The frameworks we run on
Three methods we use on every engagement, written out in enough detail to follow without us.
The AI Success Framework.
AurvikAI's method for building AI systems that move a number somebody already tracks.
Define the outcome
What does success look like, specifically enough to measure? 'A 30% reduction in support tickets about billing' is an outcome. 'A better customer experience' is a wish. Most AI projects fail here, before anything is built.
Audit the data
AI is only as good as the data it learns from. We audit data quality, completeness, and relevance before selecting a model. Bad data with a good model produces confidently wrong answers.
Architect for production
Model selection, integration design, latency, cost and monitoring are settled together, because each one constrains the others. Production requirements shape every architecture decision.
Build and evaluate
Iterative development with continuous evaluation against the success metrics defined in step one. No surprises at deployment — stakeholders see progress throughout.
Deploy and optimise
A ninety-day window after deployment. We measure against the metrics agreed in step one and keep iterating until they hold. Going live is the middle of the work.
TechBlueprint
Four phases. Each ends with something you own, whether or not you continue with us.
Diagnose
We read the code, the data and the incident history, and sit with the people who run the system. Two weeks. You come out with the real problem, which is often not the one in the brief.
Blueprint
The architecture, the order the work happens in, and the numbers we agree to be judged on — all settled before anything is built.
Build
Two-week increments, each ending in something running. Testing, security and deployment sit inside the pipeline, so nothing waits for a hardening phase.
Scale
We harden it, measure against the numbers agreed in Blueprint, and hand it over with the documentation your team needs to run it without us.
SaaS Strategy Framework.
How to architect, price, and scale a SaaS product — developed across hundreds of SaaS engagements.
Define the value metric
What does your customer get more of when they get more value from your product? This becomes your pricing axis and your product strategy anchor.
Architect for multi-tenancy from day one
Data isolation, tenant management, and the infrastructure decisions that separate SaaS from 'software hosted in the cloud.' These decisions are expensive to change later.
Build the growth loop before the growth team
Activation, retention, and expansion are engineering problems before they're marketing problems. Instrument the product before hiring to grow it.
Design for the enterprise buyer on day one
SSO, RBAC, audit logs, and SOC 2 readiness. Enterprise deals are won or lost on these features — they're easier to build in than to bolt on.
