Case studies/CS_03/2025

Deploying a RAG-powered cognitive assistant that automated 75% of internal ops Q&A.

75%
Automated Ops Q&A
14
Integrated SaaS Sources
+64
Internal Team NPS
<1.1s
Median Response Latency
Client
Synapse AI
Industry
AI Productivity / Enterprise
[ Brief ]

The short version

Enterprise operations teams were losing hundreds of hours searching across 14 fragmented SaaS tools. We engineered a vector-search RAG agent integrated natively into Slack with strict enterprise role-based permissions.

Services
RAG ArchitectureAgentic WorkflowsEnterprise AI Security
Stack
OpenAI APIPineconeTypeScriptCloudflare WorkersSlack API
01

The Challenge

A rapidly growing 50-person operations team was repeatedly answering the same operational queries across disparate channels. Employee onboarding ramp times were escalating and internal team satisfaction scores were dropping.

02

Architectural Approach

Instead of building a separate portal, we brought the intelligence directly into Slack where engineers and ops staff work daily. The assistant indexes 14 enterprise document repositories into Pinecone vector spaces every 15 minutes.

03

Engineering & Guardrails

Security guardrails enforce user identity token validation at query time — ensuring staff only receive contextual answers derived from files they already have explicit permissions to view.

04

Business Impact

The cognitive agent resolves 75% of operational inquiries without human escalation, maintaining median answer latency under 1.1 seconds and saving over 30 hours per employee every month.

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