Complete Proposal: Build Your Own - Conversational Agent Solution

What is Conversational Agent Solution Standard 25h?

A comprehensive implementation service that deploys AI-powered conversational agents directly into your existing enterprise workflow applications. This solution transforms the Conversational Agent Consultancy concept into a practical, working system integrated with your daily business tools.

Core Integration Options

Choose from your current environment:

Microsoft Ecosystem

• Microsoft Teams (chat interfaces, bots, adaptive cards)

• Microsoft Calendar (scheduling assistants, meeting intelligence)

• Microsoft Copilot (extended capabilities)

• Microsoft Power Apps (low-code agent integration)

Google Workspace

• Google Chat & Spaces

• Google Calendar

• Google Workspace Add-ons

• Google AppSheet integration

Zoom Workspace

• Zoom Team Chat (bot integration)

• Zoom Apps (embedded agent experiences)

• Zoom Meeting intelligence and summaries

• Zoom Phone integration for voice-enabled agents

WhatsApp Business

• WhatsApp Business API integration

• Automated customer support conversations

• Rich media messaging (images, documents, buttons)

• Integration with CRM and business systems

• 24/7 customer engagement

Technical Architecture

Agent Framework Options

Microsoft Agent Framework (Azure Bot Service)

• Seamless Azure integration, enterprise security

• Bot Framework SDK with adaptive cards

• Power Virtual Agents integration

• Azure Active Directory authentication

Google Agent Framework (Dialogflow CX)

• Native Google Workspace connectivity

• Advanced conversation design tools

• Google Cloud integration

• Workspace APIs for Calendar, Gmail, Drive

Llama Stack

• Meta's open-source agentic AI platform

• Standardized API for building AI agents

• Framework-agnostic architecture with interoperable components

• Self-hosted or cloud deployment options

• Community-driven with growing ecosystem

• Supports multiple LLM providers (Llama, Mistral, etc.)

LLM & Inference Selection

High-Performance Inference Providers

Groq

• Ultra-fast inference with LPU™ (Language Processing Unit)

• 500+ tokens/second throughput

• Sub-second response times for real-time conversations

• Cost-effective for high-volume deployments

• Supports: Llama 3.1, Mixtral, Gemma models

• Ideal for: Customer-facing chatbots, real-time assistants, high-concurrency scenarios

Together AI

• Wide model selection (50+ open-source models)

• Competitive pricing and performance

• Fast inference optimization

• Supports: Llama 3, Mixtral, Qwen, DeepSeek models

Fireworks AI

• Optimized for speed and cost

• Function calling and structured outputs

• Enterprise-grade reliability

• Supports: Llama, Mixtral, Gemma, custom fine-tuned models

Replicate

• Simple API for hundreds of models

• Pay-per-use pricing

• Easy model experimentation

• Good for prototyping and varied model needs

Enterprise LLM Providers

Anthropic (Claude)

• Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku

• Superior reasoning and safety features

• 200K context window

• Best-in-class for complex analysis and business consultancy

• Enterprise security and compliance

OpenAI

• GPT-4o, GPT-4 Turbo, GPT-3.5

• Extensive ecosystem and tooling

• Function calling and assistants API

• Proven track record at scale

Google Vertex AI

• Gemini 1.5 Pro, Gemini 1.5 Flash

• Native Google Workspace integration

• 1M+ token context window

• Multimodal capabilities (text, images, video)

Azure OpenAI Service

• Enterprise-grade OpenAI models

• Microsoft security and compliance

• Private deployment in your Azure tenant

• SLA guarantees and dedicated capacity

AWS Bedrock

• Multiple model providers in one platform

• Claude, Llama, Mistral, Amazon Titan

• Serverless inference

• Pay-per-use with enterprise security

Open-Source & Self-Hosted Models

IBM Granite

• Enterprise-focused, commercially safe

• Available in multiple sizes (3B, 8B, 13B, 34B)

• Trained on business and code data

• On-premise deployment option

• Strong for regulated industries

Meta Llama 3.1 / 3.2

• Llama 3.1: 8B, 70B, 405B parameter options

• Llama 3.2: 1B, 3B (optimized for edge/mobile), 11B, 90B (vision-language models)

• Open weights, commercially usable

• Strong general capabilities

• Can be self-hosted or used via Groq/Together

Mistral/Mixtral

• Mistral 7B, Mixtral 8x7B, Mixtral 8x22B

• European provider with GDPR focus

• Apache 2.0 license

• Excellent performance/cost ratio

• Available via Mistral API or self-hosted

Qwen 2.5

• Alibaba's open model family

• Strong multilingual capabilities

• 0.5B to 72B parameters

• Excellent for Asian language support

DeepSeek

• DeepSeek-V2, DeepSeek-Coder

• Cost-effective, high-performance

• Strong coding capabilities

• Chinese provider with global availability

Phi-3

• Microsoft's small language models

• 3.8B, 7B, 14B parameters

• Efficient for on-device deployment

• Optimized for enterprise scenarios

Self-Hosted Inference Stacks

Ollama

• Local model deployment made simple

• Run Llama, Mistral, Gemma locally

• Docker-based, easy setup

• Perfect for development and testing

vLLM

• High-throughput, memory-efficient inference

• PagedAttention optimization

• Industry-standard for production deployments

• Continuous batching for efficiency

TGI (Text Generation Inference)

• Hugging Face's production server

• Optimized for popular models

• Streaming support, token streaming

• Docker-ready deployment

LM Studio

• Desktop application for local models

• User-friendly interface

• Great for testing and prototyping

• Windows, Mac, Linux support

LlamaFile

• Single-file executable models

• No dependencies, runs anywhere

• Mozilla/Justine Tunney project

• Perfect for air-gapped environments

Multi-Agent Orchestration

Workflow agents communicate with each other to handle complex business processes:

• Handoff between specialized agents - Sales, support, analytics agents collaborate

• Parallel processing - Multi-step workflows executed simultaneously

• Contextual memory sharing - Agents share context across the network

• Hierarchical routing - Master agent delegates to specialized sub-agents

• Event-driven triggers - Agents activate based on business events

Inference Strategy Selection Matrix

Use Case Recommended Provider Alternative Options Real-time customer chat Groq (Llama 3.1) Fireworks AI, Together AI Complex reasoning Anthropic Claude GPT-4o, Gemini Pro High volume, cost-sensitive Groq, Together AI Mistral API, Llama self-hosted Maximum privacy Self-hosted (vLLM + Llama) IBM Granite on-premise Google Workspace native Vertex AI (Gemini) Self-hosted with Workspace APIs Microsoft ecosystem Azure OpenAI Anthropic, Groq Multilingual Qwen, GPT-4o Claude, Gemma Regulated industries IBM Granite, Azure OpenAI Self-hosted Llama with vLLM Code generation Claude, DeepSeek-Coder GPT-4o, Llama 3.1 405B

Product Tiers

Standard Edition (25h implementation)

Inference Options:

• Cloud-based: Groq, Anthropic, OpenAI, Mistral API

• Single model deployment

• API-based integration

• Standard rate limits

Deliverables:

• Single conversational agent

• One primary integration (Teams, Google Chat, Zoom, or WhatsApp)

• Basic workflow automation

• Standard enterprise security

• Documentation and training materials

Protected Edition (40h implementation)

Inference Options:

• Azure OpenAI (private deployment)

• AWS Bedrock (VPC deployment)

• Self-hosted vLLM + Llama/Mistral

• IBM Granite on-premise

• Private endpoint configuration

• Custom rate limits and capacity

Additional Features:

• Enhanced data governance

• Advanced encryption and access controls

• GDPR/regulatory compliance features

• Audit logging and monitoring

• Private data isolation

• VPC/private cloud deployment

• SOC 2 Type II alignment

Note: Self-hosted deployments may require additional hours (typically +5h) for infrastructure setup and configuration.

Enterprise Multi-Agent (60h implementation)

Inference Options:

• Multiple providers with automatic routing

• Groq for speed + Claude for reasoning

• Fallback chains (Groq → OpenAI → Claude)

• A/B testing infrastructure

• Cost optimization routing

• Load balancing across providers

Advanced Capabilities:

• Multi-agent orchestration system

• Complex workflow automation

• Multiple workspace integrations (2-3 platforms)

• Advanced analytics and monitoring

• Custom agent personalities and behaviors

• Inter-agent communication protocols

Enterprise Full Ecosystem (100h+ implementation)

Comprehensive Solution:

• Complete multi-agent ecosystem

• Integration across all major platforms (Teams, Google, Zoom, WhatsApp)

• Advanced multi-model routing and optimization

• Custom agent development (4-6 specialized agents)

• Enterprise-grade monitoring and analytics

• Full DevOps/MLOps pipeline setup

• Comprehensive training program for teams

• Dedicated support during rollout

Advanced Features:

• Advanced workflow orchestration with complex business logic

• Integration with multiple backend systems (CRM, ERP, databases)

• Custom model fine-tuning options

• Multi-region deployment

• Disaster recovery and high availability setup

• Advanced security and compliance frameworks

• Change management support

• Executive dashboards and reporting

Cost Comparison (Approximate)

Per Million Tokens Pricing

Provider/Model Input Output Speed Best For Groq Llama 3.1 70B $0.59 $0.79 ⚡⚡⚡⚡⚡ High volume, real-time Together AI Llama 3.1 70B $0.88 $0.88 ⚡⚡⚡⚡ Balanced cost/speed Claude 3.5 Sonnet $3.00 $15.00 ⚡⚡⚡ Complex reasoning GPT-4o $2.50 $10.00 ⚡⚡⚡ General purpose Mistral Large $2.00 $6.00 ⚡⚡⚡⚡ European data residency Gemini 1.5 Pro $1.25 $5.00 ⚡⚡⚡ Long context Self-hosted Llama Infrastructure only Infrastructure only ⚡⚡ Maximum control

Implementation Options by Infrastructure

Option 1: Pure Cloud (Standard 25h)

• Groq/Anthropic/OpenAI API

• Microsoft/Google/Zoom workspace integration

• No infrastructure management

• Fastest deployment

• Timeline: 2-3 weeks

Option 2: Hybrid Cloud (35h)

• Cloud inference for primary operations

• Local backup/fallback capabilities

• Data stays on-premise when needed

• Best of both worlds approach

• Timeline: 3-4 weeks

Option 3: Self-Hosted (45h)

• vLLM + Llama/Mistral deployment

• Full data sovereignty

• Custom infrastructure setup

• Complete control over deployment

• GPU infrastructure configuration

• Container orchestration setup

• Timeline: 4-6 weeks

Note: Self-hosted deployments require additional infrastructure costs (GPU servers, storage, networking). Can be combined with Protected Edition (40h) for a total of ~45-50h depending on complexity.

Detailed Deliverables Breakdown

Standard Edition (25h)

Discovery & Architecture (8h)

• Inference provider selection workshop (2h)

  • Evaluate business requirements

  • Compare provider capabilities

  • Estimate usage costs

• Performance vs. cost analysis (1h)

  • Model benchmarking

  • ROI calculations

• Security & compliance review (2h)

  • Data governance requirements

  • Regulatory compliance check

  • Access control design

• Architecture design (3h)

  • System architecture diagram

  • Integration architecture

  • Deployment strategy

Development & Integration (12h)

• LLM/inference integration (4h)

  • API setup and configuration

  • Authentication and security

  • Rate limiting and error handling

• Multi-provider routing (if applicable) (2h)

  • Fallback logic implementation

  • Load balancing setup

• Prompt engineering & optimization (3h)

  • System prompts development

  • Context management

  • Response optimization

• Workspace platform integration (3h)

  • Teams/Chat/Zoom/WhatsApp setup

  • Bot registration and permissions

  • UI/UX implementation

Testing & Optimization (5h)

• Performance benchmarking (2h)

  • Response time testing

  • Throughput analysis

  • Load testing

• Cost optimization tuning (1h)

  • Token usage optimization

  • Caching strategies

• User acceptance testing (1h)

  • Test scenarios execution

  • Bug fixes and refinements

• Documentation (1h)

  • Technical documentation

  • User guides

  • Maintenance procedures

Protected Edition (40h)

Includes all Standard Edition deliverables (25h) plus:

Advanced Security Setup (8h)

• Private deployment configuration (3h)

  • VPC/private endpoint setup

  • Network security configuration

  • Firewall and access rules

• Data governance implementation (2h)

  • Data classification setup

  • Encryption configuration (at-rest and in-transit)

  • Data residency compliance

• Compliance framework setup (2h)

  • Audit logging configuration

  • GDPR/regulatory compliance checks

  • Security monitoring setup

• Access control & authentication (1h)

  • Advanced IAM policies

  • Multi-factor authentication

  • Role-based access control

Enhanced Testing & Documentation (7h)

• Security testing (3h)

  • Penetration testing coordination

  • Vulnerability scanning

  • Security audit preparation

• Compliance testing (2h)

  • Regulatory compliance validation

  • Data handling verification

• Advanced documentation (2h)

  • Security runbooks

  • Compliance documentation

  • Incident response procedures

Enterprise Multi-Agent (60h)

Includes all Protected Edition deliverables (40h) plus:

Multi-Agent Orchestration (12h)

• Agent architecture design (3h)

  • Define specialized agents (sales, support, analytics, etc.)

  • Design inter-agent communication protocols

  • Workflow mapping

• Multi-agent development (6h)

  • Implement 2-3 specialized agents

  • Handoff logic between agents

  • Context sharing mechanisms

• Orchestration layer (3h)

  • Master agent / router implementation

  • Load balancing across agents

  • Priority and queue management

Advanced Integration (5h)

• Multiple platform integrations (3h)

  • Add 2nd workspace platform

  • Unified user experience

  • Cross-platform data sync

• Backend system integration (2h)

  • CRM/ERP connections

  • Database integrations

  • API middleware setup

Advanced Monitoring & Optimization (3h)

• Analytics dashboard (2h)

  • Agent performance metrics

  • Usage analytics

  • Cost tracking

• Optimization tuning (1h)

  • Multi-model routing optimization

  • Cost-performance balancing

Enterprise Full Ecosystem (100h+)

Includes all Enterprise Multi-Agent deliverables (60h) plus:

Comprehensive Agent Development (20h)

• Development of 4-6 specialized agents

• Complex workflow automation

• Advanced business logic implementation

• Custom integrations with legacy systems

Full Platform Integration (10h)

• Integration across 4+ platforms

• Unified authentication and user management

• Cross-platform data synchronization

• Mobile and desktop optimization

Enterprise Infrastructure (10h)

• Multi-region deployment

• High availability and disaster recovery

• DevOps/MLOps pipeline setup

• CI/CD for agent updates

Training & Change Management (5h)

• Executive training sessions

• Team training workshops

• Change management support

• User adoption programs

Ongoing Optimization & Support (5h included, additional as needed)

• Performance tuning and optimization

• Monthly strategy reviews

• Feature enhancements planning

• Quarterly business reviews

Key Value Propositions

Immediate ROI

• Reduce response time from hours to seconds

• Handle 10x more inquiries without additional staff

• 24/7 availability across all time zones

• First response within seconds, not hours

Seamless Integration

• Works within tools employees already use daily

• No separate platforms or logins required

• Minimal change management needed

• Familiar user experience

Scalable Intelligence

• Starts with one agent, grows to multi-agent ecosystems

• Scales from pilot team to entire organization

• Handles growing data volumes automatically

• Pay only for what you use

Future-Ready Architecture

• Framework-agnostic design allows LLM switching

• Modular design supports easy feature additions

• Built for emerging AI capabilities

• Protection from vendor lock-in

Data-Driven Decisions

• AI processes vast amounts of data instantly

• Decisions based on quantifiable evidence

• Real-time insights from enterprise data sources

• Integration with external knowledge bases

Use Case Examples

1. IT Helpdesk Agent (Teams/Zoom)

• Answers technical questions instantly

• Creates support tickets automatically

• Routes complex issues to human specialists

• Provides step-by-step troubleshooting guides

• Impact: 70% reduction in Level 1 support tickets

2. Sales Assistant (Teams/WhatsApp)

• Provides instant product information

• Generates personalized quotes

• Schedules meetings via Calendar integration

• Qualifies leads automatically

• Impact: 40% faster sales cycle

3. HR Onboarding Agent (Google Chat/Teams)

• Guides new employees through onboarding

• Answers policy and benefits questions

• Routes to HR specialists when needed

• Tracks onboarding completion

• Impact: 60% reduction in onboarding time

4. Customer Support (WhatsApp Business)

• 24/7 customer inquiry handling

• Order status and tracking

• Product recommendations

• Seamless handoff to human agents

• Impact: 85% of queries resolved without human intervention

5. Data Analysis Agent (Teams/Google Chat)

• Queries business intelligence systems

• Generates on-demand reports

• Provides insights and recommendations

• Natural language data exploration

• Impact: 10x faster access to business insights

Estimated Total Work Hours

Package Core Hours Timeline Optional Enhancements Standard 25h 25h base 2-3 weeks +5-10h per additional integration Protected 40h 40h base 3-4 weeks +5h for self-hosted infrastructure Enterprise Multi-Agent 60h 60h base 4-6 weeks +10h per additional agent Enterprise Full Ecosystem 100h+ 100h+ 6-8 weeks Custom scope based on needs

Ongoing Support Options

• Monthly retainer: 4-8h/month for optimization, updates, and enhancements

• Training sessions: 2h per user group

• New agent development: 10-15h per specialized agent

• Performance optimization: 5h per quarter

• Security audits: 8h annually

Technical Requirements

From Client

Access & Permissions:

• Microsoft 365 / Google Workspace / Zoom admin access

• Application registration permissions

• WhatsApp Business API access (if applicable)

Data Sources:

• SharePoint, Google Drive access

• Database connection credentials

• API endpoints for business systems

• CRM/ERP integration requirements

Documentation:

• Security and compliance policies

• Data classification guidelines

• User access requirements

• Existing system architecture

Infrastructure Requirements

Cloud Deployment (Standard/Protected):

• Azure, GCP, or AWS account (optional, for advanced features)

• LLM provider API keys (Anthropic, OpenAI, Groq, etc.)

• Workspace application registration

• Monitoring tools (Application Insights or equivalent)

Self-Hosted Deployment (Protected + Self-Hosted):

• GPU servers for inference (A100/H100 recommended for high performance)

• Container orchestration (Kubernetes or Docker Swarm)

• Load balancing infrastructure

• Storage for models and logs

• Network security (firewalls, VPN)

Recommendation Framework

For High-Speed Requirements: Start with Groq
For Complex Analysis: Use Claude 3.5 Sonnet
For Budget-Conscious: Together AI or Groq
For Maximum Privacy: Self-hosted vLLM + Llama
For Enterprise Microsoft: Azure OpenAI
For Enterprise Google: Vertex AI Gemini
For Regulated Industries: IBM Granite or Azure OpenAI (private)
For Customer-Facing (WhatsApp): Groq for speed + Claude for complex queries
For Multilingual Support: Qwen 2.5 or GPT-4o

Why Choose This Solution?

✓ Rapid Deployment - Working agent in 25 hours, not months
✓ Proven Frameworks - Built on enterprise-grade platforms
✓ Flexible Technology - Choose your preferred LLM and framework
✓ Practical AI - Embedded in daily workflows, not standalone tools
✓ Protected Options - Enterprise security for sensitive industries
✓ Agile Approach - Iterative development with continuous feedback
✓ Cost Transparency - Clear pricing, predictable costs
✓ Scalable Architecture - Grows with your business needs

Next Steps

1. Discovery Call (1h - No charge)

• Discuss your specific use case and requirements

• Identify integration points and priorities

• Review security and compliance needs

• Estimate usage and costs

2. Architecture Workshop (2h)

• Design your agent solution

• Select optimal LLM and inference provider

• Define success metrics

• Create implementation roadmap

3. Pilot Sprint (25h/40h/60h/100h+)

• Deploy your first conversational agent(s)

• Train your team

• Gather feedback and iterate

• Measure initial results

4. Expand & Optimize

• Scale based on results and feedback

• Add additional agents or integrations

• Optimize for performance and cost

• Continuous improvement

Investment

Package Price Range (€) Best For Standard 25h €2,500 - €3,500 Single agent, cloud-based Protected 40h €4,000 - €5,500 Regulated industries, enhanced security Enterprise Multi-Agent 60h €6,000 - €8,500 Complex workflows, multiple agents Enterprise Full Ecosystem 100h+ €10,000 - €15,000+ Full ecosystem transformation

Ongoing Support: €400-800/month (retainer)
Additional Integrations: €500-1,000 per integration
Training: €200 per session
Self-Hosted Infrastructure Setup: +€500-1,500 (typically +5h)

Pricing varies by region, complexity, and infrastructure requirements. Infrastructure and LLM API costs are additional.

Security & Compliance

Data Protection

• GDPR compliance by design

• Data encryption in transit and at rest

• Access control and authentication

• Audit logging and monitoring

• Data residency options (EU, US, Asia)

Certifications & Standards

• SOC 2 Type II alignment

• ISO 27001 compatible

• HIPAA-ready (Protected Edition)

• PCI DSS considerations

• Industry-specific compliance support

Privacy Features

• On-premise deployment options

• Data anonymization capabilities

• User consent management

• Right to deletion support

• Transparent AI decision-making

Success Metrics

We measure success through:

• Response Time: Average time to first response

• Resolution Rate: % of queries resolved without human intervention

• User Satisfaction: CSAT scores from agent interactions

• Cost Savings: Reduction in support costs

• Adoption Rate: % of team actively using the agent

• Accuracy: Quality of responses and recommendations

• Scalability: System performance under load

Frequently Asked Questions

Q: Can we start with one platform and expand later?
A: Yes! The Standard 25h package focuses on one integration. Additional platforms can be added for 5-10h each.

Q: What's the difference between Protected Edition (40h) and Self-Hosted option (45h)?
A: Protected Edition (40h) includes enhanced security features and can use various deployment options (cloud or self-hosted). The Self-Hosted option (45h) specifically refers to on-premise infrastructure deployment, which typically requires the base Protected Edition plus additional infrastructure setup time (~5h additional).

Q: What if we want to switch LLM providers later?
A: Our architecture is provider-agnostic. Switching typically requires 3-5h of reconfiguration.

Q: Do you provide training for our team?
A: Yes! Training is included. Additional sessions available at €200 per 2-hour session.

Q: What happens after the initial implementation?
A: We offer ongoing support retainers (4-8h/month) for optimization, updates, and new features.

Q: Can the agent access our confidential data?
A: Data access is controlled by your permissions. Protected Edition offers enhanced security for sensitive data.

Q: How quickly can we get started?
A: After the discovery call and architecture workshop, implementation takes 2-6 weeks depending on the package.

Q: What's included in the Enterprise Full Ecosystem (100h+) package?
A: This comprehensive package includes 4-6 specialized agents, integration across all major platforms, advanced multi-agent orchestration, enterprise infrastructure setup, comprehensive training, and dedicated rollout support. It's designed for organizations ready for complete AI transformation.

Contact & Next Steps

Ready to transform your business with AI agents?

📅 Schedule a free discovery call to discuss your needs
📧 Email us for questions or detailed proposals
🌐 Visit our website for case studies and demos

Timeline Summary:

• Discovery Call: 1 hour (this week)

• Architecture Workshop: 2 hours (next week)

• Implementation: 2-8 weeks (depending on package)

• Go-Live: Your AI agent working in production

This proposal is valid for 90 days. Pricing and timelines may vary based on specific requirements, infrastructure needs, and LLM provider changes. All implementations include 30 days of post-deployment support.

Version: 1.1
Date: October 2025
Last Updated: Critical consistency corrections applied
Next Review: Updates quarterly with new LLM and framework options

Document Changelog

Version 1.1 (Current)

• ✅ Corrected package naming consistency (Enterprise Multi-Agent 60h)

• ✅ Added Enterprise Full Ecosystem (100h+) to Product Tiers section

• ✅ Clarified difference between Protected Edition (40h) and Self-Hosted option (45h)

• ✅ Added detailed deliverables breakdown for all packages (40h, 60h, 100h+)

• ✅ Enhanced Llama Stack description with official Meta platform details

• ✅ Updated Llama model references (3.1 and 3.2 with specifications)

• ✅ Added FAQ clarifying package differences

• ✅ Improved consistency across all pricing and hour estimates.