Vendor briefings in under five minutes, across billions in annual spend

VIBE, the Vendor Intelligence & Briefing Engine, brings procurement data, market signals, financial health, and research for 20+ strategic vendors into one AI-powered dashboard.

Outcome

Analyst research down about 70%, from 15 to 20 hours a week to 4 to 6, vendor briefings generated in under five minutes, spend visible in real time, and an estimated $4M to $10M+ in annual cost optimization.

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At a glance

Client & industry

Fortune 20 global technology and digital platform company, managing billions in annual spend across some of the world's largest technology and services vendors

Engagement

Design, build, and run an AI-powered vendor intelligence platform, from strategy and architecture through deployment and continuous enhancement

Scale

20+ strategic vendors at launch, built to scale to 100+, across cloud infrastructure, enterprise software, AI platforms, consulting, and networking

Business owner

The client's vendor strategy and operations team

Maps to below services

Data Engineering & Advanced Analytics, AI & ML Engineering

Where it started

Intelligence on vendor relationships worth billions, spread across five disconnected systems.

Business problem

Analysts spent 15 to 20 hours a week per vendor pulling data together by hand, and a single briefing took two to three business days. Negotiations and renewals ran on outdated information, and adding vendor coverage required proportional headcount growth as the portfolio expanded.

No decision framework

Financial, procurement, research, news, and spreadsheet data sat in separate places with no single source of truth. Spend by business unit or cost center arrived in monthly reports, so overspend, unfavorable renewals, vendor financial risk, and pricing shifts surfaced weeks or months after they mattered.

Agivant's role

Engineering partner across strategy, architecture, dashboard design, full-stack development, LLM integration, and knowledge transfer

What Agivant built

Agivant worked with vendor strategy leadership to tie every feature to a business outcome, then built the full platform on the client's in-house large language model. VIBE writes vendor research on demand, pulls live spend from the procurement system, and refreshes its own content before it goes stale. Value in days and weeks.

Unified intelligence dashboard

Complete profiles for 20+ strategic vendors, with highlights, briefings, spend detail, and research in one tabbed view and instant vendor switching.

AI-powered research automation

The in-house LLM writes executive summaries, strategic briefs, financial health assessments, technology roadmaps, competitive positioning, and risk signals.

Real-time spend intelligence

Live POs, cost-center filtering, and active and year-to-date spend straight from the procurement system, with one-click export for finance.

Proactive intelligence engine

Checks content freshness every 24 hours, regenerates anything stale, and alerts analysts to material changes.

Retrieval and knowledge layer

RAG retrieval over a vendor knowledge graph, vector database, and document store, feeding QBR reports and an AI query interface.

How it is built

Frontend	AI engine	Knowledge store	Data ingestion	Financial data	Caching	Controls
Next.js, React, TypeScript, Recharts	The client's in-house large language model, at zero external AI cost	RAG retrieval, vendor knowledge graph, vector database, document store	Manus AI orchestration, document parsing, API connectors, research and financial feeds	Enterprise procurement system via GraphQL	Generated-output cache with a five-day lifetime	SSO, role-based access control, secret management

Before

Five-plus disconnected systems, manual research, and spend visible only in monthly reports.

After

One dashboard backed by an in-house LLM, live procurement data, a knowledge layer, and a cache-first request flow with scheduled refresh.

Proof in production

An analyst opens a vendor and gets highlights, a briefing, spend detail, and research in one view. A cache hit returns instantly. On a miss, the AI engine combines the LLM with live web search, generates the brief, and caches it for the next request. In the background, a scheduler checks freshness every 24 hours and regenerates anything stale before anyone asks for it.

Measured results

Coverage now grows with the vendor portfolio, without proportional headcount growth.

~70% down

Weekly research effort per analyst

Under 5 min

Vendor briefing, previously 2 to 3 business days

Real time

Spend visibility, previously monthly reports

5+ to 1

Systems consolidated into one platform

20+

Vendors per analyst, up from 2 to 3

$4M to $10M+

Estimated annual cost optimization

The repeatable pattern

Reusable asset

The cache-first generation pattern, prompt-governed LLM output with scheduled freshness checks, carries to any intelligence domain where research goes stale, and coverage scales from 20+ vendors to 100+ on the same platform. Every build amplifies the next.

Challenges overcome

Running core intelligence on the client's in-house LLM at zero external AI cost, bringing live procurement data into a research tool while keeping sensitive spend behind role-based access, and handing over documentation and runbooks so the client team owns the platform long term.

Enterprise movement

Foundational

Operational

Path to autonomous