AintNoS

AI transformation studio Ain’t No Software·AI, Not OS
First agent live in about 4 weeks · from audit to production

We make AI part of how your company runs, and turn it into results you can measure.

AintNoS leads AI transformation for 50–500 person companies. Strategy comes first. Then we build agents and workflows into the tools your teams already use, with people approving every decision that matters.

Book a transformation call Project your profit impact
Tower crane over a building under construction
ConstructionChange-order agent
Accountant working with a calculator and laptop
FinanceCash collections agent
Approval, not
autopilot.
AIQuote-to-Order Agent
#sales-desk · nowL2
Quote Q-7731, line 17 is at 11% margin, below the 14% floor. Approve 13%?
ApproveReject
Consultant writing goals on a whiteboard
Strategy2-week audit readout
Glowing orange growth line over a dark city skyline
ImpactMeasured against your baseline
Doctor at a computer with a patient
HealthcarePrior-authorization agent
Every step
on the record. 
06:00:00 scan     412 programmes
06:02:41 match    3 fit your profile
06:03:10 draft    eligibility memo
06:03:12 review   finance lead
City lights at night seen through a fence
New revenueGrants & incentives agent
Leadership team meeting around a boardroom table
TransformationMonthly steering review
Abstract network of glowing lines
AI AgentsTools, guardrails, logs

We don’t sell you another platform. We change how the work gets done, inside the tools you already pay for, and we measure every result.

Measured before we build

We time your current process first, so every result is compared with your own baseline.

Tested on your real data

Each agent and workflow ships with an evaluation set of your past cases and a pass rate you can re-run.

No lock-in

Built in your accounts with your API keys. Code, prompts and documentation are handed over.

People on the risky steps

Anything that moves money, contacts a customer or changes a record can require a one-click approval.

Services

Four ways in. Fixed scope, fixed fee.

Every engagement is quoted as a fixed fee after a 30-minute call. Most clients start with an audit, prove value with one build, then scale. HoverTap a card for the full picture.

Flagship3–6 months

AI Transformation

From scattered pilots to AI in every team’s week, measured in the P&L.

Fixed fee per phase
  • AI usage policy and tool approvals
  • 3+ agents or workflows in production
  • Role-based training and internal champions

Best for 50–500 people with an executive sponsor.

roadmap / 6-month programExample
WorkstreamM1M2M3M4M5M6 Policy & dataPolicy CollectionsAgent 1 QuotesAgent 2 GrantsAgent 3 Team trainingBy role P&L reviewP&L
Plan a program
Start here2 weeks

Strategy Audit

Know where AI pays back before you spend. We score 20–40 real tasks on value and feasibility.

Fixed fee · credited to a build
  • Scored opportunity map
  • Build, buy or skip for each task
  • 90-day roadmap with owners

Credited in full against a build if you go ahead within 60 days.

opportunity mapIllustrative
  • Invoice entryBuild
  • QuotesBuild
  • Call notesBuy
  • Credit callsSkip
Ask about an audit
Build3–6 weeks

AI Workflows

One process rebuilt end to end. A model reads, sorts and drafts; a person approves.

Build Sprint · fixed scope
  • A running workflow in your tools
  • An eval set of your real cases, with pass rate
  • Runbook and 30 days of tuning

Best for high-volume, text-heavy work with clear rules.

supplier onboardingPattern
  1. Form arrives by email
  2. Tax ID and bank details checked
  3. Duplicates and sanctions flagged
  4. Vendor record drafted in the ERP
  5. Finance approves the new vendor
Scope a sprint
Connect1–3 weeks

Automation

Stop retyping the same data into five systems. Rules first; a model only where rules run out.

Fixed scope and fee
  • Integrations with retries and alerts
  • Credentials stay in your vault
  • A diagram of every trigger

Best for teams keying the same data into three or more tools.

deal won → project live1 AI step
  1. CRM
  2. Project
  3. Slack
  4. AI brief
  5. Invoices
Wire it up
Our products

Three products, built by AintNoS.

Ready-made tools from the same team, for problems we kept meeting in client work. Both use what you already have.

See what your ERP is leaking.

Send read-only ERP exports and BTLedger returns a rupee-accurate list of billing gaps, each with the evidence to check it in your own system.

  • Delivered, not invoiced
  • Billed below the agreed price
  • Billed twice

Read-only exports · No ERP login · AI off by default

Explore BTLedger
Sample reportSynthetic data
OrderInvoiceAmount
SO-4410INV-9921₹ 42,300.00
SO-4411INV-9922₹ 18,950.00
SO-4415Not invoiced₹ 1,86,400.00
SO-4418INV-9930₹ 64,120.00
SO-4421INV-9934₹ 27,800.00
Unbilled · 1 finding · high confidence₹ 1,86,400.00

Give AI the work. Keep your privacy.

PriGate swaps names, clients, IDs, account numbers and amounts for believable doubles before your text reaches ChatGPT, Claude, Copilot or Gemini, then puts the real names back in the answer.

  • Spots names, IDs, bank numbers and amounts
  • Swaps each for a believable double
  • Brings the real names home in the answer

Runs on your computer · Word, Excel, PDF & e-mail

Explore PriGate
Round tripExample
You type

Will Brightline Logistics pay Priya Sharma $2,400,000?

The AI sees

Will Harborview Freight pay Odette Underhill $1,700,000?

You read

Brightline Logistics will pay Priya Sharma if the clause is narrowed.

3 values swapped · real names restored0 leaked

Every charger, live on one map.

ChargeMesh connects to your chargers over OCPP and shows state, sessions, energy and faults as they happen, with safe remote commands.

  • Every site, charger and connector on a live map
  • Incidents with safe remote commands over OCPP
  • Earnings by charger, site and network

OCPP 1.6J · Multi-tenant · Simulated demo

Explore ChargeMesh
Network boardSimulated
SiteChargingEarned today
Avinashi Road Hub5 of 5₹ 18,420
Peelamedu Mall3 of 4₹ 9,860
L&T Bypass PlazaFault on C2₹ 12,310
OMR Sholinganallur4 of 5₹ 16,050
GST Road Tambaram2 of 4₹ 7,240
5 sites · 1 open incident₹ 63,880
AI Agents & results

Agents that do the work, inside guardrails you set.

Five agents we build: two for almost any business, two for specialist fields, and one that finds money you are already entitled to. Each shows a sample run, its guardrails and what it changed.

L1 · Suggests, a person acts L2 · Acts after approval L3 · Acts within set limits

Sample runs and results are illustrative. Tool names are examples; agents connect to whatever your team already uses.

Profit impact

See what five years of AI could do to your profit.

Move the sliders to match your business. Hover the chart to compare, and click any month to see where the change comes from. It’s an estimate built on your inputs, and the assumptions are listed below the chart.

Scenario

Everything is calculated in your browser. Nothing you enter is stored or sent.

Year-5 profit––
5-year net gain–after program cost
Payback–investment recovered
Margin, year 5––
Operating profit, annualized run rate
With AIWithout AI
Line chart comparing annualized operating profit with and without AI over 60 months.
Assumptions and yearly table

Revenue grows 5% a year in both lines. Work is automated gradually from month 3, reaching full adoption after 24, 15 or 10 months (conservative, expected, ambitious). Only 45%, 65% or 80% of the automatable time turns into savings. Freed capacity adds 0.5%, 1% or 2% extra revenue once fully adopted, which earns four times your operating margin, capped at 50%. Running AI costs 20% of the savings plus $36k a year. The program investment is spread over months 1–6, which is why profit dips first. Actual results depend on your processes and are measured against your baseline in the audit.

YearWithout AIWith AIDifferenceMargin with AI
How an engagement runs

First agent running in about a month.

The same four steps start every engagement. Transformation programs repeat steps two to four in each department.

  1. AuditWeek 1

    We sit with the people doing the work, time each step, and collect 50+ real past cases.

    You get: a baseline and a scoped build
  2. BuildWeeks 2–3

    We build in your accounts and test against those cases until the pass rate meets the bar we agreed.

    You get: a working agent and its eval report
  3. PilotWeek 4

    It runs alongside your team on live work. Every AI decision is reviewed.

    You get: a go / no-goFirst agent live
  4. ScaleWeek 5+

    Runbook, recorded walkthrough, 30 days of tuning, then the next department.

    You get: a team that can run it
What week 1 produces: an opportunity map, not a pitch deckIllustrative example
TaskVolume / monthRule clarityError costData accessVerdict
Supplier invoice entry1,20053NetSuite APIBuild · sprint 1
Order-status emails90042Zendesk + ShopifyBuild · sprint 2
Sales call notes to CRM40031Zoom + HubSpotBuy · native tool
Credit limit decisions3525SpreadsheetsSkip · low volume, high risk
Weekly ops report451BI toolSkip · a scheduled query does it

Two of five are recommended against. That’s typical, and why the audit comes first.

After any engagementOngoing Partner. Monitoring, model updates and a new agent each quarter. Monthly, capped hours, cancel with 30 days’ notice.Talk about ongoing →

FAQ

Questions teams ask on the first call.

When will you tell us not to use AI?

Recommending against a project is part of the job. The usual reasons:

  • A formula or a filter already solves it. A model only adds cost and a new way to fail.
  • The volume is low and mistakes are expensive. Thirty credit decisions a month don’t justify the risk.
  • The process changes every week. Stabilise it first, then automate.
  • Nobody owns the process. Without an owner to review exceptions, an agent drifts.
  • The data can’t leave your network. Then we use self-hosted models, or we don’t proceed.
Where does our data go?

To the model provider you approve, under a business agreement that excludes your data from training, or to a self-hosted model if your policy requires it. We document every system that touches the data before we build.

Which AI models do you use?

Whichever passes your eval set at the lowest cost. Usually that’s a current OpenAI or Anthropic model, and sometimes a smaller open model for high-volume classification. Because every agent is tested against your cases, switching models later is a test run, not a rebuild.

What’s the difference between an agent and a workflow?

A workflow follows fixed steps. An agent chooses which tool to use next, within limits you set. We use the simplest one that does the job, and most projects mix both.

Who owns what you build?

You do. It’s built in your accounts with your API keys, and the code, prompts, eval sets and documentation are handed over.

What happens when an agent gets something wrong?

Every agent has a confidence threshold and an autonomy level. Below the threshold, the case goes to a person. Every action is logged with its reasoning, so mistakes can be traced and added to the eval set.

How do you measure whether it worked?

Against the audit baseline: time per case, error rate, and the share of cases that still need a person. If the pilot doesn’t beat the baseline, we say so and don’t roll it out.

Talk to AintNoS

Tell us which process eats the most hours.

Thirty minutes, no deck. You describe the process. We tell you whether AI fits, what it would take, and a fixed price if it does.

  • Min 0–10You walk through the process as it runs today
  • Min 10–25We sketch the agent or workflow and its risks
  • Min 25–30Fit or no fit, and next steps if it fits
What are you interested in?
We reply within one business day.

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