AI Automation

AI, ek asli business problem par

Homepage par chatbot nahi. Document processing, demand forecasting aur support automation — us manual process ke saamne naapa hua jise wo hata rahi hai.

AI ka paisa kahan wapas aata hai

  • Staff roz invoice, PO aur delivery note se data dobara type karta hai
  • Wahi bees customer sawaal din bhar manually jawab diye jaate hai
  • Stock planning andaaze par chalti hai kyunki demand ka pattern kabhi dekha hi nahi jaata
  • Report banti hai, par padhkar kya badla ye dekhne ka kisi ke paas waqt nahi
  • Quality inspection manual hai aur har shift me alag

AI Automation — kya-kya milega

Document extraction

Invoice, PO aur delivery note apne aap padh kar system me.

Support chatbot

Aapke catalogue aur policy par trained, zaroorat par aadmi tak escalate.

Sales assistant

WhatsApp par enquiry qualify karke garam lead aage bhejta hai.

Demand forecasting

Asli pattern aur season par reorder ka sujhav.

Natural-language reporting

Aam bhasha me sawaal puchiye, number mil jaata hai.

Anomaly detection

Ajeeb transaction ya khapat hote hi flag.

Content automation

Product description aur listing bade paimane par.

Call analysis

Call ka transcript aur summary seedha CRM record me.

Kya badlega

Roz ke ghanton ka data entry khatam
Baar-baar wale sawaal ka turant jawab, kisi bhi waqt
Stock ka faisla demand pattern par, andaaze par nahi
Dikkat apne aap saamne aati hai, dhoondhni nahi padti

  1. 1

    Discovery

    1–2

    Un logo ke saath baithte hai jo asli kaam karte hai — sirf management ke saath nahi. Asli requirement wahin milti hai jise wo saalon se chupchaap jhel rahe hai.

  2. 2

    Blueprint aur estimate

    1–2

    Scope, module list, integration, timeline aur fix cost. Ye document aap kisi doosre vendor ko bhi dikha sakte hai — wahi to matlab hai iska.

  3. 3

    Design

    2–3

    Development se pehle chalne wali screen. Is stage par screen badalne me ek ghanta lagta hai; banne ke baad ek hafta.

  4. 4

    Development

    4–16

    Do-do hafte ke sprint, har sprint ke end me chalta hua demo. Progress lagatar dikhta hai, aakhir me surprise nahi milta.

  5. 5

    Testing aur UAT

    2–3

    Go-live se pehle aapki team asli data par chalati hai. Har issue likhit me log hota hai aur band hota hai.

Aksar puchhe jaane wale sawaal

Hamara data public model ko train karne me jaayega?

Nahi. Ya to API-based model use karte hai jahan aapka data training me nahi jaata, ya self-hosted model jab data infrastructure se bahar jaana hi nahi chahiye. Kaunsa tareeka lagega, ye kaam shuru hone se pehle tay hota hai.

AI ne galat kar diya to?

Paise se juda ya na-palatne wala kaam aadmi ki confirmation se hi hota hai. AI draft banata hai, insaan approve karta hai. Jahan bharosa kam ho, wo case apne aap aadmi ke paas chala jaata hai.

Kharche ka faayda hai ya nahi, ye kaise pata chalega?

Pehle manual process ka baseline banate hai — kitne ghante, kitni galti, kitna kharch. Agar automation usse naap kar behtar na kare, to hum bana hi nahi dete, saaf keh dete hai.

Hindi ya regional bhasha ke document padh lega?

Haan, mili-juli bhasha aur haath se likhe document bhi. Haath ki likhaai par accuracy alag-alag hoti hai, isliye commit karne se pehle aapke asli document par test karte hai.

Software se nahi, problem se shuru kariye

45 minute ki call. Dekhte hai manual kaam sach me kahan hai, aur batate hai ki kuch banana zaroori bhi hai ya nahi. Koi kharcha nahi, koi obligation nahi.

Agar aapke liye custom software galat jawab hai, to usi call par saaf bata denge.

WhatsApp Call karein