Stage 3: AI-Native Products

Timeline: 3-6 Months

Once our internal house is in order (Stage 1 & 2), we aggressively pivot to delivering AI value to our clients. This stage is about mastering the art of building “AI-Inside” products—applications that don’t just “use” AI, but are built around AI capabilities.

Why This Stage Matters

This is where Techvify generates new revenue streams and distinguishes itself in the market. We stop being just a “software vendor” and become a “strategic AI partner.”

Client Value

We help clients move from “Chatting with AI” to “Automating Business Processes with AI Agents.”

Key Activities & Deliverables

Activity	Description	Deliverable
Use Case Discovery	Workshops to identify high-value AI opportunities for clients.	AI Opportunity Radar & Feasibility Report.
RAG & Vector Search	Standardizing architectures for Knowledge connectivity.	Techvify RAG Boilerplate (Azure/OpenAI).
Copilot Development	Building custom copilots for specific business roles.	Custom Copilot MVP deployments.
Agentic Workflows	Implementing autonomous agents using Semantic Kernel/LangChain.	Multi-Agent Systems for complex tasks.

Tools & Platforms

• Frameworks: LangChain, Semantic Kernel, AutoGen.
• Vector DBs: Qdrant, Pinecone, Azure AI Search.
• Evaluation: RAGAS (RAG Assessment), LangSmith.

Product Delivery Approach

We use a specialized “AI Product Framework”:

1. Discovery & Data Audit: Asses client data readiness and privacy requirements.
2. PoC (Proof of Concept): 2-week sprint to validate the model’s feasibility on client data.
3. MVP (Minimum Viable Product): Production-ready core loop with human-in-the-loop safeguards.
4. Scale & Optimize: Fine-tuning, prompt optimization, and cost management.

Reference Architecture Overview

graph TD
    User[Client User] -->|Query| UI[Application UI]
    UI -->|API Request| Orch[Orchestrator Service]
    
    subgraph Brain [The AI Brain]
    Orch <-->|Retrieve Context| Vector[Vector DB]
    Orch <-->|Tool Execution| Tools[External APIs/SQL]
    Orch -->|Prompt + Context| LLM[Azure OpenAI / GPT-5 or Claude Opus 4.5]
    end
    
    LLM -->|Response| Orch
    Orch -->|Answer| UI
    
    style Orch fill:#f9f,stroke:#333
    style LLM fill:#bbf,stroke:#333

Success Metrics

• Revenue: 20% of new projects involve AI components.
• Capabilities: Proven capability to deliver RAG, Copilots, and Agents.
• Client Satisfaction: Measurable ROI for clients (e.g., “Customer support costs reduced by 30%”).

Real Example: “The Legal Contracts Analyser”

Client Need: A legal firm spends 100s of hours reviewing NDAs. Our Solution: An AI Agent that:

1. Reads PDF/Word contracts.
2. Compares them against the firm’s standard “Playbook”.
3. Highlights risky clauses and suggests redlines.

Outcome: Review time cut from 2 hours to 10 minutes per contract.