AI Lead Capture Automation
Capture, qualify, enrich, and route enquiries before the trail goes cold
Lead automation should not begin with a spreadsheet export. We connect forms, landing pages, ad campaigns, directories, chat widgets, LinkedIn lists, and inbound email into one qualification flow. The system validates phone numbers and email IDs, removes obvious junk, enriches missing fields, scores intent, and sends high-priority enquiries to CRM, WhatsApp, or the right sales owner. This matters in India because many sales teams still lose qualified leads between Meta Ads, Google Ads, Excel, and WhatsApp. The workflow gives sales one view of source, urgency, next action, and conversation history.
Intent scoringCRM routingLead enrichment
Enterprise Web Scraping
Data extraction pipelines that survive changing websites
A useful scraper is closer to a data product than a script. We build extraction systems with Playwright, Scrapy, browser automation, queueing, rate control, retries, proxy strategy, schema validation, and monitoring. Common use cases include product catalogues, competitor prices, public listings, job boards, market research, local business directories, and lead datasets. We define freshness, deduplication, failure alerts, and lawful boundaries before the first crawl. The output can feed a database, API, dashboard, CRM, or AI enrichment workflow.
Playwright/ScrapyProxy strategySchema checks
WhatsApp Marketing CRM
A compliant WhatsApp CRM bot for campaigns, reminders, and sales follow-up
WhatsApp is where Indian buyers reply, but unmanaged WhatsApp becomes scattered sales memory. We build on the official WhatsApp Business API with opt-ins, approved templates, audience segments, drip campaigns, payment reminders, order updates, and agent handover. AI can classify intent, draft replies, summarize long conversations, and suggest the next action. Rules decide when automation should stop and a human should take over. The CRM records source, consent, campaign, customer history, and follow-up status.
Official APIOpt-in flowsAI reply assist
Automated Trading Bots
Strategy automation with backtesting, broker APIs, and risk controls
Trading automation needs discipline before speed. We build systems around clean market data, tested strategy rules, broker APIs, position sizing, stop-loss logic, logs, alerts, paper trading, and emergency kill switches. Binance, Zerodha Kite, Interactive Brokers, Alpaca, and custom feeds can be connected through REST or WebSocket layers. We separate strategy logic from execution logic so a bad signal does not become an expensive order. The same engineering habits carry into business automation: logs, limits, audit trails, and controlled action.
BacktestingBroker APIsRisk controls
AI Agent Development
AI agents that retrieve context, use tools, ask for approval, and finish tasks
An AI agent is useful only when it has a defined job, reliable tools, and limits. We design agents that can read documents, search internal knowledge, call APIs, update records, draft responses, create summaries, and pause for approval when the action carries risk. RAG is often the foundation: documents are chunked, embedded, indexed, retrieved, cited, and evaluated before the agent acts. We use OpenAI, Claude, LangChain, LlamaIndex, vector databases, and custom orchestration where the workflow demands it. The result is not a chatbot dressed as a system. It is a supervised workflow with memory, retrieval, tools, and audit trails.
RAG pipelinesTool callingGuardrails
Data Cleansing & Enrichment
Clean CRM exports, product data, lead lists, and RAG knowledge bases
Most AI projects fail quietly because the data layer is weak. We clean names, companies, phone numbers, email IDs, addresses, product fields, duplicate records, inconsistent categories, and half-filled CRM exports before automation touches them. Then we enrich the dataset with public signals, validation APIs, business rules, and AI classification where it helps. For RAG systems, we chunk documents, add metadata, remove noise, test retrieval quality, and track which sources support each answer. Clean data is the difference between an AI demo and a workflow a team can trust.
DeduplicationField validationRAG-ready data