Time to Value
3–6 months typical
Integration architecture, middleware setup, data mapping, testing cycles
Deloitte: 65% of AI projects take 6+ months to deploy
4–8 months typical
Custom code, developer hiring, sprint cycles, QA, security reviews
McKinsey: 54% cite skills gaps as primary delay
Under 1 hour
Install managed package → build agent with AI → deploy. No code, no developers, no middleware.
Total Cost of Ownership
Unpredictable
Per-user fees + API overage charges + middleware licensing + ongoing integration maintenance
Forrester: TCO is the #2 concern in enterprise AI adoption
High & hidden
Developer salaries + platform licensing + maintenance burden + technical debt accrual
IDC: Hidden costs add 40–60% to initial AI budgets
Transparent & predictable
Org/month credit model — pay for what you use, not per user. Built-in credit management and optimization tools for complete visibility. Paid plans from $1,500/org/month (annual). Try free: 2,000 credits, no credit card.
Governance Burden
Significant
Data leaves your org → separate DPA, security review for each vendor, ongoing compliance monitoring
Thomson Reuters: 79% worry about AI compliance challenges
Self-managed
You build it, you govern it — custom audit logging, custom access controls, custom incident procedures
PwC: 68% cite security as top barrier to enterprise AI
Architectural — built in
100% native: inherits your FLS, sharing rules, profiles. Full audit trail. Configurable guardrails. Zero data egress.
Developer Dependency
High
Requires integration architects + middleware specialists + ongoing API maintenance
Very high
Requires full development team — custom code, testing, deployment pipelines, version control
McKinsey: 54% of orgs lack the skills to build AI in-house
Zero
Designed for Salesforce Admins. No-code agent builder. AI auto-generates prompts and context from your schema.
Pilot Success Rate
Low
Complex integrations stall at POC stage; scope creep on middleware and data mapping
Gartner: 77% of AI projects fail to reach production
Low
Internal builds lack executive patience; 85% of pilots never scale
MIT Sloan: 85% of AI pilot projects fail to scale
High
Install-to-deploy in under an hour means pilots prove value in the first meeting, not the first quarter.
Enterprise Scalability
Constrained
Scaling requires more API calls, more middleware capacity, more integration points to manage
Fragile
Each new use case = new development effort. Technical debt compounds with every addition.
Native scale
Add agents in minutes. 8 channels, 12 tools, 5 knowledge types. Scales with your Salesforce org — not against it.
Time to Value
External3–6 months — Integration + middleware
DIY4–8 months — Custom dev + QA cycles
REDA AIUnder 1 hour — Install → build → deploy
Total Cost of Ownership
ExternalUnpredictable — Per-user + API + middleware
DIYHigh & hidden — Dev salaries + tech debt
REDA AITransparent — Org/month, built-in optimization
Governance Burden
ExternalSignificant — Data leaves org, extra DPAs
DIYSelf-managed — You build it, you govern it
REDA AIBuilt in — Inherits Salesforce security
Developer Dependency
ExternalHigh — Integration architects needed
DIYVery high — Full dev team required
REDA AIZero — Designed for Admins
Pilot Success Rate
ExternalLow — Stalls at POC stage
DIYLow — 85% fail to scale
REDA AIHigh — Proves value in first meeting
Enterprise Scalability
ExternalConstrained — More API, more middleware
DIYFragile — Tech debt compounds
REDA AINative scale — Grows with your org
