AI Sprint Program
From Opportunity to Production in 30–60 Days
From AI opportunity to production-deployed system in 30–60 days — inside Salesforce or your existing enterprise stack.
AI Opportunity
Use case defined & data mapped
Engineering Sprint
Model integration & logic build
Production Deployed
Running inside enterprise stack
What Is the AI Sprint Program?
The Nanostuffs AI Sprint Program is a structured, time-boxed engagement that takes a single, high-value AI use case — inside Salesforce or your enterprise stack — from discovery to production-deployed system in 30–60 days.
Every sprint starts with a defined business outcome, delivered by a dedicated 2–3 person engineering pod — ending with a working system in production — not a prototype, not a report, not a roadmap.
A running system your team can own.
Is the AI Sprint Program right for you?
Not sure where AI fits your business? Answer 6 questions and get specific use cases matched to your industry, goals, and tech stack — instantly. No sales call required.
AI Sprint Fit Score
Check all parameters that currently apply to your organization
Why Most AI Projects Never Reach Production
Most AI projects stall between PoC and production
The typical enterprise AI journey: pilot looks great, then spends months in architecture review, integration complexity, Salesforce org readiness, and security sign-off. The Sprint Program is designed specifically to close this gap.
AI vendors promise outcomes but deliver demos
Many AI vendors deliver slide decks, prototypes, or notebooks instead of real systems. The Nanostuffs Sprint Program guarantees a production-deployed system at the end of every sprint. If it is not in production, the sprint is not complete.
Enterprises cannot approve open-ended engagements
Fixed budgets, quarterly targets, and board scrutiny make open-ended AI projects difficult to approve. A defined 30–60 day sprint with clear output and pricing gets approved much faster.
Internal teams lack the AI engineering depth to execute
Your engineers are capable. But deep ML engineering, LLM integration, and data infrastructure expertise takes years to build internally. We bring it from day one.
Three Phases. One Production System.
AI Opportunity Discovery
Architecture audit, data review, Salesforce org assessment, use case prioritisation, KPI mapping, and sprint scope definition.
AI Sprint Build
A dedicated engineering pod takes one scoped use case from build to production — with knowledge transfer and monitoring configured before handoff.
Continuous Optimisation
Model retraining, Salesforce workflow monitoring, system optimisation, and expansion to additional use cases.
What Every Sprint Includes
Production-Deployed System
A live system integrated directly into your production architecture. Not a prototype. Not a staging demo.
Architecture Documentation
Full technical documentation describing the system architecture, integration points, and maintenance requirements written specifically for your team to own.
Monitoring & Observability
Model performance monitoring, alerting systems, and dashboards configured before delivery.
Knowledge Transfer Session
A structured session with your engineering team — covering architecture, maintenance procedures, and how to extend the system going forward.
Post-Launch Support Window
A dedicated support window of 2–4 weeks covering bug fixes, performance issues, and integration adjustments — included in every sprint.
Sprint Retrospective Report
A written report covering what was built, KPI performance, learnings, and recommended next sprint opportunities.
Every one of these is included in every sprint — not add-ons, not upsells.