Enterprise AI agent development
AI agents and conversational products built for real operations.
TrishiAI helps companies design, ship, and improve AI agents for support, operations, and customer experience, with delivery across custom stacks and Google conversational platforms.
7+ years
Software and product delivery experience
6+ years
Dialogflow ES/CX implementation work
Enterprise-ready
Focus on governed, production-grade AI systems
Operating lanes
Product delivery for teams that need more than a demo.
Strategy, conversation design, integrations, orchestration, and post-launch iteration are treated as one delivery system.
AI agents
Retrieval, tools, policies, and evaluations designed around a workflow that actually matters.
Google conversational stack
Support across Conversational Agents, Dialogflow CX, Agent Assist, and CX Agent Studio.
Enterprise fit
Delivery considers handoff, integrations, observability, and stakeholder trust from the start.
Product polish
Interfaces and backend orchestration are built together so the end product feels usable, not experimental.
Naming note
Google now surfaces Dialogflow CX under Conversational Agents. The site uses both terms intentionally so enterprise buyers can recognize current and legacy platform language.
Core services
A company site built around delivery, not generic AI claims.
The revamp centers the core buying motions for enterprise teams: product clarity, platform expertise, implementation depth, and a direct path to discovery.
AI Agent Development
Design and build AI agents that support operations, customer experience, and internal teams with real business workflows.
Google Conversational Platform Development
Implement customer and support experiences across Google conversational products, including current and legacy Dialogflow naming.
Support and CX Automation
Build assistants for customer service, case deflection, triage, and guided support operations.
Google specialization
Explicit support for Google conversational and CX agent platforms.
The site now gives Google-platform work its own section instead of burying it in a generic expertise list.
Conversational Agents (Dialogflow CX)
Build and refine structured conversation systems using Google’s current Conversational Agents stack while keeping Dialogflow CX language visible for buyer familiarity.
- Flows and pages
- Fulfillment
- Voice and chat channels
Google Conversational Agent Platform
Support enterprise teams adopting Google conversational tooling for support journeys, automation, and customer-facing agent experiences.
- Channel integration
- Routing logic
- Operational handoff
Agent Assist
Design live-assist experiences that help support agents with guidance, retrieval, and structured next actions during customer interactions.
- Knowledge assist
- Suggested responses
- Operational efficiency
CX Agent Studio
Support development for newer Google CX agent experiences, including agent setup, tools, deployment planning, and evaluation-oriented delivery.
- Instructions
- Tools and sub-agents
- Monitoring and iteration
Use cases
Focused on workflows companies actually buy.
Support, operations, internal knowledge, and lead-handling are clearer demand drivers than abstract AI capability language.
Customer support automation
Resolve repeat questions, route cases intelligently, and improve containment with AI-led support journeys.
Operations copilots
Give teams fast access to SOPs, internal knowledge, and action-oriented assistants tied to real systems.
Sales and lead qualification
Capture requirements, qualify inbound demand, and move prospects into the right workflow with conversational experiences.
Internal knowledge assistants
Unify fragmented documentation and business data into assistants that teams can use without searching across tools.
Delivery process
A clearer path from scoping to live operation.
The new structure keeps process visible, because enterprise trust comes from understanding how the work will be delivered.
Step 1
Discover
Align on use case, audience, integration constraints, and the business outcome that defines success.
Step 2
Architect
Choose the right agent pattern, platform, data sources, and control points before implementation starts.
Step 3
Build
Implement conversation design, orchestration, interfaces, integrations, and evaluation loops together.
Step 4
Launch and improve
Measure performance, identify failure modes, and iterate toward stronger containment, accuracy, and usability.
Start the conversation
Plan the right AI agent build before you commit to implementation.
Share the workflow, platform requirements, and delivery timeline. The primary conversion path now leads directly into a discovery conversation instead of marketplace profiles.