Question 1

Is our data ready for artificial intelligence?

Accepted Answer

Usually more ready than you fear, and the audit settles the question in the first month. The engagement opens with a data audit that tells you what you have, where it lives, and what it can actually support. If foundations are missing, we name exactly what to fix and build those pipelines as part of the work. We do not train models on data that cannot hold one, because that is how projects fail quietly.

Question 2

How is this different from the AI pilots that never shipped?

Accepted Answer

The order of operations. Failed pilots typically start from a model and hunt for a use case; we start from revenue at stake, fix the data foundation first, and set an offline baseline the model must beat before deployment is even discussed. Production is the definition of done, so integration into your CRM and workflows is scoped from day one rather than left as a phase two. That discipline is why our models end up in daily use instead of in a slide deck.

Question 3

Do you replace our data team or work with it?

Accepted Answer

We work with whatever you have, from a full data team to no technical staff at all. If you have analysts and engineers, they are in the room from the audit onward; if you do not, we run the build end to end and operate it for you. Either way, the code lives in your repositories, the models run in your infrastructure, and we document everything as we go. When the engagement ends, the capability stays with your people.

Question 4

Do we need to fine-tune a model, or is RAG enough?

Accepted Answer

Usually retrieval gets you most of the way, and we prove it before you spend on fine-tuning. Retrieval-augmented generation grounds a strong base model in your own content and updates the moment your data does, which is the right call when knowledge changes often. Fine-tuning earns its cost when the model needs to internalize a consistent voice, a domain vocabulary, or a judgment call that examples teach better than instructions, and the two combine well. We benchmark both against your own task and a held-out set, then recommend the cheapest approach that clears the accuracy bar you set.

Question 5

How do we know the model keeps working after launch?

Accepted Answer

Because monitoring ships with it. Every production model gets drift detection, accuracy tracking against fresh outcomes, and retraining pipelines, so degradation is caught and corrected as data changes. You see model health in the same dashboards as the business numbers it drives. A model without monitoring is a liability on a timer, so we never ship one that way.

Question 6

Who owns the models, and does our data leave our environment?

Accepted Answer

You own the models, the code, and the training data outright, and everything runs inside your cloud: your warehouse, your VPC, your access controls. Your data never leaves your environment and is never pooled with another client's. We build in your repositories and document the pipelines.

	EGGKNITE	In-house hires	Off-the-shelf AI tools
Speed to production	First model backtested against your history and shipped into your stack in the first engagement.	A data science team hired, ramped, and still hunting for its first use case.	Live in a day, generic from day one.
Fit to your data	Trained on your closed-won history, your catalog, and your customers.	Deep fit eventually, if the team survives the first stalled project.	One model for every customer in every industry.
Proof before spend	Every use case backtested against a baseline it has to beat before deployment.	Proof arrives after the salaries are already spent.	The demo is the proof.
Data privacy	Models run in your cloud, behind your access controls. Nothing pools with other clients.	In your control, once the infrastructure exists.	Your data trains someone else's product.
Ownership	Models, code, and training data are yours outright, documented in your repos.	Owned in-house by definition.	Cancel the subscription and the capability disappears.

Predictions you can act on.

The AI execution gap

Case Studies

What's included.

Predictive Models & Custom Solutions

Generative AI & Private LLMs

Model Customization & Fine-Tuning

Language, Vision & Audio AI

The method.

Core Differences

Deliverables that ship.

Questions, answered.

Let's grow.

Monthly dose of growth marketing.