AI Transformation
AI transformation, starting from the customer.
We help enterprise teams find the AI use cases worth building, prove them quickly and run them safely inside the platforms they already use.
Overview
What AI transformation means
AI transformation means putting AI to work in how a business operates, not running a lab on the side. In customer experience that usually means assistants that answer questions, search that understands intent, faster catalog and content work, personalization and automation of routine service tasks.
Each of these needs three things: good data, a way to measure quality and guardrails that keep it safe. Most disappointing projects fail on those, not on the model.
We start with the business problem, test a narrow version with real data, measure it against today’s baseline and only then scale. Along the way we apply the same engineering discipline we use for reliability.
Benefits
Where AI helps the customer experience
We focus on uses that have a measurable baseline.
Faster answers
Assistants resolve routine questions at any hour and hand harder ones to people with the context attached.
Better product discovery
Search that understands intent, synonyms and context helps customers find what they meant.
Lower service effort
Suggested replies, summaries and routing give agents more time for the cases that need them.
Richer catalog and content
Draft and enrich product data and descriptions faster, with review before anything goes live.
Decisions backed by data
Forecasts and insights appear in the tools teams already use, with sources they can check.
Risk under control
Privacy, security and human review are designed in from the first prototype.
Services
What we do
From the first workshop to a monitored production service.
Use-case discovery
Workshops to list ideas, score them on value and feasibility and pick the first one to prove.
Data readiness
Check that the data an AI system needs exists, is accurate and can be accessed safely.
Pilots with a baseline
A narrow version built with real data and judged against how the work is done today.
Assistants and agents
Assistants built on Salesforce and AWS services such as Amazon Bedrock, connected to your orders, accounts and knowledge.
Search and discovery
Semantic search and recommendations for your catalog and help content.
Evaluation, guardrails and governance
Test sets, review steps, access rules and monitoring for quality, cost and safety.
Approach
How an engagement runs
Small steps with a decision at the end of each.
- 1
Find
Pick one use case with a clear owner, a measurable goal and data you can use.
- 2
Prove
Build a small pilot and measure it against the current process before investing more.
- 3
Build
Engineer it properly: integration, security, evaluation, cost controls and a release pipeline.
- 4
Govern
Monitor quality, cost and safety in production, review results regularly and retire what does not work.
Questions
Common questions about AI Transformation
Where should we start?
With one narrow use case that has an owner, a measurable goal and usable data. A small proof beats a big plan.
Do we need to train our own model?
Usually not. Most business uses work well with hosted foundation models connected to your own data through retrieval. Custom training is the exception.
How do you handle data privacy?
We design access around least privilege, keep data in your own cloud accounts where possible and choose service settings that keep data out of model training. We review each provider’s terms with you.
How do we know it works?
We build a test set from real questions, compare answers with approved ones, add human review and track results in production, so quality is a number you can watch.
Will AI replace our team?
Our aim is to remove repetitive work and give people better tools. Every project includes a plan for human review and for how roles change.
Talk about your AI Transformation project.
Tell us where you are today. We will tell you what we would do first.