AI Agents
AI Agents
We build production AI agents that automate support and back-office workflows - grounded in your knowledge base and integrated into existing systems through APIs. The result is not “just a chatbot,” but an operational layer that routes tickets, drafts documents, retrieves answers with sources, and escalates to humans when needed - delivered with access control, audit logs, and monitoring.
Client
NDA
Industry
Professional Services
Year
2025
Services Provided
AI Agents · RAG Knowledge Search · Workflow Automation · API Integration · Observability
Key Challenges®
//04
AI agents only deliver value when they fit real workflows. The challenge was building agents that are accurate, safe, and easy to adopt—while integrating into existing tools and keeping human control where it matters.
Reliable answers from messy knowledge
We had to unify scattered documents and policies into a retrieval layer that returns source-backed responses.
//01
Permissions and sensitive data
The agents had to respect roles, access boundaries, and confidentiality across teams and systems.
//02
Workflow integration, not a new tool
The agents needed to live inside existing systems—triggering actions via API instead of adding manual steps.
//03
Operational reliability
Observability, audit logs, feedback loops, and escalation flows were required to run safely in production.
//04
Key Challenges®
//04
AI agents only deliver value when they fit real workflows. The challenge was building agents that are accurate, safe, and easy to adopt—while integrating into existing tools and keeping human control where it matters.
Reliable answers from messy knowledge
We had to unify scattered documents and policies into a retrieval layer that returns source-backed responses.
//01
Permissions and sensitive data
The agents had to respect roles, access boundaries, and confidentiality across teams and systems.
//02
Workflow integration, not a new tool
The agents needed to live inside existing systems—triggering actions via API instead of adding manual steps.
//03
Operational reliability
Observability, audit logs, feedback loops, and escalation flows were required to run safely in production.
//04
Key Challenges®
//04
AI agents only deliver value when they fit real workflows. The challenge was building agents that are accurate, safe, and easy to adopt—while integrating into existing tools and keeping human control where it matters.
Reliable answers from messy knowledge
We had to unify scattered documents and policies into a retrieval layer that returns source-backed responses.
//01
Permissions and sensitive data
The agents had to respect roles, access boundaries, and confidentiality across teams and systems.
//02
Workflow integration, not a new tool
The agents needed to live inside existing systems—triggering actions via API instead of adding manual steps.
//03
Operational reliability
Observability, audit logs, feedback loops, and escalation flows were required to run safely in production.
//04
Design Approach®
//004
We took an engineering-first approach: define workflows and success criteria, ground outputs in sources, enforce access control, then integrate into real systems with monitoring from day one.
Workflow mapping and success criteria
We identified high-volume tasks and defined where the agent assists versus where humans decide.
//01
RAG knowledge layer with sources
We built retrieval and ranking to reduce hallucinations and keep answers grounded in approved content.
//02
API-driven automations
We connected agents to helpdesk/CRM/internal tools to create, route, tag, and draft with consistent outputs.
//03
Design Approach®
//004
We took an engineering-first approach: define workflows and success criteria, ground outputs in sources, enforce access control, then integrate into real systems with monitoring from day one.
Workflow mapping and success criteria
We identified high-volume tasks and defined where the agent assists versus where humans decide.
//01
RAG knowledge layer with sources
We built retrieval and ranking to reduce hallucinations and keep answers grounded in approved content.
//02
API-driven automations
We connected agents to helpdesk/CRM/internal tools to create, route, tag, and draft with consistent outputs.
//03
Design Approach®
//004
We took an engineering-first approach: define workflows and success criteria, ground outputs in sources, enforce access control, then integrate into real systems with monitoring from day one.
Workflow mapping and success criteria
We identified high-volume tasks and defined where the agent assists versus where humans decide.
//01
RAG knowledge layer with sources
We built retrieval and ranking to reduce hallucinations and keep answers grounded in approved content.
//02
API-driven automations
We connected agents to helpdesk/CRM/internal tools to create, route, tag, and draft with consistent outputs.
//03
Final Outcome
//04
The outcome is a production-ready AI agent layer that reduces repetitive work and accelerates operational workflows. Teams get faster triage, better knowledge access, and consistent outputs—delivered through existing tools with controlled access, auditability, and monitoring.
4
Core agent workflows delivered
2
Core agent workflows delivered
24
/7
Monitoring and audit logs
1
Unified knowledge layer (RAG)
“We didn’t want a chatbot. We needed automation that fits our real workflows. The agents were integrated into our tools, respected access boundaries, and delivered consistent results we could actually operate.”
— Anika Chauhan, Co-Founder & CPO
Final Outcome
//04
The outcome is a production-ready AI agent layer that reduces repetitive work and accelerates operational workflows. Teams get faster triage, better knowledge access, and consistent outputs—delivered through existing tools with controlled access, auditability, and monitoring.
4
Core agent workflows delivered
2
Core agent workflows delivered
24
/7
Monitoring and audit logs
1
Unified knowledge layer (RAG)
“We didn’t want a chatbot. We needed automation that fits our real workflows. The agents were integrated into our tools, respected access boundaries, and delivered consistent results we could actually operate.”
— Anika Chauhan, Co-Founder & CPO
Final Outcome
//04
The outcome is a production-ready AI agent layer that reduces repetitive work and accelerates operational workflows. Teams get faster triage, better knowledge access, and consistent outputs—delivered through existing tools with controlled access, auditability, and monitoring.
4
Core agent workflows delivered
2
Core agent workflows delivered
24
/7
Monitoring and audit logs
1
Unified knowledge layer (RAG)
“We didn’t want a chatbot. We needed automation that fits our real workflows. The agents were integrated into our tools, respected access boundaries, and delivered consistent results we could actually operate.”
— Anika Chauhan, Co-Founder & CPO
