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Custom Agentic AI for Financial Advisors
Unlock higher advisory productivity and operational efficiency while boosting ROI. Automate routine tasks and speed client onboarding. Drive deeper engagement with AI-powered personalization and real-time insights of custom AI agents. Seamlessly integrated with your CRM, this solution fuels revenue growth and strengthens your firm’s competitive edge.
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How Agentic AI Eliminates Advisory Inefficiencies
Eliminate advisory inefficiencies with custom Agentic AI—boost productivity and unlock new revenue.
Advisors and teams spend hours on low-value activities—report generation, status tracking, follow-ups, portfolio summaries. We deploy AI agents that handle recurring tasks like data gathering, report prep, meeting summarization, and compliance checks—freeing up your team to focus on advisory work and revenue generation.
- Automates data collection and report generation from multiple systems
- Summarizes meetings, tracks task status, and flags compliance gaps
- Syncs with CRM and planning tools for real-time portfolio insights
Clients expect tailored advice, but advisors can’t manually personalize interactions across hundreds of relationships. Our machine learning models analyze portfolio data, engagement history, and behavioral patterns to generate personalized insights, recommendations, and outreach at scale.
- Generates real-time, client-specific recommendations based on portfolio and behavior
- Prioritizes outreach based on engagement signals and life-stage indicators
- Integrates with CRM to trigger personalized messages and nudges automatically
Advisors answer identical basic questions dozens of times daily while complex client situations sit unaddressed. Our custom autonomous workflow agents powered by LLMs handle routine inquiries and appointment scheduling—freeing advisors to focus on high-value, strategic conversations.
- Handles routine inbound queries via chat, email, and voice with 24/7 availability
- Escalates nuanced or urgent cases to human advisors with context-rich summaries
- Continuously learns from interactions to improve accuracy and reduce repeat queries
Client service teams are overwhelmed by repetitive inquiries—balance checks, transaction statuses, account updates.We build custom Voice & Chat AI Agents that operate 24/7 across email, voice, and chat channels—reducing costs, shortening wait times, and freeing your team to focus on complex, high-impact issues.
- Deflects up to 80% of Tier 1 support queries with accurate, real-time responses
- Supports omnichannel integration for seamless handoff between chat, voice, and email
- Provides analytics on query patterns to inform product, CX, and staffing decisions
Real-Life Example: How Agentic AI Cut Advisor Workload and Boosted Upsells
21% Growth in Client Acquisition for U.S. Financial Advisory Firm
An American financial advisory company specializing in digital investment services needed to stand out in a crowded market. Their challenge: deliver smarter, more adaptive advice to a growing client base—without increasing advisor headcount.
They turned to a custom Agentic AI solution that:
- Leveraged machine learning to automatically rebalance portfolios based on real-time market shifts
- Activated intelligent “next-best actions” like tax-loss harvesting and risk reassessment triggers
- Delivered scalable personalization using client behavior and portfolio data
Results:
- 25% growth in client acquisition, fueled by differentiated AI-powered advisory
- Improved investment outcomes through more responsive and efficient portfolio management
- Increased retention, thanks to timely, personalized engagement and proactive financial insights
12% Increase in Upsells for Mid-Sized Wealth Management Firm
A mid-sized U.S. wealth management firm with around 60 employees needed a smarter way to surface personalized investment recommendations and cross-sell opportunities—without expanding its internal analytics team.
They partnered with a software engineering company to implement a custom AI-driven advisor recommendation engine that:
- Used machine learning to analyze client demographics, current holdings, and real-time market data
- Suggested portfolio adjustments tailored to each client’s profile and market conditions
- Surfaced cross-selling opportunities through an intuitive dashboard of “next best actions”
Results:
- 40% reduction in manual research time for advisors
- 12% increase in upsell success, particularly in retirement products
- Enhanced decision-making with a streamlined, AI-powered advisor interface
34% Time Savings for Advisors at Multi-State RIA
An independent Registered Investment Advisor (RIA) with ~80 employees operating across several U.S. states needed a way to reduce support overhead while maintaining a high-touch client experience.
They partnered with a nearshore software development firm to deploy a custom AI-powered chatbot and NLP engine that:
- Handled routine client inquiries—like balance checks, performance updates, and appointment scheduling—through natural language
- Supported advisors with fast access to market data and compliance checklists
- Integrated seamlessly with the firm's CRM to track and log all chatbot interactions
Results:
- 30% reduction in support tickets within the first quarter post-launch
- 32–37% time savings for advisors on routine queries and lookups
- Strengthened brand positioning as a forward-thinking, tech-enabled advisory firm
15% Increase in Client Acquisition for Wealth Planning Startup
A Canadian wealth planning startup with fewer than 50 employees was preparing to expand into the U.S. market and needed a more adaptive way to assess client risk—beyond static questionnaires.
They partnered with a software development provider to implement an AI-enhanced risk profiling engine that:
- Automatically adjusted risk tolerance based on behavioral signals like trade frequency and cash flow patterns
- Incorporated external market sentiment data to alert advisors of potential risk shifts
- Delivered dynamic, data-driven risk assessments to improve portfolio alignment
Results:
- Manual re-checking of risk profiles reduced by over 60%
- 15% increase in new client acquisition, driven by unique “adaptive risk” positioning
- Stronger advisor-client trust through timely, personalized portfolio adjustments
Agentic AI in Financial Advisory Schema
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Client relationships need humans. Data entry doesn't.
Automate routine tasks with agentic AI in financial advisory services.
Unlock 25%+ Growth Potential in Your Advisory Business
Deploy custom Agentic AI to cut advisor workload by up to 40%, increase upsell conversion by 12–15%, and deliver real-time, hyper-personalized advice at scale. Automate what slows you down. Focus your team on what drives revenue.
Aleksandr Sheremeta
Solution Expert
AI in Financial Advisory Implementation Across Sectors
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Wealth Management
- Auto-trigger portfolio rebalancing when allocations drift beyond set thresholds
- Analyze client life events to suggest tax-loss harvesting and estate planning updates
- Screen alternative investments and flag hidden risks from multiple data sources through AI for financial advisors
Retirement Planning
- Calculate required distributions and catch 401(k) contribution violations automatically
- Run Monte Carlo simulations to update retirement readiness scores in real time
- Guide IRA rollovers by comparing fees and executing transfer paperwork with AI in financial advisory services
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Investment Advisory
- Scan earnings reports and SEC filings to surface investment strategies, themes, and risks
- Execute trades automatically when technical or fundamental criteria trigger
- Generate performance attribution reports by sector and security selection, using AI in financial advisory
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Risk Management
- Analyze client finances to identify insurance coverage gaps and recommend amounts
- Monitor regulatory changes and scan communications for compliance red flags
- Expedite insurance claims by pulling documents and coordinating with carriers with an AI-powered financial advisory
Financial Planning for SMBs
- Forecast cash flow needs by analyzing receivables, payables, and seasonal patterns
- Compare employee compensation packages and calculate tax implications of adjustments, leveraging AI for financial advisors
- Model business expansion scenarios with industry benchmarks and financing requirements
Robo-Advisory Services
- Assess risk tolerance through behavioral analysis beyond standard questionnaires
- Execute trades across thousands of accounts while optimizing for taxes and costs, using AI in financial advisory services
- Personalize content based on individual portfolio performance and market conditions
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Emotion Tracker
For a banking institute, we implemented an advanced AI-driven system using machine learning and facial recognition to track customer emotions during interactions with bank managers. Cameras analyze real-time emotions (positive, negative, neutral) and conversation flow, providing insights into customer satisfaction and employee performance. This enables the Client to optimize operations, reduce inefficiencies, and cut costs while improving service quality.
They delivered a successful AI model that integrated well into the overall solution and exceeded expectations for accuracy.
Client Identification
The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.
The team has met all requirements. DATAFOREST produces high-quality deliverables on time and at excellent value.
Entity Recognition
The online marketplace for cars wanted to improve search for users by adding full-text and voice search, as well as advanced search with specific options. We built a system application using Machine Learning and NLP methods to process text queries, and the Google Cloud Speech API to process audio queries. This helped greatly improve the user experience by providing a more intuitive and efficient search option for them.
Technically proficient and solution-oriented.
All Success Stories
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Your best advisors are wasting time on basic math.
Fix that through AI-Powered Financial Advisory.
Key Steps to Creating an AI-Powered Financial Advisory
Process Audit
We map your advisors ' tasks using workflow analysis tools and time-tracking software because most firms don't know what eats their time.
01
Data Integration
We connect your scattered systems into one data pipeline because AI for financial advisors can't work with information it can't access. APIs and custom middleware are used.
02
Model Training
We teach AI for financial advisors using your client conversations and advisor decisions because generic models don't understand your specific compliance requirements. Machine learning frameworks and domain datasets are used.
03
Agent Development
We build specialized AI-powered financial advisory workers for different tasks because one agent can't handle portfolio analysis and client chat equally well. Multi-agent frameworks and task orchestration tools are used.
04
Compliance Testing
We validate every decision of AI in financial advisory against financial regulations because getting fined costs more than taking the time to test correctly. Regulatory compliance software and audit trails are used.
05
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Pilot Launch
We start with your most experienced advisors because they catch problems faster and won't blame the technology when something breaks. Controlled rollout management and feedback systems are used within AI for financial advisors.
06
Production Scaling
We expand to your whole team only after we know how to fix issues quickly because broken AI in financial advisory services hurts client relationships permanently. Monitoring dashboards and automated fail-safe systems are used.
07
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FAQ
What types of advisory tasks can be automated with conversational AI?
Basic fact-finding, portfolio monitoring, and routine client updates are the most reliable candidates for automation right now. Due to their complexity and regulatory requirements, more nuanced tasks like estate planning or tax strategy still need significant human oversight.
Can Agentic AI handle complex financial queries or only basic tasks?
Current AI for financial advisors can handle structured queries about market data, portfolio performance, and basic risk assessments, but struggles with contextual judgment calls or multi-factor decisions. The technology excels at quickly processing large amounts of data but lacks the intuition and experience needed for complex financial strategies.
How long does it take to deploy and train an Agentic AI solution for advisors?
Initial deployment typically takes 3-6 months, assuming your data is clean and properly structured—if not, add another 2-4 months for data preparation. Training advisors to use it effectively and integrate it into their workflow usually requires another 1-2 months, with ongoing adjustments based on real-world usage.
Does AI replace human advisors or simply enhance their productivity?
AI for financial advisors currently functions best as a productivity tool that handles routine tasks and provides data-driven insights, letting advisors focus on relationship-building and complex decision-making. The technology isn't ready to replace human judgment in high-stakes financial decisions, especially when dealing with unusual circumstances or emotional client situations.
What kinds of insights can Agentic AI surface from client interactions?
AI in financial advisory services can identify patterns in client behavior, risk tolerance shifts, and potential life events that might be affected by AI in financial planning. The real value comes from spotting these signals early, but human advisors must still validate and act on these AI-driven insights appropriately.
Can AI solutions personalize financial advice in real-time?
AI-powered financial advisory can adjust recommendations based on predefined parameters and historical data, but true personalization still requires human oversight to account for nuanced personal circumstances. Real-time personalization is limited to relatively safe areas like fundamental portfolio rebalancing and investment alerts.
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