1. Abstract

This use case presents a revolutionary AI-driven platform that redefines how Chartered Accountants deliver consultancy. At its core is a face-cloned AI avatar of CA Shubham Patel, trained on over 18 hours of real video data. This avatar offers real-time, multilingual, context-aware advice, replicating the voice, tone, expression, and consulting intelligence of a real CA.

Through the integration of:

1. 🧠 Multi-Agent Architectures (Planner, Executor, Memory),
2. 🗣️ Real-Time Voice Interaction,
3. 📈 Investment Advisory using a Seasonality Indicator,
4. 📚 Modular Prompt Libraries, and
5. 🔁 Realtime Data APIs,

the system simulates authentic, personalized, and highly scalable expert consultancy. It is accessible 24x7, inclusive across regional languages, and extensible into various domains.

2. Problem Statement

The average CA today is:

1. Overburdened with client demands during due dates.
2. Repeating answers to queries that could be automated.
3. Unable to advise across time zones or after hours.
4. Pulled into domains beyond compliance, such as investment advisory.

🧠 Real-Life Scenario:

Clients now expect guidance on:

1. “When should I buy TCS?”
2. “Which sector is trending in Q3?”
3. “Can I claim capital gains benefit if I sell in March?”

This type of investment plus compliance guidance, in multiple languages, with real-time data — is neither scalable nor humanly sustainable.

3. The Core Innovation: A India’s First Face-Cloned CA AI Interactive Realtime Avatar

At the heart of this use case is a realistic, custom AI avatar of CA Shubham Patel — fully trained with:

This cloned avatar appears on a Webflow-based consultancy portal where users can ask tax, compliance, or investment-related questions — anytime.

4. Flow of Interaction: How It Works

👨‍💼 Client/User Journey

1. User selects language + avatar
2. Speaks query (e.g., “Should I buy Infosys this month?”)
3. Avatar processes input:
4. 🧠 Planner agent routes the question to prompt library
5. ⚙ Executor agent fetches real-time stock + tax data
6. 📚 Memory agent applies context and voice tone
7. Avatar answers:
8. In the selected language
9. With realistic expression and voice
10. Citing facts from PDFs, market APIs, and custom logic

🧠 Code-enhanced Insight: The avatar also uses a custom-coded Seasonality Indicator, explained below.

5. Seasonality Indicator – Technical Integration for Investment Advisory

A core differentiator of this solution is the integration of technical investment intelligence within the avatar’s responses using a custom-coded Seasonality Indicator in Pine Script (TradingView).

💡 Purpose

To advise clients not just what to buy, but when to buy or sell based on historical monthly trends and win ratios.

🛠️ Code Implementation

1. ✅ Written in Pine Script v5
2. ✅ Deployed on TradingView as "Seasonality Monthly v2.0"
3. ✅ Pulls 10+ years of monthly stock return data
4. ✅ Outputs a matrix showing:
5. Monthly returns by year
6. AVG, SUM, +ive counts
7. WR – Win Ratio (% of positive years per month)

Sample Avatar Use:

🗣️ “TCS shows a strong October performance with a win ratio of 77.78%. Historically, this has been a good entry month.”

📊 Output Table Screenshot – NSE:TCS

This output powers the avatar to convert complex quantitative trends into digestible, spoken insights for clients.

6. Multi-Agent AI Architecture

To simulate intelligent consultancy decisions, the system uses a multi-agent framework:

These agents work together in real-time, orchestrated via WebRTC, GitHub Workflows, and Webflow front-end.

7. Prompt Library – Modular & Domain-Based Intelligence

The solution features a structured Prompt Library curated over multiple verticals, such as:

This library enables the avatar to simulate domain-specific CA expertise and adapt answers depending on context.

8. Tools & Technologies Used

This solution brings together LLMs, speech, frontend, and trading APIs into one seamless AI platform:

9. Real-Life Consultancy Simulation (Webflow)

The full solution was deployed as a voice-enabled AI Avatar Consultancy Hub on Webflow, featuring:

1. ✅ Welcome screen with avatar selection
2. ✅ Language options (English, Hindi, Gujarati, Marathi)
3. ✅ Voice input for client questions
4. ✅ Avatar delivers responses with emotion, tone, and hand gestures
5. ✅ Realtime screen feedback (price trend, tax advice, investment flow)

Microphone & audio enhancements were added to simulate a real-world interaction — turning a static CA website into an interactive consultancy room.

10. Impact on CA Fraternity & Clients

11. Social Impact & Way Forward

Your vision extends this innovation into wider societal use:

Future additions include integration with Bhashini API for full Indian language support and WhatsApp-based avatar interaction.

12. Final Innovation Summary

13. Conclusion

The Grand Finale solution is not just an upgrade—it’s a paradigm shift.

It transforms a proof-of-concept AI avatar into a fully immersive, agent-powered, real-time consultancy system with a cloned human persona, real-time investment advisory, domain-specific intelligence, and voice-based multilingual delivery.

This layered innovation pushes the boundary of how CAs, clients, and citizens can interact with expertise—anytime, anywhere.

As the first-ever realistic interactive CA avatar trained to simulate professional consultation using:

1. AI Agents
2. Realistic expression
3. Prompt-based intelligence
4. Real-time voice interaction
5. Custom Pine Script outputs for stock analysis,

this project marks a significant leap in Tech x CA integration—redefining advisory services in India.

Yours,

CA Shubham Patel

Reference Code for Seasonality Indicator:

Seasonality Indicator

PASTE THE BELOW CODE IN TRADINGVIEW PINE EDITOR

CODE

//@version=5

indicator("Seasonality Monthly v2.0", "Seasonality Monthly v2.0", format = format.volume)

if not timeframe.ismonthly and not timeframe.isdaily

runtime.error("Please switch timeframe to either Daily or Monthly")

i_year_start = input(2000, "Start Year")

// i_method = input.string("time", "Method", options=["time","time_close"])

i_method = "time_close"

i_text_size = input.string(size.auto, "Text Size", [size.auto, size.tiny, size.small, size.normal, size.large, size.huge])

//--------------------------------------------------------Functions----------------------------------------------------

f_array_stats(array_) =>

   count_pos_ = 0

   count_neg_= 0

   count_ = 0

   sum_ = 0.0

   if not na(array_) and array.size(array_) > 0

       for i_ = 0 to array.size(array_) - 1

           elem_ = array.get(array_, i_)

           if not na(elem_)

sum_ += elem_

count_ += 1

switch

elem_ > 0 => count_pos_ += 1

elem_ < 0 => count_neg_ += 1

   avg_ = count_ > 0 ? sum_ / count_ : 0.0

   [sum_, avg_, count_, count_pos_, count_neg_]

//-------------------------------------------------------------------------------------------------------------------   

[year_, month_, chg_pct_] = request.security(syminfo.tickerid, "M", [year(time_close), month(time_close), nz(close/close[1]-1)], gaps = barmerge.gaps_on, lookahead = barmerge.lookahead_on )

// month_ = switch i_method

//"time"      => month(time)

//"time_close" => month(time_close)

// year_ = switch i_method

//"time"      => year(time)

//"time_close" => year(time_close)

var year_start_ = math.max(year_, i_year_start)

var no_years_ = year(timenow) - year_start_ + 1

var matrix<float> data_ =matrix.new<float>(no_years_,13,na)

var table table_ = na

var text_color_ = color.white

var bg_color_ = color.gray

// chg_pct_ = nz(close/close[1]-1)

if year_ >= year_start_

   // log.info(str.format("Time {0} Year {1} Month {2} ChgPct {3,number,#.##%}", str.format_time(time_close), year_, month_, chg_pct_))

   cur_val_ = nz(matrix.get(data_, year_-year_start_, month_-1))

matrix.set(data_, year_-year_start_, month_-1, cur_val_ + chg_pct_)

// if barstate.islast

//log.info("ROWS {0}", no_years_ + 7 )

if barstate.islast

   table_ := table.new(position.middle_center, 13, no_years_ + 7, border_width = 1)

   // log.info("ROWS {0}", year_ - year_start_ + 7 )

table.cell(table_, 0, 0, str.format("Seasonality Monthly Performance - {0}:{1}", syminfo.prefix, syminfo.ticker), text_color = text_color_, bgcolor = color.blue, text_size = i_text_size)

table.merge_cells(table_, 0,0,12,0)

   row = 1

   table.cell(table_, 0, row, "Year", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 1, row, "Jan", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 2, row, "Feb", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 3, row, "Mar", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 4, row, "Apr", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 5, row, "May", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 6, row, "Jun", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 7, row, "Jul", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 8, row, "Aug", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

   table.cell(table_, 9, row, "Sep", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 10, row, "Oct", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 11, row, "Nov", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 12, row, "Dec", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

   for row_ = 0 to no_years_ - 1

table.cell(table_, 0, 2+row_, str.tostring(row_ + year_start_), text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

       for col_ = 0 to 11

           val_ = nz(matrix.get(data_, row_, col_),0.0)

val_color_ = val_ > 0.0 ? color.green : val_ < 0.0 ? color.red : color.gray

table.cell(table_, 1+col_, 2+row_, str.format("{0,number,###.##%}", val_), bgcolor = color.new(val_color_,80), text_color = val_color_, text_size = i_text_size)

           true

   //Aggregates

   row_ = no_years_ + 2

table.cell(table_, 0, row_, "AVG", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 0, row_+1, "SUM", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 0, row_+2, "+ive", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

table.cell(table_, 0, row_+3, "WR", text_color = text_color_, bgcolor = bg_color_, text_size = i_text_size)

   for col_ = 0 to 11

       arr_ = matrix.col(data_, col_)

       // val_ = array.sum(arr_)

       [sum_, avg_, count_, count_pos_, count_neg_] = f_array_stats(arr_)

       val_color_ = sum_ > 0 ? color.green : sum_ < 0 ? color.red : color.gray

table.cell(table_, 1+col_, row_, str.format("{0,number,###.##%}", avg_), bgcolor = color.new(val_color_,50), text_color = val_color_, text_size = i_text_size)

table.cell(table_, 1+col_, row_+1, str.format("{0,number,###.##%}", sum_), bgcolor = color.new(val_color_,50), text_color = val_color_, text_size = i_text_size)

table.cell(table_, 1+col_, row_+2, str.format("{0}/{1}", count_pos_, count_), bgcolor = color.new(val_color_,50), text_color = color.new(color.white, 50), text_size = i_text_size)

table.cell(table_, 1+col_, row_+3, str.format("{0,number,#.##%}", count_pos_/count_), bgcolor = color.new(val_color_,50), text_color = color.new(color.white, 50), text_size = i_text_size)

       true

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