WhatsApp · AI agents · n8n · RPA · documents

Your business runs 24/7.
Your team doesn't.

We build AI employees that answer your WhatsApp, read your documents and run the back office — scoped to one process, quoted at a fixed price, delivered to your repository. Eleven years in enterprise IT, nine of them keeping other people's bots alive after the consultants left.

Fixed price before we start — one process, one date
Your repository, your cloud, your credentials
30 days of defect fixing included
Support is optional — never a dependency
agents — live
$agents --now-running
enquiry answered · 4s documents read · 11 of 11 written to CRM · validated low confidence → review queue

Start here

Most automation projects fail. You already suspected that.

Four numbers from independent research — not from us. If you have been pitched AI before and it went nowhere, this is why, and it is the reason we work the way we do.

95%

of generative-AI pilots produced no measurable return on the P&L.1

MIT Project NANDA, 2025
42%

of companies scrapped most of their AI initiatives in 2025 — up from 17% the year before.2

S&P Global, n=1,000+
30–50%

of initial RPA projects fail outright, on EY's own observation.3

EY, Get Ready For Robots
56%

of executives report high regret over their biggest tech purchase of the last two years.4

Gartner, n=1,120
Nine of our eleven years were spent on the unglamorous half of this industry: keeping bots alive after the consultants left. That experience is why we scope narrow, quote fixed, hand you the repository, and tell you which processes you should not automate at all.

60-second self-check

Tick every one that happens in your business.

Nothing is sent anywhere. This is the same list we work through on the audit call — most teams tick four or more.

0 Tick the ones you recognise. Every box is a process we have automated before.

What we build

Six services. Every one with a price, a date, and a list of who it is wrong for.

We scope narrow on purpose. If a process is unstable or an off-the-shelf tool already covers it, we say so on the audit call rather than after the invoice.

WhatsApp AI AutomationMost asked for

A WhatsApp number that answers, qualifies and books without a person reading every message first. Business API provisioning and Meta verification, conversation design reviewed against your actual message history, human handoff with full context when the agent is unsure, CRM write-back, and two weeks of post-launch tuning.

₹50,000 – ₹1.5L2–3 weeks
Wrong for you if a human has to calculate a quote before any useful reply can be sent.

AI Agent Development

Agents scoped to one job with defined inputs, a defined output, and a human checkpoint where the cost of being wrong is high. Process mapping with the people currently doing the task, model selection and prompting against your own data, confidence thresholds, refusal paths, audit logging, and an evaluation dataset you keep.

₹1.5L – ₹5L4–6 weeks
Wrong for you if the process has no written rules and no history of past decisions to learn from.

n8n Workflow Builds

Self-hosted on your infrastructure, versioned in your repository. Workflows exported as JSON to Git, error handling that identifies the exact step that failed, credentials managed separately from the workflows, and a runbook documenting the failure modes for each one.

₹40,000 – ₹2L1–3 weeks
Wrong for you if it is a simple two-step integration — a stock SaaS connector is cheaper.

UiPath & RPA

New bots where the rules are stable, and rescue work on bots that keep breaking when a UI changes. Process discovery and feasibility assessment, attended or unattended development in UiPath or Power Automate, selector hardening, Orchestrator setup with exception queues, and a handover that lists the known issues instead of hiding them.

₹1L – ₹6L3–8 weeks
Wrong for you if the process changes monthly — maintenance will cost more than it saves.

Document Processing

Invoices, claims, KYC packets and contracts turned into structured data — with a confidence threshold and a review queue for everything below it. Extraction tuned on your documents rather than a demo dataset, per-field confidence scoring, validation against master data before anything is written, and accuracy measured on a held-out set.

₹1L – ₹4L3–6 weeks
Wrong for you if the data already arrives as a feed — an API, EDI or database export.

RPA to AI Migration

Moving the brittle parts of an existing estate to AI models while leaving the parts that work alone. Audit ranked by maintenance cost, a keep / migrate / retire recommendation with reasoning for every bot, one bot at a time, parallel running until parity, and a licence reduction plan where one applies.

Quoted after audit6–12 weeks
Wrong for you if you have fewer than five bots and they are working fine.

How an engagement runs

Four steps. The first one is free and the last one is you not needing us.

No discovery phase that bills for three months. No transformation programme. One process, taken all the way to done.

01

Audit

Forty-five minutes on your most painful process. You leave with a straight answer on whether automating it is worth doing — including when the answer is no.

45 min · free
02

Scope and fixed price

One process, one price, one delivery date, written down before any work starts. If the scope changes, the price conversation happens before the work, not after.

fixed, in writing
03

Build in the open

Repository access from day one and a working demo every week. You watch it being built, so there is no reveal at the end that turns out to be a slide deck.

weekly demos
04

Handover and exit

Documentation and a runbook as standard deliverables, not upsells. Thirty days of defect fixing included. Ongoing support exists but is optional — by design.

30 days included

Three columns, one honest look

Doing it manually. Hiring a typical AI agency. Working with us.

The middle column is not a strawman — it describes how a large part of this industry actually operates, and it is why so many of these projects end up abandoned.

Where it shows up Manual, today A typical AI agency Agentic AI Automation
Who owns the code Nothing to own — it lives in people's heads Their platform, their account, their terms Your repository, your cloud, your credentials
What it costs as volume grows Another hire, and another after that Metered per task; the bill climbs quietly Self-hosted, no per-task metering from us
Price certainty Predictable, and permanently high Time and materials, scope creeping Fixed price and a date, before work starts
Reply to an enquiry Next working day, if someone spots it Instant, but a scripted bot that annoys people Seconds, from your real message history
When it is unsure A person asks a colleague — eventually Guesses confidently and you find out later Confidence threshold, refusal path, review queue
Documents extracted Typed by hand from a photo on a phone OCR on a demo dataset; you re-check everything Tuned on your documents, accuracy measured on held-out data
When something breaks Somebody notices, days later Silent failure — no error, nothing logged Step-level error handling and a runbook per workflow
Proof it worked Nobody is measuring Anonymous case studies and round numbers Measurement agreed at scope; named case studies only
The day it ends The person leaves and the knowledge goes too You lose access to what you paid for Documentation, runbook, and everything already yours

Ask any vendor these

Ten questions worth asking before you sign anything. Ours are answered below.

Print this list and put it to every agency you speak to, us included. The answers tell you far more than a portfolio does.

01Who owns the code, and how do you hand it over?You do, from day one. It is built in your repository and your cloud account, not ours. There is nothing to transfer at the end because it was never held anywhere else.
02Whose name does the WhatsApp number sit under inside Meta?Yours. We do the Business API provisioning and Meta verification, but the number, the Business Manager and the assets stay in your name. You never need our permission to keep using your own channel.
03Who pays for the AI API, and whose key is it?Your account, your key, your bill — visible to you in real time. We do not resell tokens with a margin on top, and you can see exactly what a month of running actually costs.
04What happens the day we stop working together?Nothing stops. Documentation and a runbook are standard deliverables. Support is optional by design — if you never call us again, the thing we built keeps running.
05What does the agent do when it does not know the answer?It stops. Every build has a confidence threshold, a refusal path and a review queue underneath it. A named person gets the case with the full context and the reason it was escalated. It is designed to hand over, not to guess.
06How will we know whether this actually worked?The measurement method is agreed at scoping, before the build, using a number you already track. For document work, accuracy is measured on a held-out dataset you keep and can re-run yourself later.
07What does it cost to change something three months after go-live?Because the workflows are in your repository and documented, small changes are something your own team can make. Where you want us, it is quoted per change — we do not hold edits hostage to a retainer.
08What happens to the bill if our volume multiplies by five?Our fee does not move — it was fixed for the build. Self-hosted n8n has no per-task metering. What does scale is your model and messaging usage, billed directly by the provider at their published rates, which we walk you through at audit.
09Where are our customers' conversations stored?On infrastructure in your accounts, in a region you choose. We work with clients in India, the UK, the US, the UAE, Singapore and Saudi Arabia, and data residency is settled at scoping rather than discovered later.
10What are you recommending we do not automate?There is always something, and you will hear it on the free audit call. Processes that change monthly, work with no written rules, anything already handled by a tool you own. We turn work down when the maintenance would cost more than the saving.

Where we have done this before

Six sectors, and the one process we would start with in each.

Banking, healthcare, law, logistics and manufacturing — the sectors behind eleven years of enterprise IT and nine years of RPA across India, the UK and the Middle East.

Healthcare & clinics

PainNo-shows and intake forms that eat the front desk's morning.
FixAppointment reminders and pre-consultation intake on WhatsApp. Controlled trials show two reminders beat one on attendance.8

E-commerce & D2C

PainSlow replies to enquiries and endless "where is my order" messages.
FixEnquiry qualification and order-status answering on WhatsApp, written back to your store and CRM.

Law firms

PainAssociates burning billable hours on first-pass document review.
FixDocument intake, classification and clause extraction, with a review queue below the confidence threshold.

Manufacturing

PainThree-way matching of purchase orders, invoices and goods receipts, by hand.
FixPurchase order and goods-receipt matching in UiPath or Power Automate, with an exception queue.

Logistics

PainPaper proof-of-delivery re-keyed from photographs, days after the fact.
FixProof-of-delivery capture and exception handling, validated against master data before it is written.

Professional services

PainRevenue leaking through unbilled time and a pipeline nobody has updated.
FixTimesheet chasing, proposal assembly and CRM hygiene as self-hosted n8n workflows.

What it costs

Published, because you should not have to sit through a call to learn the range.

Every engagement is quoted at a fixed price against a written scope. These are the bands almost everything lands inside.

One focused process ₹50,000 – ₹1.5L

A single workflow taken end to end. WhatsApp automation and most n8n builds sit here. Typically delivered in 1–3 weeks; a narrow single-process build can be 7–10 days.

Multi-process department build ₹2.5L – ₹8L

Several connected processes across a function — document pipeline plus review queue plus system write-back, or a bot estate migration. Typically 4–12 weeks.

Ongoing support · optional ₹15,000 – ₹40,000/mo

Monitoring, tuning and change requests. Genuinely optional — 30 days of defect fixing is already included, and the runbook is written so your team can take it on instead.

Typical delivery is four weeks. Model and messaging usage is billed to you directly by the provider at their published rates — we take no margin on it, and we walk you through the real monthly figure on the audit call before you commit to anything.


We have no case studies published. That is deliberate, and it is temporary.

Our rule is that a case study names the client, states how the result was measured, and carries written approval from that client before it goes online. Two engagements are in that approval process now. Until they clear it, this page stays empty rather than filling up with anonymous "a leading NBFC" stories and round numbers nobody can check.

What we can do instead: on the audit call we will talk through comparable deployments in detail under NDA, including the parts that went badly. Given that 95% of AI pilots return nothing measurable1 and more than half of tech buyers regret their last big purchase,4 we would rather earn the first hour of your scepticism than spend it.


Who you are actually hiring

A small senior team in Faridabad. No branches, no bench.

Not a staffing agency, not a reseller, not a large consultancy. We take a limited number of engagements at a time because the work is done by the people you meet on the audit call.

IndiaUnited KingdomUnited States UAESingaporeSaudi Arabia

Remote delivery with overlapping working hours by arrangement. One office, in Faridabad, Haryana.

  • 11 yrsin enterprise IT — banking, healthcare and logistics.
  • 9 yrsbuilding RPA and automation for banks, hospitals, law firms and logistics operators across India, the UK and the Middle East.
  • ToolsUiPath, Power Automate, n8n, and the AI model layer on top of them.
  • WhyMost of those nine years went on the unglamorous half of automation: keeping bots alive after the consultants left. This company exists because that half is where projects are actually won or lost.
  • We say noTo unstable processes, to work an existing tool already covers, to anonymous case studies, and to anything that would make you permanently dependent on us.

Straight answers

The questions that come up on nearly every call.

Will this replace our staff?

Not in the engagements we run. We automate the copy-paste half — the retyping, the chasing, the "did anyone reply to this" half. Surveys put manual data entry alone at nine-plus hours per employee per week, with around 60% of people reporting burnout from repetitive data work.9 That is the part we take. Judgement work still needs the person, and it gets more of their attention once the retyping stops.

We tried automation before and it broke. Why is this different?

Because that is the problem we were hired to fix for nine years before starting this. The failure mode is almost never the build — it is silent failure after handover: a schedule that stops firing, a token that expires and returns a polite empty response, a screen that changes and a selector that no longer matches. Every build we deliver has step-level error handling, an exception queue and a runbook that names the failure modes per workflow. Nothing goes out with only the happy path tested.

Do we have to replace our existing software?

No. Agents work on top of what you already use — your CRM, ERP, accounting software, shared drives and your existing WhatsApp number. Replacing a working system is usually the most expensive possible way to fix a process problem, and we will say so.

What if the agent gives a customer wrong information — who is liable?

You are, legally, and that is exactly why we build the way we do. In February 2024 a tribunal in British Columbia rejected an airline's argument that it was not responsible for what its own chatbot told a passenger.10 Every agent we ship has a confidence threshold, a refusal path and a review queue, and the scope of what it is permitted to state on its own is agreed with you in writing before launch.

How long does it take?

Typical delivery is four weeks. A narrow single-process build can be 7–10 days. UiPath work runs 3–8 weeks and a bot-estate migration 6–12. You get the specific date in the scope document before any work begins — for context, Deloitte's survey of 400 firms found 63% of RPA projects missed their delivery deadlines,11 which is precisely why we commit to one in writing.

Is n8n really better than Zapier or Make?

For some things, and not for others. Self-hosted n8n makes sense when you are paying meaningfully per task, when you need data to stay on your own infrastructure, or when you want the workflows in your Git repository. For a simple two-step integration, a stock SaaS connector is cheaper and we will tell you to use one.

Can you work with our data protection and compliance requirements?

It is settled at scoping, not discovered afterwards. Everything runs in your accounts, in a region you pick, with access scoped to the specific systems a workflow needs and every action logged. We work across India, the UK, the US, the UAE, Singapore and Saudi Arabia, so residency questions are a normal part of the conversation.

What does the free audit actually involve?

Forty-five minutes on one process — the one that hurts most. We map how it really works, not how the process document says it works. You leave with a straight recommendation, including the times the recommendation is "don't automate this, fix the process first" or "the tool you already pay for does this."

Start with one process

Show us the job nobody in your team wants to do.

Forty-five minutes, no charge, no deck. You will get a straight answer on whether it is worth automating — and if it isn't, you will get that answer too.

EmailContact@agenticAiAutomation.co OfficeFaridabad, Haryana, India ServingIndia · UK · US · UAE · Singapore · Saudi Arabia

Follow the build

We publish what we learn, including the failures.

Workflow teardowns, honest post-mortems, and the odd rant about per-task pricing. Pick your channel.

Sources for every figure on this page

  1. MIT Project NANDA, The GenAI Divide: State of AI in Business 2025 — 95% of generative-AI pilots showed no measurable P&L return. Coverage
  2. S&P Global Market Intelligence, 2025, n=1,000+ — 42% of businesses scrapped most AI initiatives, up from 17% in 2024. CIO Dive
  3. EY, Get Ready For Robots — "we have seen as many as 30 to 50% of initial RPA projects fail." PDF
  4. Gartner, survey of 1,120 executives — 56% report high regret over their largest tech purchase in the last two years. Coverage
  5. Oldroyd, McElheran & Elkington, The Short Life of Online Sales Leads, Harvard Business Review, 2011 — 2,241 US companies audited; average first response 42 hours; 23% never responded. HBR
  6. Analysis of the "98% open rate" claim, which originates in vendor marketing with no published methodology; operator data across 40 campaigns puts real read rates at roughly 60–94% depending on list quality. Analysis
  7. Meta, WhatsApp Business Platform pricing — per-delivered-message billing since 1 July 2025; rates vary by country; India marketing rate increase effective 1 January 2026. Meta docs
  8. Randomised trial, 54,066 visits across 25 primary-care clinics — no-show rate 4.4% with two reminders vs 5.3–5.8% with one (P<.001). AJMC
  9. QuestionPro for Parseur, July 2025, n=500 US professionals — 9+ hours per week on manual data transfer; ~60% report burnout from repetitive data tasks. Release
  10. Moffatt v. Air Canada, BC Civil Resolution Tribunal, 19 February 2024 — airline held responsible for information its chatbot provided. CBC
  11. Deloitte Global Robotics Survey, 400 firms — 63% did not meet RPA delivery deadlines. Forbes