Most automation agencies sell a chatbot. We build the workflow around it: the trigger, the data it reads, the systems it updates, and the point where a person approves. Everyone on our team writes code, so the agent that answers your WhatsApp enquiry is the same system that writes the lead into your CRM and tells you why it did. Pick the job below, or start with a free automation audit.
Every automation starts from a business problem. These are the ones we solve most often, and what you get for each:
Capture, qualify and route every enquiry before it goes cold
Forms, ad campaigns, directories, chat and inbound email feed one qualification flow. Junk is removed, missing fields are filled, intent is scored, and hot leads reach the right salesperson on CRM or WhatsApp within minutes.
ProofLead capture and routing systems built for Salevant.ai.
Replies in seconds, in Hindi or English, and hands hot leads to your team
Built on the official WhatsApp Business API with opt-ins and approved templates. The bot answers, asks your qualifying questions, writes the lead into your CRM and hands over to a person when the conversation needs one. Campaigns and payment reminders run from the same system.
Agents that follow up, file and report, and show you what they did
Each agent has one job: lead follow-up, WhatsApp support, inbox-to-meeting, invoices, CRM clean-up or lead scoring. It works inside the tools you already use, pauses for approval where money or a customer promise is involved, and logs every step.
ProofAn inbox agent that reads enquiries, checks the calendar and drafts replies for approval, live for a law practice.
Self-hosted workflows on your own server, with retries and alerts
n8n connects your CRM, WhatsApp, email, sheets and accounts without per-task fees, and can run on a server you control. We build the workflows, add AI steps where they earn their place, wire in error handling and hand over with training.
PDFs, scans and Excel reports read, checked and filed into your systems
Invoices, reports and scanned forms are extracted, checked against your rules and written into your CRM or accounts. Anything unclear goes to a person for review, and every record links back to the file it came from.
ProofYears of PDFs, Excel sheets and scanned reports classified and filed into one client's CRM.
Agents that retrieve context, use tools and ask before acting
For work beyond a single job: agents that search your documents, call your APIs, update records and pause for approval when the action carries risk. Private RAG keeps retrieval on your data, with sources cited on every answer.
Data extraction pipelines that keep working when websites change
Catalogues, competitor prices, listings and directories collected on a schedule, validated against a schema and delivered to your database, CRM or dashboard. Freshness, deduplication, alerts and lawful limits are agreed before the first crawl.
Strategy automation with backtesting, broker APIs and risk controls
Your strategy rules, tested on historical data, then connected to broker APIs with position sizing, stop-loss logic and an emergency kill switch. Every order, position and P&L is logged and visible live on one dashboard.
Clean CRM exports, lead lists and knowledge bases before AI touches them
Duplicate records, half-filled CRM exports, inconsistent categories and messy product data are cleaned and validated first. Then records are enriched from validation APIs and business rules, so the automation downstream works on data your team trusts.
ProofA multi-year report archive deduplicated and structured before it went into the client's CRM.
GPT, Claude, Gemini, LangChain, and vector databases are implementation choices. The first decision is what the workflow may read, change, approve, reject, and escalate.
Salevant.ai gives us a practical base in lead capture, enquiry qualification, CRM routing, and sales workflow automation. That experience matters when a company wants AI to move prospects from source to follow-up without losing context.
Lead lists, CRM exports, product catalogues, and document libraries are cleaned, validated, deduplicated, and structured before AI touches them. Bad input still produces bad automation.
AI agents can call tools, search records, draft replies, and update systems. We define permissions, approval gates, output checks, fallback states, and logs before they enter production.
Calls, WhatsApp, CRM, payments and field teams are mapped as one sales path before anything is automated, because that is how most Indian businesses actually sell.
Automation stops when APIs change, websites block traffic, CRMs reject fields, or prompts drift. We add logging, alerts, retries, and manual recovery paths from the start.
A stable process is worth automating. An unstable one only produces the same wrong answer faster, so every engagement starts by sorting the work into these two columns.
Anything that touches money or a customer commitment gets a human check before it lands, and every automated decision writes an audit trail you can read later.
Automation lives inside your existing systems. Nobody on your team has to learn a new dashboard to benefit from it.
We map how enquiries, documents and follow-ups move through your business today, then give you a written list of the workflows worth automating, the hours each one takes now, and a build cost range.
We identify the manual step that costs money: slow lead response, dirty CRM data, weak document retrieval, missed WhatsApp follow-up, scraping gaps, or trading logic without risk controls.
We map every source, destination, permission, API limit, field rule, approval point, and exception path. This keeps the AI automation project grounded in how the business actually works.
For narrow use cases, we show a working version quickly: one lead flow, one RAG query path, one WhatsApp handoff, one scraper, or one agent task. The prototype proves the system can move real data.
Retries, logs, queues, fallbacks, validation checks, source citations, human approvals, and alerts are added before launch. Most AI projects break at the edges, not in the demo.
The workflow goes live with dashboards, error alerts, usage checks, cost tracking, and repair paths. Teams know what the automation did, what it skipped, and what needs human attention.
Prompts, retrieval, routing rules, field mappings, scoring, and reports improve after real users touch the system. AI automation should mature from operational evidence, not opinions.
Quick summary — everything you need to know about our AI & Automation service
We build lead capture systems, WhatsApp CRM bots, AI chatbots, RAG systems, AI agents, web scrapers, data enrichment workflows, reporting automation, and trading bots. The scope depends on the workflow depth, data quality, integrations, and approval rules.
Yes, when the workflow is narrow. A lead routing flow, scraper proof, WhatsApp handoff, internal knowledge chatbot, or simple RAG assistant can usually be demonstrated in 24-48 hours. Multi-agent workflows, broker-grade trading systems, and large document retrieval systems need discovery before a realistic delivery estimate.
A chatbot mainly answers. An AI agent can retrieve context, use tools, call APIs, update records, check rules, and ask for approval before taking action. We still keep guardrails around it because unrestricted agents are unreliable in business workflows.
Yes. We prepare documents, chunk content, add metadata, create embeddings, index them in a vector database, test retrieval quality, and connect the answer layer to GPT or Claude. The important part is evaluation: the system must retrieve the right evidence before it produces an answer.
We build on the official WhatsApp Business API with opt-in capture, approved templates, consent records, and agent handover. The aim is to support sales follow-up without turning WhatsApp into spam.
Yes. We commonly connect automation with Zoho, HubSpot, Google Sheets, WhatsApp Business API, payment systems, custom CRMs, internal dashboards, and REST APIs. For legacy systems, we first map what can be integrated safely.
It depends on three things: how deep the workflow goes, how many systems it reads from and writes to, and how many approval rules sit in the middle. A single lead-routing flow and an agent working across your CRM, WhatsApp and accounts are priced very differently. The free automation audit ends with a written build cost range for each workflow we recommend.
A narrow workflow can usually be shown working on your own data in 24-48 hours. Going live takes longer, because failure states, access controls, monitoring and handover are built and tested against real traffic first. You get a timeline once the integrations are mapped.
Where it needs to stay private, it stays on infrastructure you control. n8n can run self-hosted on your own server, document search can sit in a private RAG index, and each workflow is limited to the records it needs. Access is role-based and every read and write is logged.
It stops safely and tells someone. Every step is logged, calls that fail for temporary reasons are retried, and an alert goes out when a step still does not complete. Items that could not be processed wait in a queue for a person instead of being dropped. After launch, fixes and changes are covered by a support retainer.
Whichever suits the workflow. Zapier suits simple app-to-app steps at low volume. n8n gives more control over branching and custom code, and self-hosting removes per-task fees. Custom TypeScript or Python fits unusual APIs, strict data rules or high volume. Many builds mix all three.
That answer is usually enough for us to tell you what it takes, roughly what it costs, and whether we are the right people for it. No deck, no discovery fee — a conversation with the engineer who would do the work.