AI in your product

    Custom chatbots

    Support bots and internal assistants trained on your data, linked to your CRM, and deployable on cloud or your own servers when required.

    Beyond a FAQ page with a text box

    Chatbots fail when they cannot see live order status, invent return policies, or leave users stuck with no path to a person. We build assistants that read from your real systems: orders, tickets, bookings, inventory, not static FAQ pages alone.

    For a marketplace we tied conversations to specific products and orders so buyers and sellers were not repeating information already in the system. For an operations platform we built an internal assistant that answers stock and margin questions from live data, not outdated spreadsheets.

    When the bot should act, not just reply

    The assistant can look up a record, create a ticket, or send a notification when that is what the user needs. We set permissions so a customer-facing bot cannot trigger admin actions. When the bot is unsure, the full conversation goes to your team so customers do not repeat themselves.

    When data must stay on your servers

    Banks, government contractors, and healthcare clients sometimes cannot send customer data to third-party AI services. We can run open models on your infrastructure with the same document search and tool connections as cloud setups. We will tell you upfront where capability differs so you can decide.

    Tools we typically use

    We adapt to your stack when needed. These are common on projects like this.

    OpenAI / Claude / Gemini
    Ollama / LLaMA / Mistral
    React / Next.js
    Node.js / Python
    WebSockets
    CRM APIs (HubSpot, Salesforce, Zoho)

    Common questions

    How is this different from Intercom or Zendesk AI?
    Off-the-shelf tools work well for generic support. We build when you need deep integration with custom ERP/CRM logic, proprietary data sources, or deployment constraints those platforms do not support.
    Can the chatbot work in Hindi or other languages?
    Yes. Model choice affects quality per language. We test with your actual user queries in each language before launch.
    What does ongoing maintenance look like?
    You will want to review misfires monthly and update the knowledge base when products or policies change. We can hand that over to your team or stay on retainer for tuning.

    Start a project

    Tell us what you need built

    One call is enough to see if we are the right team. If we are, you get a written scope before any contract, so you know cost and timeline upfront.

    NDA availableNo hard sellReply within 2 business hours

    What happens next

    3 steps
    01

    15-minute call

    You explain the problem. We ask questions. No slide deck from us.

    02

    Written scope in ~48 hours

    Timeline, team shape, milestones, and price range for you to review.

    03

    Kickoff with your squad

    PM, tech lead, shared Slack or Teams, and repo access from day one.