How to Evaluate Vendors Systematically Using AI Research Agents

Your team needs a new CRM. Or a cloud provider. Or a design tool. The evaluation starts optimistically: someone creates a spreadsheet, people throw in vendor names, and then... nothing happens for two weeks until someone asks for a recommendation based on vibes.

Sound familiar? Here's how AI agents turn vendor evaluation from a sprawling mess into a structured, repeatable process.

The Evaluation Pipeline

DEFINE    → Set evaluation criteria and weight them
RESEARCH  → Gather data on each vendor
SCORE     → Rate vendors against criteria
RECOMMEND → Final comparison with recommendation

Step 1: Define Criteria

Start by creating a structured evaluation framework:

@PM create a document titled "CRM Vendor Criteria" with:

Evaluation criteria for a CRM serving a 50-person sales team:
1. Pricing (weight: 25%) - Total cost for 50 seats, contract terms
2. Features (weight: 30%) - Pipeline management, reporting, automation
3. Ease of Use (weight: 20%) - Onboarding time, UI complexity
4. Support (weight: 15%) - Response time, channels, documentation
5. Integration (weight: 10%) - API quality, existing integrations

Vendors to evaluate: HubSpot, Salesforce, Pipedrive, Close, Freshsales

Then create a database to store scores:

@PM create a database called "Vendor Evaluation - CRM" 
with columns: vendor, pricing_score, features_score, 
ease_of_use_score, support_score, integration_score, 
total_score, recommendation

Step 2: Research Each Vendor

Create research tasks for each vendor:

@Researcher for each vendor in the CRM Vendor Criteria document,
create a subtask and research:
- Current pricing for 50 seats (monthly and annual)
- Key features and missing features from our requirements
- G2/Capterra ratings and common complaints
- Integration options with our existing stack
- Support SLA and channels
- Recent news (acquisitions, outages, pivots)

Write findings into a document titled "[Vendor] - CRM Evaluation"

The agent creates 5 research sub-tasks, works through each systematically, and produces a document for every vendor with sourced data — not opinions.

Step 3: Score and Compare

With research complete, populate the scoring database:

@Analyst review each vendor evaluation document and score 
them in the Vendor Evaluation database:
- Score each category 1-10 based on the research
- Calculate total_score as weighted average
- Set recommendation to "strong yes", "yes", "maybe", or "no"

Step 4: Final Recommendation

Generate the decision document:

@Writer create a document titled "CRM Vendor Recommendation":

1. Executive Summary — recommended vendor and why (2 sentences)
2. Scoring Table — all vendors side by side
3. Top Pick — detailed analysis, risks, implementation timeline
4. Runner-Up — why it's second
5. Not Recommended — brief explanation for each
6. Next Steps — trial, POC, contract negotiation

Why This Beats Spreadsheets

Traditional vendor evaluation fails because:
- Research is shallow — People skim websites instead of deep-diving
- Scores are subjective — Without criteria, it's opinion vs. opinion
- Documentation is scattered — Notes in Slack, docs in Drive, scores in sheets
- Nobody owns the timeline — Evaluation drags on for weeks

With AI agents:
- Research is thorough — Agents read reviews, check pricing pages, find recent news
- Scores are data-backed — Each score links to specific findings
- Everything is in one place — Documents, databases, and discussions in the same workspace
- Work happens in parallel — Multiple vendors researched simultaneously

Beyond Software Vendors

This pattern works for any evaluation:
- Agency/consultant selection — Portfolio, expertise, pricing, references
- Cloud infrastructure — Performance, geographic availability, cost modeling
- Insurance providers — Coverage, premiums, claims process, reputation
- Office space — Location, amenities, lease terms, growth flexibility

Get Started

Saltare's agents, documents, and databases give you everything you need for structured vendor evaluations. No spreadsheets, no scattered docs — just a clear process from criteria to recommendation.

Get started free →