashboards your team uses to trust the system.
Intelligence
How it thinks
The reasoning layer. Multi-step agents, retrieval over your own data, careful orchestration, and evaluation that actually catches regressions.
Models
The brains
Frontier and open weights, picked per task. We stay model-agnostic so the system improves as the field does.
Infrastructure
The plumbing
Cloud architecture across AWS, GCP, and Azure. Vector storage, CI/CD, monitoring, and the runbooks for 3 AM.
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.
The process
How a project actually goes, week by week.
Week 1
Discover
A 30-minute call where we map out which corner of your business actually benefits from AI. No slides, free of charge.
Weeks 2 to 3
Design and build
Fixed price, with a working demo at the end of every week so you can see what is real and what is still on the to-do list.
Week 4
Ship and measure
It goes live with an evaluation suite attached, so the numbers that matter are visible from day one instead of vibes.
Ongoing
Iterate
Most clients stay on retainer for monthly model swaps, prompt tuning, and the new use cases that always show up after launch.
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.
FAQ
The questions we keep getting.
These are the questions that come up on almost every first call, so it is worth answering them once here. If yours is not in the list, write to support@aproksha.com and we will answer within a business day.
What does Aproksha do?
Aproksha is an AI implementation studio. We design, build, and deploy AI systems for businesses, including chatbots, voice agents, internal copilots, document AI, computer vision, and custom LLM-based products. We work with companies that need AI shipped to production, not just prototyped.
Where is Aproksha based?
Aproksha is headquartered in Amaravathi, Andhra Pradesh, India. We work with clients globally: North America, Europe, the Middle East, Southeast Asia, and across India.
What does an Aproksha engagement cost?
There are three ways clients usually work with us. A scoped project with a fixed price starts at around ₹2 lakh (about $2,500). A monthly retainer for ongoing builds starts at ₹1 lakh. If you want a senior engineer embedded directly into your team, that runs from ₹5 lakh a month. The final number depends on how much model usage we expect, how many systems we have to integrate with, and how regulated your space is.
How long does an AI project take with Aproksha?
A typical scoped project ships a working prototype in week two and a production-ready system in 6 to 10 weeks. Larger or more regulated builds (healthcare, finance) run longer because of evaluation and compliance overhead. We deliver weekly, every week.
What AI models does Aproksha use?
We are model-agnostic. We work with frontier hosted APIs (Claude from Anthropic, GPT from OpenAI, Gemini from Google) and with open-source models (Llama, Mistral, Qwen, Gemma) when fine-tuning, on-premise deployment, or data sovereignty is required. Model selection follows the use case, not the vendor.
Can Aproksha work on-premise or air-gapped?
Yes. On-device, on-premise, and air-gapped deployments are a core capability, with open-source models tuned to your domain. This is the right answer for sensitive data, regulated industries (healthcare, defense, banking), and data-sovereignty requirements.
Is Aproksha GDPR, DPDP, and EU AI Act compliant?
We design AI systems with these frameworks in mind from day one. Compliance posture is documented in every Statement of Work. We align with India’s DPDP Act 2023, GDPR, the EU AI Act, NIST AI Risk Management Framework, and ISO/IEC 42001.
What industries does Aproksha serve?
Healthcare, financial services, retail and direct-to-consumer, real estate, education, manufacturing, legal, and logistics. Most production AI work is the same five or six engineering patterns applied to a specific domain. We know the patterns cold; the domain is the part where we rely on you to explain how things actually work inside your business.
Does Aproksha offer support after deployment?
Every production engagement includes evaluation, monitoring, and a written runbook. Post-launch, clients on a retainer receive continuous improvement, model upgrades, and observability dashboards. Project-only engagements include a defined warranty period.
How is Aproksha different from a traditional AI consultancy?
Every engagement produces working software you can run, not a deck recommending what you should build. Beyond that, the difference comes down to honesty: if a use case is beyond what AI can reliably do today, or if the AI version would only be marginally better than a regular piece of software, we will say so on the first call. Our Responsible AI page also lists the kinds of engagements we will not take on, which is unusual for a studio our size.
What is Aproksha Labs?
Aproksha Labs is the public product arm of the studio. Each Lab product (Vaani, ClinicAI, BhaiAI, MAK) is something we ship to real users and also keep around as a starting template that clients can adopt and white-label. The Labs side is where we get to take technical risks; the Studio side is where we apply what we have learned to a specific business.
How do I work with Aproksha?
Start with a 30-minute discovery call via the contact page. We respond within one business day. Most engagements move from first call to signed Statement of Work within two weeks.
Ready to put AI to work?
Thirty minutes on a call, no slides, and at the end we'll tell you honestly whether there is something worth building here. Sometimes the answer is no, and that is fine.