One governed AI Suite. Two layers: what runs it, and what runs on it.
From Sanjal Enterprises in New Delhi: a Foundation layer every agent shares — gateway, orchestration, retrieval, observability, LLMOps and private deployment — plus an Application layer of purpose-built agents your teams actually use.
Foundation vs. Application
Every agent in the Application layer is composed from Foundation layer services — it does not maintain its own model access, retrieval pipeline or audit trail. That is what keeps 25+ agents governable by one team instead of 25 separate integrations.
Application Layer
Purpose-built agents your teams use directly
Foundation Layer
Governed infrastructure shared by every agent above
MCP / A2A-compatible. Foundation-layer tools and data sources are exposed to agents through the Model Context Protocol (MCP), and agents hand off to one another using Agent-to-Agent (A2A) messaging. Third-party and custom agents can connect to the same Foundation layer without proprietary connectors, and Application-layer agents are not locked to a single orchestration engine.
The infrastructure every agent shares
Six components, each independently useful, together forming the governed substrate for the Application layer.
Enterprise AI Gateway
A single, governed entry point for every model, prompt and AI service used across the organization.
FoundationAgentic Orchestration Platform
Coordinates multi-step, multi-agent workflows across tools, data sources and human approval points.
FoundationRAG & Knowledge Retrieval
Grounds every AI response in an organization's own documents, records and policies.
FoundationAI Governance & Observability
Continuous visibility into what every agent did, why, and whether it stayed inside policy.
FoundationLLMOps
The lifecycle discipline for evaluating, versioning, deploying and rolling back models and prompts safely.
FoundationSovereign / Private AI Deployment
Runs models inside an organization's own environment when residency, sovereignty or isolation requirements rule out shared public endpoints.
Agents built on the Foundation layer
Grouped by function. Every agent below inherits gateway access, retrieval, orchestration and governance from the Foundation layer — none of them run as an unmonitored, standalone script.
Employee Enablement
Enterprise AI Assistant
A company-aware assistant for every employee, grounded in internal knowledge and connected to core systems.
Employee EnablementAI Meeting Assistant
Turns meetings into structured notes, decisions and follow-up actions automatically.
Employee EnablementAI Email Assistant
Drafts, triages and routes email using company context, with a human always in the approval loop.
Employee EnablementAI HR Assistant
Answers policy and benefits questions and supports HR processes, with clear boundaries around sensitive decisions.
Customer Engagement
AI Chatbot Platform
Deploys grounded, brand-consistent conversational agents across web, mobile and messaging channels.
Customer EngagementAI Voice Agent
Handles inbound and outbound voice calls with natural conversation and clean handoff to human agents.
Customer EngagementAI Customer Support
Resolves support tickets across channels using product knowledge, account context and defined resolution actions.
Customer EngagementConversational Analytics
Turns every customer conversation — chat, voice and email — into structured, searchable insight.
Sales & Revenue
AI Sales Copilot
Prepares reps for every call, drafts follow-ups, and keeps CRM records current automatically.
Sales & RevenueAI Proposal Generator
Assembles accurate, on-brand proposals from approved content in a fraction of the manual drafting time.
Sales & RevenueAI Contract Review
Flags risk, non-standard clauses and missing terms before a contract reaches legal.
Every agent is auditable by default
- Every prompt, tool call and output is logged and attributable to a team, application and user.
- High-impact actions require a configured human approval checkpoint before execution.
- Model and prompt changes go through evaluation and staged rollout before reaching production.
- Regulated workloads can run on private, sovereign infrastructure under the same governance model.
Designed for regulated environments
PII and secrets redaction runs at the gateway before a prompt reaches any model. Retrieval never bypasses source-system permissions. Red-team and drift evaluations run on a recurring schedule against production agents, not only at launch.
See AI Governance & Observability →Scope the right combination for your environment
Most engagements start with the Foundation layer and one or two Application-layer agents against a defined use case, then expand.