Should you build or buy AI for RFPs?
Compare the true 3-year cost of building an in-house AI response platform versus buying AutoRFP.ai — including engineering time, maintenance, time to value, and win-rate impact.
36-month comparison horizon
Your RFP workload
How many responses you run and what expert time costs.
Responses per year
responses
Hours per response
hours
Blended internal hourly cost
$/hour
Current win rate
%
Build scenario
In-house platform engineering, rebuilds, and upkeep.
Engineers allocated
engineers
Fully-loaded engineer cost
$/year
Initial build time
months
Expected rebuild cycles
cycles
Months per rebuild
months
Ongoing maintenance FTE
FTE
Cloud & model cost
$/month
Buy scenario
AutoRFP.ai subscription, implementation, and admin time.
AutoRFP.ai annual subscription
$/year
Implementation effort
weeks
Internal admin time
hrs/month
Performance assumptions
Editable benchmarks — tune time reduction and win-rate uplift for each path.
Build time reduction
%
Editable benchmark
AutoRFP.ai time reduction
%
Editable benchmark
Build win-rate uplift
pts
AutoRFP.ai win-rate uplift
pts
How this calculation works
- Build 3-year cost = initial build labour + rebuild labour + maintenance FTE + cloud/model spend after v1.
- Buy 3-year cost = AutoRFP.ai subscription × 3 years + implementation labour (weeks × 40 hrs × blended rate) + admin labour over 36 months.
- Build time to value = initial build months + (rebuild cycles × months per rebuild). Buy time to value = implementation weeks ÷ 4.
- Capacity gained = responses/year ÷ 12 × months Buy is live earlier. Hours gained applies AutoRFP.ai’s time-reduction rate. Annual hours saved once live = responses × hours/response × each path’s time-reduction rate.
- Projected win rates = current win rate + each path’s uplift (capped at 100%). Wins influenced use each path’s active months within the 36-month horizon.
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