Implementation Services
Implementing Generative AI within the Enterprise
Performance tuning is a commitment for all parties in a relationship.
We engage with our clients to accelerate performance on their digital capabilities looking to drive long-term value. Our commitment is to deliver great performance – and our commercial models are built around that.
This is not a linear engagement; it is iterative, and takes a mindset of experimentation to get the optimal result. While we have a playbook from what has worked with other clients, we need to adapt, test, and learn from your specific needs.
Intuition may point you in the right direction, proper measurement and data verifies that you are on the right track. From our experience working with clients, we use a hypothesis/test model rooted in statistics that allows for rapid testing and iteration.
Working With Us
Our engagement model
Our multi-step model is designed to quickly engage with a client, identify areas for improvement, test, validate, deploy, and then repeat.
We start with a free, initial conversation with a client to understand their goals, internal capabilities, competitive positioning, and challenges.
Based on that, we’ll work with a client on a proposed optimization program to start and thesis of where we see improvement opportunities.
We work with our clients to gain access to important measurement and benchmarking technologies to use in this engagement. This may include Google Analytics, Adobe, or other packages.
Additional work may be necessary to implement key performance metrics to benchmark how the properties are working – and see the impact of future improvements.
Based on measurements, we’ll look through the technical infrastructure to see how each stage in the customer journey is performing.
There may be bottlenecks, bounces, and missed opportunities. We’ll assess opportunities for improvement – and come up with a prioritized list of ways to improve performance.
This stage will lay out a strategy for the engagement.
Based on the strategy developed, suggested improvements will be tested in a live environment using an A/B test mechanism to gain statistical significance of improvement.
Once desired results are achieved in the validate stage, changes are pushed into production permanently and monitored and continuously measured against the baseline.