AI agents for Indian businesses
AI agents that do the work. Your people approve what matters.
Agents that read your documents, check them against your rules and update Tally, your CRM or WhatsApp, and pause for a person wherever the decision carries risk.
studio finance invoice-matching
LiveRepresentative flow with sample data.
In short
What is AksharAI?
AksharAI is a platform for building AI applications and agents, made by Code Colonies Private Limited. Agents read documents, check them against your rules and update systems such as Tally, your CRM or WhatsApp, and they pause for a named person wherever a decision carries risk. It runs hosted, in your own cloud account or on premise, and plans start at ₹7,500 a month.
- Made by
- Code Colonies
- Plans from
- ₹7,500 a month, before GST
- Custom builds
- INR 199 an hour
Why most AI pilots stall
The model is rarely the problem. Everything around it is.
Most Indian businesses have tried an AI tool by now. The ones that kept it running solved three ordinary problems first.
The demo worked. Production did not.
A chatbot answered ten test questions well. Then it met real invoices, real customers and real exceptions, and nobody could say why it got the eleventh one wrong.
The data sits in five places
Orders in Tally, customers in the CRM, policies in a shared drive, conversations on WhatsApp. An assistant that cannot reach them is guessing.
Nobody decided who approves
Can the agent send the reply, or only draft it? Can it post the entry, or only suggest it? Without those answers, teams either block it or trust it too much.
Follow one invoice
From the inbox to Tally, step by step.
A morning in a Rajkot distributor's accounts team, shown with sample data.
09:12 · It arrives
A supplier invoice lands in the accounts inbox
Supplier S-014, a polymer supplier, emails a PDF invoice for 400 kg of HDPE granules. The agent picks it up from the inbox it watches, along with the photos suppliers send on WhatsApp.
AksharAIIncoming documentsAKRepresentative screen with sample data. Client screens stay private. 09:12 · It reads
The fields are read and laid out for checking
Supplier GSTIN, invoice number, PO reference, line items, rates and tax are pulled into a structured record. Anything read with low confidence is marked for a person to check.
AksharAIINV-3384, Supplier S-014AKINV-3384, Supplier S-014
- Supplier GSTIN
- 24AAKFS4471M1ZQ
- PO reference
- PO-2211
- Item
- HDPE granules, 400 kg
- Rate
- ₹236.00 per kg
- IGST
- ₹16,992.00
- Total
- ₹1,11,392.00
- Received by email
- Fields read
- Checked against PO and GRN
- Accountant approval
Representative screen with sample data. Client screens stay private. 09:13 · It checks
Your rules decide what is a match
The agent finds PO-2211 and the goods receipt in Tally, and checks quantity, rate, GSTIN and tax against the tolerances your team set. One line is outside tolerance, so it says why.
AksharAIChecks for INV-3384AKRepresentative screen with sample data. Client screens stay private. 09:40 · A person decides
The accountant sees the reason and decides
Priya in accounts gets the exception with the invoice, the PO and the difference side by side. She confirms the new rate with the supplier, approves it and adds a note for the PO.
AksharAIApproval, INV-3384AKApproval, INV-3384
Rate on INV-3384 is ₹236 per kg against ₹234 on PO-2211. Difference ₹800 on 400 kg. All other checks passed.
Supplier confirmed the revised rate from 1 October. Approve, and note it on the PO.
Approved by Priya Mehta at 09:40. Draft purchase voucher prepared with the note.
Voucher ready for posting. Logged with sources.
Representative screen with sample data. Client screens stay private. 09:41 · It lands
The entry goes into Tally, and the log keeps the record
The approved voucher is posted to Tally. The run log keeps the invoice, the checks, the approver and the time, so anyone can see later what happened and why.
AksharAIInvoice matching, this weekAKInvoice matching, this week
Invoices received
186
Matched first time
141
Sent to a person
45
Posted after approval
179
- Rate differences48
- Missing GRN27
- GSTIN mismatch14
- Unreadable scans11
Representative screen with sample data. Client screens stay private.
How it works
From one slow task to an agent you trust.
The same four steps whether you build it yourselves in the studio or with our AI team.
- Step 1
Pick one piece of work
A task that repeats every day, has clear inputs and a known right answer: matching invoices, answering order questions, triaging tickets.
- Step 2
Connect what it needs
Your documents, your ERP or CRM, your policies. Only the data the task needs, with access set per team.
- Step 3
Set the checkpoints
Decide what the agent may do alone and what a person approves. Test it on last month's real cases before anyone relies on it.
- Step 4
Run it and watch it
Every action is logged with its sources. Your team reviews the exceptions, and the rules improve from what they find.
The platform
Six parts, one place to build.
Design the app, wire the logic, configure the agent, build the screens, connect your systems and decide how it is governed.
Agents
Start from an agent that already knows the job.
Copilots that help a person work faster, and automation agents that take a task end to end within limits you set.
Built around the reviewer
The person who checks the work today decides what the agent may do.
Agents are scoped with the people who do the work, and those people become the reviewers. Their approvals, corrections and rules are what make an agent dependable.
Where it runs
Your data goes only where you allow.
An agent that reads salary slips or patient records has different needs from one that answers delivery questions. Pick the setup per application.
- Hosted by us
- The quickest start. We run the platform, handle updates and monitoring, and you build from day one.
- Your private cloud
- AksharAI runs in your own AWS, Azure or Google Cloud account, inside your network rules.
- On premise
- Installed on your servers for data that must not leave the building, with models you host yourself where needed.
Build it with our team
Want our AI team to build it with you?
- Rate
- INR 199 / USD 2 an hour
- Minimum
- 150 hours
- Advance
- None
- Payment
- After acceptance
- Custom connectors to your ERP, CRM or legacy database
- Agents tuned and tested on your real cases
- Forward deployed engineers who sit with your team
One agent, many channels
Answer where your customers and staff already are.
The same agent, with the same rules and the same log, can reply on the web, on WhatsApp, by email and on a voice line.
Industries
Useful work, sector by sector.
Each sector has its own documents, its own rules and its own pressure points.
Free tools
Work it out from your own numbers.
- Agent ROI calculator3 minutesHours and cost an agent could save on one process, against the plan, model and build cost, with the payback period.
- AI use case finder2 minutesPick your team and what slows it down. Get the agents and templates that fit.
- Hours saved calculator1 minuteTasks a week, minutes each, the share an agent could take, and your team's hourly cost.
- Agent readiness check3 minutesTen questions on one process. A score out of 100 and the next steps that matter.
- Cost to build estimate2 minutesA rough figure for a custom build by our team, in hours at INR 199 with a 150 hour minimum.
Resources
Practical reading for the people building it.
- Guide · Building agentsAI agents, chatbots and RPA: what is the difference?A chatbot answers questions, RPA repeats fixed steps in software, and an AI agent works through a task using judgement within rules. Most businesses need more than one, each for the work it suits.6 min read
- Guide · Data and integrationsRetrieval augmented generation (RAG) for company documents, in plain EnglishRAG lets an AI model answer from your own documents. The system finds the most relevant passages first, gives only those to the model, and asks for an answer that cites them, so people can check the source.6 min read
- Guide · Use casesKYC document extraction for NBFCs and banksAn extraction agent can read KYC documents, pull out the fields, cross check them and flag mismatches. The KYC officer still verifies identity and decides, under the RBI's KYC Master Direction and the lender's own policy.6 min read
- Guide · Use casesAI ticket triage for support teamsAI triage reads each new ticket, works out what it is about and how urgent it is, adds missing details and puts it in the right queue. Support leads set the rules and review the cases the agent is unsure about.6 min read
Questions
What teams ask us first.
What is AksharAI?
A platform for building AI applications and agents, made by Code Colonies. Your team designs the app, sets the logic, connects your systems and decides where it runs. Our AI team can also build it with you.
Do we need developers to use it?
Not for most of the work. Business teams can design forms, flows and simple agents in the visual builder. Connecting an old ERP, writing a custom connector or tuning an agent for a hard task usually needs an engineer, either yours or ours.
Which AI models does it use?
It works with leading model providers, such as OpenAI, Anthropic Claude and Google Gemini, and with open models you can host yourself. We choose per project, based on the task, the cost and where the data may go.
Will the agent act on its own?
Only where you allow it. Each agent has action boundaries: what it may read, what it may draft and what it may do. Anything with money, customers or legal weight can be set to need a person's approval first.
Can it run inside our own environment?
Yes. AksharAI can be hosted by us, run in your private cloud, or installed on your own servers when sensitive data must stay inside your walls.
How do we start?
Book a demo and bring one process that wastes time every week. We will show an agent working on a version of it, and say whether it is a good first candidate.
See it on your work
Bring one slow, repetitive process. We will show you an agent doing it.
A working session with someone who builds these every week.
Or get a ballpark proposal in two minutesWhat happens next
- Step 1A short call about one process and the systems involved
- Step 2A demo of an agent working on a version of your task
- Step 3A clear view of fit, approvals and what it would take