AI that does the work, not the talking.
WhatsApp automation, AI agents and retrieval over your own documents - wired into the systems you already run. Every capability on this page is live in a production system you can read about, not a slide.
19
ERP modules in daily production use
15,267
Problems served by an AI study platform
24/7
Automated WhatsApp customer response
4
Model providers integrated in production
Six things AI is genuinely good at
Ordered by how often they are actually bought, not by how advanced they sound.
WhatsApp AI Automation
The official Meta Cloud API with a model behind it: incoming customer messages are read, matched against the customer's own order history, and answered or escalated. Built against Meta's messaging policy, because a restricted business number takes the entire channel down with it.
- Replies drafted from real order and payment history
- Automatic escalation to a human on low confidence
- 24-hour service window tracked per contact
AI Agents & Assistants
Agents that carry out a task rather than describe it: read an inbound enquiry, pull the matching products, draft the quotation, and put it in front of a human to approve. Scoped to a workflow with a defined start and end, which is why they hold up in production.
- Tool-calling against your existing database and APIs
- Human approval step on anything that leaves the building
- Full audit trail of what the agent read and wrote
RAG & Document Intelligence
Retrieval-augmented generation over your own documents - catalogues, contracts, SOPs, past quotations - so answers come from your files with a citation, instead of from the model's memory. This is the fix for hallucination, and it is the reason internal AI tools survive contact with a real team.
- Vector search over your document set
- Answers cite the source file and section
- Access control carried through to retrieval
AI Document & Resume Analysis
Structured extraction from unstructured files. Resumes scored against a role, invoices read into line items, enquiries parsed into a CRM record - the work that currently costs a person an afternoon of copying fields between two screens.
- Structured JSON output, validated before it is stored
- Scoring and ranking against your own criteria
- Bulk processing with a per-file confidence score
AI Workflow Automation
The handoffs between systems, automated end to end: an email arrives, the enquiry is extracted, the record is created, the acknowledgement goes out, the salesperson gets a WhatsApp. AI is used at the steps that need judgement and plain code at the steps that do not - which is what keeps it cheap to run.
- Email, WhatsApp and form intake into one pipeline
- Deterministic code where AI is not needed
- Retries and dead-letter handling on every step
LLM Integration Into Existing Software
Adding a model to software you already run, without a rebuild. Vertex AI, Groq, OpenAI or an open-weight model on your own infrastructure - chosen on latency, cost per call and where your data is allowed to sit, not on which one is in the news.
- Model choice driven by cost, latency and data residency
- Token cost budgeted and monitored per feature
- Graceful degradation when the provider is down
Pilot first, commit second
An AI build's cost is driven by how messy the existing data is, and nobody knows that on day one. The pilot exists to find out cheaply.
Scope one workflow
A call to find the step that is actually costing you time, and an honest answer on whether AI is the right tool for it. Sometimes it is a database query and you get told that.
Pilot on your real data
Two to three weeks building that one workflow against your own records, not a demo set. Accuracy is measured and reported, including where it falls short.
Decide with evidence
A written recommendation on whether to go further. Stopping here is a legitimate outcome, and it is priced so that it is not a loss.
Build, wire in, hand over
The full automation, connected to your ERP, CRM or database, with dashboards for volume, accuracy and cost per call. Code, prompts and accounts transfer to you.
Two systems, running now
Each one covers the problem, where the model sits, the architecture, and what it does not do.
What AI work costs
Quoted as engagements rather than packages, because scope depends on the state of your data. Website and ERP pricing lives on the services page - these are separate budgets and separate conversations.
AI Pilot
$549 – $899
One workflow, proven on your own data before you commit
2–3 weeks
- One workflow, scoped and measured
- Built against your real data, not a demo set
- Accuracy measured and reported honestly
- Runs in production, not a slide deck
- Written recommendation on whether to go further
AI Automation
$1,499 – $2,999
Support, sales or operations automated end to end
4–8 weeks
- Everything in the Pilot
- WhatsApp, email and web intake in one pipeline
- Wired into your ERP, CRM or database
- Human approval on anything customer-facing
- Dashboards for volume, accuracy and cost per call
- 3 months of support after launch
Custom AI Platform
Custom
RAG, agents and internal tools as one system
8 weeks and up
- Retrieval over your full document set
- Multiple agents with shared tools and memory
- Role-based access carried into retrieval
- Self-hosted or private-cloud model options
- Architecture documentation and team handover
- Priority support
Model running costs
Billed by the model provider on usage, not by me. A support automation handling 1,000 conversations a month typically runs ₹1,500–₹6,000 ($20–$75) in API costs. Measured and reported from day one.
Your data stays yours
No client data is used to train anything. Where the data cannot leave the country or the building, the build targets a self-hosted or private-cloud model instead - it costs more to run, and it is sometimes the only lawful option.
Where AI is the wrong tool
If a rule or a database query gets it right every time, that is what gets written. AI is used where judgement is needed. Saying so up front is cheaper for you than discovering it at the end of a build.
Common questions
A scoped pilot on one workflow runs ₹45,000–₹75,000 and takes two to three weeks. A full automation across support, sales or operations runs ₹1,20,000–₹2,50,000 over four to eight weeks. Platform work with retrieval and multiple agents is quoted per project. Model API usage is billed separately by the provider and typically adds ₹1,500–₹6,000 a month at moderate volume.
An off-the-shelf chatbot answers from a script or a help-centre article and cannot see your business. These builds are wired into the systems you already run, so the answer to "where is my order" comes from the actual order record. The trade-off is honest: a subscription tool is live this afternoon, and a custom build takes weeks. If a subscription tool solves your problem, you will be told that.
That is what retrieval-augmented generation exists to prevent. Answers are grounded in your own documents and records and carry a citation back to the source, and anything below a confidence threshold is escalated to a person rather than guessed at. Nothing customer-facing goes out without either a grounded source or a human approval step.
Existing software, in most cases. The common shape is a model added to an ERP, CRM or internal tool that already works, reached through its API or database. A rebuild is only proposed when the existing system has no way in at all, and that is a separate conversation with a separate price.
Vertex AI (Gemini), Groq, OpenAI and open-weight models such as Llama, selected per project on latency, cost per call and where the data is legally allowed to sit. Production systems here run on Vertex AI and Groq today. The integration layer is written so the model can be swapped without rewriting the application.
You do. Source code, prompts, evaluation sets and infrastructure configuration all transfer on final payment, and the model accounts are registered in your name so the API keys and the billing relationship are yours. There is no lock-in that requires paying me to keep the system running.
Yes. Web platforms, ERP, CRM, dashboards and e-commerce remain a core part of the work, and most AI projects here sit on top of exactly that kind of system. AI leads the offering because it is where the harder problems are, not because the software work stopped.
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