DATA & ENGINEERING AGENCIES

Bridging Data Engineering and AI Visibility

Data Engineering and AI Visibility aren’t separate disciplines — they’re two halves of the same LLM ecosystem.

Data Engineering makes information structured, accessible, and high-quality.
AI Visibility makes that information discoverable, factual, and citable inside AI models.

Together, they form the new standard for enterprise AI readiness —
where pipelines meet reasoning, and data meets discoverability.

Why Partner with an AI Visibility Engineer

Generative engines are redefining how brands are represented in digital discovery.

When users ask ChatGPT, Perplexity, Gemini, or Claude for product advice or brand comparisons, these systems don’t run a search query —
they generate answers based on structured data, entity relationships, and prior grounding.

Data engineering teams already excel at:

• ingestion

• modelling

• transformation

• pipelines

• storage

• quality assurance

But clients are now asking a different question:

“Why doesn’t ChatGPT know this about us?”
“Why does Perplexity cite outdated or incorrect facts?”
“Why do LLMs recommend competitors instead of us?”

That’s where AI Visibility comes in.

Partnering with AI Growth Hub allows your engineering agency to add a high-margin, high-demand AI visibility layer on top of your existing data stack — without hiring in-house LLM specialists.

You own the data layer.
We own the visibility and generative reasoning layer.
Clients get both.

The Joint Data Engineering + AI Visibility Framework

From Ingestion to Interpretation

A structured seven-phase model showing how data engineering and AI Visibility combine to create total AI readiness:

1. Ingestion – capturing brand, product, and service data

2. Normalisation – enforcing quality, consistency, and contracts

3. Structuring – schemas, entities, attributes, knowledge graphs

4. Indexing – embedding pipelines and vectorisation

5. Retrieval – RAG-ready content and hybrid search foundations

6. Grounding – aligning LLMs with authoritative brand facts

7. Visibility – measuring citations, correctness, and model recall

This joint approach ensures clients’ data is not just engineered — but understood, recalled, and cited by AI models.

The future belongs to brands whose data is both machine-ready and model-visible.

What We Can Offer Together

Visibility Audits

Model-to-model analysis across LLMs, entity extraction gaps, structured data review, and data pipeline readiness scoring.
One-off project from $4,000.

AI Visibility Engineering Retainers

Ongoing support combining:

• data pipeline enhancements

• knowledge graph maintenance

• LLM recall monitoring

• drift and hallucination defence

Typical range $5,000–$9,000 per month.

Enterprise Knowledge Graph & RAG Development

End-to-end development of brand knowledge layers, entity alignment, RAG pipelines, index optimisation, and continuous model evaluation.
From $12,000 per month.

Delivered through a seamless partnership model —
you lead the engineering and infrastructure,
we lead the LLM, visibility, and generative analysis layer.

To the client, it’s one unified solution.

Partnership Models

1. Revenue Share

We co-deliver projects and split net revenue 50/50.
You provide data engineering expertise; we provide the AI visibility & LLM layer.

2. Delivery Collaboration

You remain the primary contractor.
We supply AI Visibility as an integrated component of your engineering package.

3. White-Label

We deliver everything under your brand.
You manage the client, we handle the engineering of LLM visibility behind the scenes.

Why This Partnership Works

For Data & Engineering Agencies:

• Add an AI Visibility service line without new hires.

• Move up the value chain in AI readiness, RAG, and LLM grounding.

• Differentiate from traditional data engineering competitors.

• Increase project value by pairing pipelines with model visibility outcomes.

For AI Growth Hub:

• Partner with engineering teams that already deliver ingestion and scaling.

• Integrate AI visibility into mature data infrastructures.

• Expand reach into existing enterprise client networks.

Together, we deliver:
Pipelines for machines.
Visibility for models.
Clarity for customers.

Let’s Co-Create the Future of Enterprise AI Readiness

If you're a data engineering or ML engineering agency ready to evolve with AI-driven discovery, let’s talk.

Data Engineering makes information usable.
AI Visibility makes it discoverable.
The future belongs to companies that excel at both.