Is this a product or a service?

Fitted software. We study how you already work, then ship a working product into the tools you already pay for. You get a live system on your desk — not a chatbot to configure, and not a project dump to maintain.

Which AI models do you work with?

OpenAI, Anthropic's Claude, Google's Gemini, and Sarvam AI — plus open models where they make sense. We are in the Sarvam.ai Startup Program, so India-first voice and language APIs are available when the work needs them. We pick for the task; the design stays swappable as models improve.

How does retrieval (RAG) work?

We index your documentation, help content, product data, and past conversations, then retrieve the most relevant pieces at answer time so responses stay grounded and cite their sources.

Can it hand off to a human?

Yes. High-stakes actions wait for a person. Escalation can trigger on intent, sentiment, or an SLA timer, and the person receives the full transcript plus the relevant CRM or helpdesk record already loaded. Routine actions can run with an audit log.

Which industries do you actually build for?

Healthcare, restaurants and schools are the desks we go deepest on — hospital operations, floor-and-kitchen systems, and a full school OS. We also ship travel desks for any destination, e-commerce storefronts for any catalogue, SaaS in-product layers, real estate enquiry flows and controlled financial-service automation. Each one is a different fit, not a renamed chatbot.

What happens on the discovery call?

Thirty minutes, no obligation. We walk through how the work actually runs. You leave with a written plan — scope, approach, and trade-offs. We'll say if a platform already fits better. Apply is for exclusive custom-build slots; email is the fallback if a call isn't practical.

Limited custom builds this quarterSelected for the Sarvam.ai Startup Program

A fitted AI layer on the tools you already pay for.AfittedAIlayeronthetoolsyoualreadypayfor.

A research-led studio in India. We study how your company already works, then build AI into the CRM, helpdesk, WhatsApp, catalogue, and docs you already use.Aresearch-ledstudioinIndia.Westudyhowyourcompanyalreadyworks,thenbuildAIintotheCRM,helpdesk,WhatsApp,catalogue,anddocsyoualreadyuse.

Retrieval on your docs

Support with citations

Lead qualification

Ops with guardrails

Your channels

Fitted to your desk

Book a discovery call

See results

30-minute discovery call · Written plan · We’ll say if a platform fits better

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Weeks

Typical first build

One workflow, in production

Live

Finished product

On your desk, not a prototype

30 min

Discovery call

Written plan, no obligation

Your stack

No rip-and-replace

CRM, helpdesk, WhatsApp, docs you already pay for

India-first models

We're in the Sarvam.ai Startup Program

Selected into Sarvam AI's Startup Program. That means we can use India-first voice and language APIs when the work needs them — alongside OpenAI, Claude, and Gemini. We pick for the task. The design stays swappable.

India-first voice & language
When Hindi, Tamil, or voice is the real channel, we can call Sarvam’s APIs instead of stretching a generic English model.
Model-agnostic by design
OpenAI, Claude, Gemini, and Sarvam. We pick for the task, and the product stays swappable as models improve.
India and abroad
The program is an advantage for Indian language and voice — not a wall. We still ship fitted layers for international teams on the stack they already run.

Sarvam.ai API docs Our story →

Click To Automate

Sarvam.ai

Startup Program · 2026

Where it fits

Healthcare. Restaurants. Schools.

Three operating systems we fit to the floor you already run — then the same research-led approach across travel, retail, SaaS and the rest.

See all use cases →

Featured

Healthcare & Hospitals

Digitize the hospital. Keep clinical judgement with the people who hold it.

An end-to-end hospital layer for clinics, multi-speciality hospitals and healthcare networks — registration through pharmacy, lab, billing and the AI dashboard — with a hard stop on anything that is actually medicine.

Open this use case

Featured

Restaurants

One system for the floor, the kitchen, and the till — not three WhatsApp groups and a KOT pad.

Reservations, live table state, kitchen display, inventory, staff roster and GST billing on one layer. Guests stop waiting because the ticket got lost. You stop discovering wastage at month-end.

Open this use case

Featured

School Management

One operating system for the entire school — not another Excel file per desk.

Academics, administration, fees, communication and operations on one platform. Teachers get hours back. Parents stop calling the office for things the portal already knows. Accounts collects without chasing registers.

Open this use case

Travel Retail SaaS Real estate Online courses Finance

Your version of this is probably a little different. We'll map it on the discovery call.

Book a discovery call

Leadership wants AI. The stack is already paid for.

CRM, helpdesk, WhatsApp, catalogue, internal docs — the company already runs on them. What's on offer is a bolt-on chatbot that doesn't know that stack, those documents, or when a person should take over.

The overnight thread goes nowhere

A real question lands after hours. The widget matches a keyword, sends a canned line, and loops. The person leaves — not because the team was busy, because nothing in the tools they already pay for was actually used.

There's a way to get it fitted

Start from how the company already works. Ground answers in your documents, write into the CRM, and hand off with context. High-stakes actions wait for a person. Routine ones run with an audit log.

Customer · 11:42 PM

My order says delivered but nothing showed up. Order #48291.

Widget

Thanks for reaching out! Have you checked your spam folder?

Customer

It's a physical package. It was marked delivered three hours ago.

Widget

Please rephrase your question so I can look up an FAQ.

Customer left the chat.

What if the next step wasn't another generic chatbot?

CHATBOTFITTED LAYER

A researched layer — grounded in your documents, wired into the tools you already pay for, and built to wait for a person when the action is high-stakes.

A bolt-on chatbot is not a fitted layer.

Scroll to watch keyword matching give way to retrieval, citations, and a clean hand-off into the tools you already run.

GenericFitted

Keyword match

Looks for an FAQ hit

Understands intent

Reads the full question against how your team actually works

Scripted reply

Repeats a canned line

Retrieves knowledge

Grounds the answer in your docs, tickets, and catalogue

Dead end

“Please rephrase” loops

Responds with a source

Answers in your tone, cites the document, logs the turn

Ticket dumped

Customer waits overnight

Hands off cleanly

A person gets the transcript, the CRM record, and nothing to re-ask

What we build

Typical builds, fitted to the workflow

We don't ship a generic chatbot. After research, these are the layers we actually build into the stack you already run.

Retrieval over your knowledge

Docs, help centre, PDFs, past tickets, product data — indexed and retrieved at answer time so responses stay grounded, current, and cited.

Support with citations and hand-off

Routine questions resolve in-thread, in your tone and policy. Anything the corpus cannot support goes to a person with the transcript attached.

Lead qualification into the CRM

Qualify inbound conversations, capture the fields sales actually uses, and write the record into the CRM you already pay for — not a side spreadsheet.

Internal-ops agents with guardrails

Draft, summarise, look things up across systems. Routine actions can run with an audit log. High-stakes actions wait for a person.

The right model for the job

OpenAI, Claude, Gemini, or Sarvam — picked for the task. We keep the design swappable as models improve.

Human hand-off when it matters

Intent, sentiment, or an SLA timer can open the thread for the right person — with the CRM or helpdesk record already loaded.

Not sure which of these you need? That's the discovery call.

Book a discovery call

Four steps, start to a live product

Research how you already work

We sit with the people who do the work, map the CRM, helpdesk, WhatsApp, and docs, and pin down where AI belongs — and where a person must stay in the loop.

Design the approach

Retrieval, model choice, guardrails, routing, hand-off. You get a written plan with scope, approach, and trade-offs — no obligation to continue.

Build into your stack

We wire the layer into the tools you already pay for and test it against real traffic, not a happy-path demo.

Ship it, then stay on it

Watch the transcripts, tune retrieval and prompts, expand only after it has earned trust. The product stays live — we don’t disappear after a demo.

A product on your desk.

Fitted to how you already work, shipped live, and kept in production. You run the business. We stay on the system.

Capabilities

What has to hold up once it is in production

Knowledge & RAG

Chunking, embeddings, re-ranking and evaluation tuned to your content — so answers cite your documents instead of sounding plausible.

Channels you already use

Website, WhatsApp, Instagram, email, Slack, in-app. The same reasoning, wired into the front doors you already pay for — not a new one to maintain.

Hand-off

High-stakes actions wait for a person. Escalation carries the transcript, detected intent, and the CRM or helpdesk record already open.

In the inbox you already have

AI and human threads stay in the helpdesk or CRM the team already lives in — notes, assignment, and an audit log. No new product to log into.

Evaluation & audit log

Answer quality against real transcripts, not a demo set. Every routine action leaves a trail. Measurement is how we tune — not the product we sell.

Control & governance

The product can run in your region, with retention rules, role-based access, and an off switch you control. Your data stays yours.

Same night. Different ending.

The same request comes in after hours. This time the layer can read the order, cite the carrier note, and explain the path — without inventing a policy.

Routine now. High-stakes wait for a person.

Your team doesn't wake up to a blank angry thread. The explanation already happened. A reship or refund is queued with the record loaded — not fired unsupervised.

The person on the other side got an answer

Not a link to a policy page. A cited explanation, a next step, and a clean hand-off if they still want a human.

Customer · 11:42 PM

My order says delivered but nothing showed up. Order #48291.

Fitted layer

I pulled #48291 — the carrier marked it delivered to the lobby at 4:18 PM. I can queue a reship or a refund for your team; I won't fire either without a person.

Customer

Reship, please. And can someone confirm in the morning?

Fitted layer

Queued. I've attached the carrier note and opened this in your helpdesk with the record loaded — nothing to re-ask.

Cited · queued for human · audit logged

Results

One fitted workflow. In production.

All build narratives →

B2B SaaS · Customer support

First-response dropped from hours to minutes — without a chatbot that made things worse

A product-led SaaS team was drowning in ‘how do I…’ tickets. We built a retrieval layer over their docs and past tickets, with a clean hand-off when the answer wasn’t in the corpus.

“We didn’t want another bot. We wanted answers that hold up — and a hand-off that doesn’t make the customer repeat themselves.”
Head of Support

Read the narrative Book a discovery call

Minutes

First response

was hours on after-hours tickets

Weeks

First build

one workflow, one channel

Their cloud

Where it ran

as the engagement required

FAQ

Questions we hear a lot

Tell us how the work actually runs. Leave with a written plan.

Every engagement starts with a 30-minute discovery call, not a quote. We dig into where a fitted AI layer belongs — and where a person should stay in the loop.

Book a discovery call

Or send a message

A 30-minute discovery call — bring how the work actually runs, we bring the questions
You leave with a written plan: scope, approach, and trade-offs
No obligation, no pressure, no generic pitch deck

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A fitted AI layer on the tools you already pay for.AfittedAIlayeronthetoolsyoualreadypayfor.

We're in the Sarvam.ai Startup Program