Qantus: precision quoting in minutes
How I designed Qantus to move from slow and error-prone quotes to a fast flow with measurable outcomes.
By Mauro Sánchez 5 min read
#qantus
#saas
#pricing-engine
#b2b
In many sales teams, quoting is still slow, manual, and error-prone.
Qantus was built to change that.
The problem
- Scattered pricing variables.
- Human errors when calculating fees.
- Delays in proposal generation.
- Lower deal-closing speed.
My role
Strategic definition, architecture design, and full-stack technical execution.
What I built
- A configurable quoting rules engine.
- Comparative scenarios for faster decisions.
- Ready-to-send proposal templates.
- An optimized flow that converts in minutes.
Impact
- ±0.3% average error margin.
- 120+ connected branches.
- Average conversion in around 3 minutes.
Key lesson
When a commercial flow is mission-critical, the best UX is not the prettiest one.
It’s the one that removes friction, reduces ambiguity, and accelerates decisions.
Live project: qantus.io
Deep dive
Qantus was designed as a reliable commercial engine: reduce errors, accelerate closing, and standardize pricing decisions in real operations.
Solution architecture
- Configurable rules model by segment and quote type.
- Validation layer to detect inconsistencies before proposal output.
- Rule version traceability for commercial auditing.
Key decisions
- Prioritize precision before early customization.
- Design comparison flows to speed up sales decisions.
- Standardize output templates to reduce operational friction.
Operational lessons
- Most impact came from reducing ambiguity in data inputs.
- Adoption increased when manual steps were removed.
- Error tracking by segment improved rules iteratively.