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AI that works in production.Priced honestly.

Most GenAI projects fail commercially, not technically: vague briefs, no accuracy standards, and vendors who disappear post-launch. Our pricing is built around measurable outcomes defined before the build begins. Start with an AI Discovery Session and walk away with a clear recommendation before you spend anything.

Book Your Free Discovery Session
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How Our AI Pricing Works

GenAI pricing is complex because the work spans a wide spectrum, from adding a chatbot to an existing app to building a fully agentic workflow that replaces a team of analysts. We price each engagement type differently and never give you a number without understanding what you're actually trying to achieve.

Every engagement starts with an AI Discovery Session:  a focused conversation with a senior AI engineer who has shipped production AI systems, not just built demos. We assess your use case, data quality, compliance requirements, and ROI potential before recommending a path.

We define accuracy benchmarks, cost ceilings, and ROI metrics before any model is selected. This is what separates AI that works in production from AI that impresses in a demo. Every engagement includes post-launch monitoring — model drift, cost alerts, and accuracy tracking as standard.

What Determines Your Investment

Six variables move the number. Understanding them helps you size your project before we speak.
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Vibe code vs custom build

Starting from a vibe-coded app is faster and cheaper, if it can handle the feature requirements. When it can't, we extract what works and integrate with custom code. The assessment happens upfront, not halfway through.
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Accuracy requirements

A 95% accuracy requirement on a medical triage system costs significantly more than 85% on an internal search tool. Accuracy benchmarks are defined before any model is selected, not after deployment.
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Data quality & volume

Clean, structured data reduces cost and build time significantly. Unstructured or sparse data requires cleaning pipelines and often a different model approach, both of which add scope and cost.
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Model approach

API-based LLM (OpenAI, Claude, Gemini) is fastest and cheapest. RAG adds complexity. SageMaker fine-tuning adds significant cost. Custom model training is enterprise-only. Each is right for different problems.
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Compliance & data sovereignty

Healthcare (HIPAA), finance (ASIC), and government projects require Australian-region deployment, full audit trails, and data sovereignty controls. This materially affects architecture and cost.
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Operations workflow complexity

Single-workflow automation (invoicing, triage, follow-ups) is straightforward. Cross-department automation spanning multiple systems, approval chains, and edge cases requires more mapping, testing, and phased rollout.

Know what you're building before you build it

Before any engagement, we give you an honest assessment of your AI opportunity — what's achievable, what it costs, and what could go wrong. No demo. No pitch deck. Just a real conversation and a written recommendation you can act on. 
AI Discovery Session

90-minute working session, not a sales call.

In 90–120 minutes with a senior AI engineer, we assess your use case, data quality, compliance requirements, and commercial viability. We tell you what we'd build, what model we'd use, and whether your expected ROI is realistic before you commit to anything.

Most clients discover either a clearer path forward, a risk they hadn't anticipated, or a simpler solution than they expected. All three outcomes save money.

$2500 +GST

90-120 minutes · Senior AI engineer

What you'll walk away with

target Use case assessment and commercial viability review kanban Data quality audit and readiness assessment icons8-laptop-and-phone-1-1 Model recommendation (API, RAG, fine-tune, or custom) icons-trello Compliance and data sovereignty requirements review tree-structure Honest investment range and timeline estimate icons-mortgage Written summary with recommended next steps

$2,500 for the answer most agencies won't give you.

Honest assessment, model recommendation, and written next steps — from a senior AI engineer.

Book Your AI Discovery Session

Choose your engagement type

Four distinct AI engagement types — each priced to match the complexity and output. The Discovery Session tells you which one fits your situation.
LLM Integration
Add AI to an existing product. Chat, search, and summarisation.
Vibe Code + AI
From vibe code to AI. Custom code where vibe code hits its limit.
Custom AI Product
AI-native product. RAG, SageMaker, fine-tuned model
Agentic AI
Multi-agent orchestration, complex pipeline automation.
$30K – $80K
AUD · Fixed fee
$8K – $40K
AUD · Fixed fee
$80K – $250K
AUD · Fixed fee
$80K – $400K
AUD · Per project
Typical Timeline
6–10 weeks
2–6 weeks
12–20 weeks
16–40 weeks
Starting point
Existing product
API integration
Vibe-coded app
or we build one
Greenfield
Full custom build
Existing systems
Automate across them
Build method
API + prompt engineering
Vibe code → assess → extend or integrate custom code
Full engineering
RAG / fine-tune / custom
Agent orchestration
Multi-model pipelines
Time to first working version
4–6 weeks
1–2 weeks
Fastest path to working
8–12 weeks
4–8 weeks
First workflow live
LLM API integration
OpenAI, Claude, Gemini, Llama
RAG system
Knowledge base, document Q&A
Add-on
If vibe code supports
Fine-tuning / SageMaker
Custom model training on your data
-
-
If needed
Multi-agent orchestration
-
-
Add-on
AI workflow automation
Built from your processes
-
Core use case
Add-on
ISO 27001-aligned delivery
Australian data sovereignty
Accuracy benchmarks defined pre-build
Where applicable
Cost monitoring from launch
100% IP ownership
"150% uplift in engagement. Every cost was scoped upfront."

— Chief Digital Officer · Vodafone Fiji
"EB Pearls assessed our Lovable in a week, and rebuilt the backend where hit its ceiling."

— Founder · SaaS Startup
"94% accuracy in production on wool valuation, replacing manual appraisal."

— Operation Manager · AWN
"We cut claims resolution time by 65% without a single remediation request."

— Head of Operations · EML
Book a Disccovery Session
Let's Build
Let's Scale
Let's Evolve

LLM Integration

Add AI to an existing product. Chat, search, and summarisation.

Vibe Code + AI

From vibe code to AI. Custom code where vibe code hits its limit.

Custom AI Product

AI-native product. RAG, SageMaker, fine-tuned model

Agentic AI

Multi-agent orchestration, complex pipeline automation.

$30K – $80K AUD Fixed fee
$30K – $80K
AUD · Fixed fee
Typical Timeline
6–10 weeks
Starting point
Existing product
API integration
Build method
API + prompt engineering
Time to first working version
4–6 weeks
LLM API integration
OpenAI, Claude, Gemini, Llama
RAG system
Knowledge base, document Q&A
Add-on
Fine-tuning / SageMaker
Custom model training on your data
-
Multi-agent orchestration
-
AI workflow automation
Built from your processes
-
ISO 27001-aligned delivery
Australian data sovereignty
Accuracy benchmarks defined pre-build
Cost monitoring from launch
100% IP ownership
"150% uplift in engagement. Every cost was scoped upfront."

— Chief Digital Officer · Vodafone Fiji
Book a Disccovery Session
$8K – $40K
AUD · Fixed fee
Typical Timeline
2–6 weeks
Starting point
Vibe-coded app
or we build one
Build method
Vibe code → assess → extend or integrate custom code
Time to first working version
1–2 weeks
Fastest path to working
LLM API integration
OpenAI, Claude, Gemini, Llama
RAG system
Knowledge base, document Q&A
If vibe code supports
Fine-tuning / SageMaker
Custom model training on your data
-
Multi-agent orchestration
-
AI workflow automation
Built from your processes
Core use case
ISO 27001-aligned delivery
Australian data sovereignty
Accuracy benchmarks defined pre-build
Where applicable
Cost monitoring from launch
100% IP ownership
"EB Pearls assessed our Lovable in a week, and rebuilt the backend where hit its ceiling."

— Founder · SaaS Startup
Let's Build
$150k +
$80K – $250K
AUD · Fixed fee
Typical Timeline
12–20 weeks
Starting point
Greenfield
Full custom build
Build method
Full engineering
RAG / fine-tune / custom
Time to first working version
8–12 weeks
LLM API integration
OpenAI, Claude, Gemini, Llama
RAG system
Knowledge base, document Q&A
Fine-tuning / SageMaker
Custom model training on your data
Multi-agent orchestration
Add-on
AI workflow automation
Built from your processes
Add-on
ISO 27001-aligned delivery
Australian data sovereignty
Accuracy benchmarks defined pre-build
Cost monitoring from launch
100% IP ownership
"94% accuracy in production on wool valuation, replacing manual appraisal."

— Operation Manager · AWN
Let's Scale
$80K – $400K
AUD · Per project
Typical Timeline
16–40 weeks
Starting point
Existing systems
Automate across them
Build method
Agent orchestration
Multi-model pipelines
Time to first working version
4–8 weeks
First workflow live
LLM API integration
OpenAI, Claude, Gemini, Llama
RAG system
Knowledge base, document Q&A
Fine-tuning / SageMaker
Custom model training on your data
If needed
Multi-agent orchestration
AI workflow automation
Built from your processes
ISO 27001-aligned delivery
Australian data sovereignty
Accuracy benchmarks defined pre-build
Cost monitoring from launch
100% IP ownership
"We cut claims resolution time by 65% without a single remediation request."

— Head of Operations · EML
Let's Evolve

Which Engagement Is Right For You?

If you want to add AI capabilities to an existing product — chat, search, summarisation, content classification then LLM Integration is the right engagement. Fixed fee, 6–10 weeks, scoped before we start.

If you have an existing vibe-coded app (Lovable, Bolt, Cursor) or want to build one fast, Vibe Code + AI is the fastest path to something working. We assess what the vibe code can handle, extend it with AI workflows, and bring in custom code where it hits its ceiling.

If you're building something AI-native from scratch, a product where AI is the core value proposition, then Custom AI Product is the right path. RAG systems, SageMaker fine-tuning, and full production architecture.

If you need complex multi-agent pipelines, document processing at scale, or enterprise automation with human-in-the-loop oversight, Agentic AI is the right engagement.

Not sure where you fit?  We'll tell you exactly which engagement is right in 30 minutes.

Book Free Discovery Session

Why Most GenAI Projects Fail in Production?

After 50+ AI systems shipped, we've seen the same failure patterns repeatedly. None of them are technical. They're all decisions made — or not made — in the first two weeks of a project.

  • No accuracy benchmarks defined before build
  • No cost monitoring — bills spiral undetected
  • No model drift detection post-launch
  • Data architecture built for demos, not compliance
  • No human oversight in consequential decisions
  • Vendor disappears after delivery

Specialist Add-On Services

Scoped and priced separately based on what your engagement actually needs. None of these are bundled by default.
Service Description Typical investment (AUD)
Data Preparation & Cleaning Unstructured, inconsistent, or sparse data requires cleaning pipelines, annotation, and structuring before any model can be trained or fine-tuned effectively.
Scoped per project$10K – $40K
Model Fine-Tuning Training a foundation model on your proprietary data for domain-specific accuracy gains. Requires sufficient labelled data and is most cost-effective when accuracy requirements exceed what prompt engineering achieves.
Fixed fee$20K – $80K
AI Compliance Framework Full compliance architecture for HIPAA, ASIC, government, and other regulated environments. Includes data sovereignty controls, audit trails, consent management, and evidence documentation.
Fixed fee$15K – $45K
AI Cost Audit Point-in-time audit of an existing AI system's cost structure. We identify inefficient query patterns, over-provisioned infrastructure, and model swap opportunities. Average reduction: 35–45%.
Fixed fee$8K – $18K
Ongoing AI Monitoring Retainer Continuous monitoring of accuracy, cost, latency, and model drift across your production AI systems. Includes monthly reports, alerting, and quarterly optimisation reviews.
Monthly retainerFrom $3K/mo
AI Team Augmentation Embed a senior EB Pearls AI engineer into your existing team. Works in your codebase, your tools, your Slack. Brings production AI experience to teams building their in-house capability.
Monthly retainerFrom $10K/mo

Frequently Asked Questions

Honest answers to what we hear most from founders, CTOs, and enterprise teams before they reach out.

The honest range is $30,000 to $500,000+ depending on what you're building. An LLM integration adding a chatbot to an existing product runs $30K–$80K. A custom AI product with RAG or fine-tuning runs $80K–$250K. Enterprise agentic automation projects run $80K–$400K+. The Discovery Session gives you a real number specific to your use case before you commit to anything.

You do. 100%. All code, data pipelines, model configurations, prompt libraries, and documentation belong to you on delivery. This is written into every contract without negotiation. We never retain ownership of anything built for a client.

Most use cases are solved effectively with API-based models (OpenAI, Claude, Gemini) plus good prompt engineering and RAG. Fine-tuning makes sense when: you need performance on highly specialised domain knowledge, you have sufficient labelled training data, accuracy requirements exceed what prompt engineering achieves, or you need to reduce inference costs at very high volume. We recommend fine-tuning only when it genuinely justifies the additional cost — which is less often than vendors suggest.

That's often the best time to book. We'll help you pressure-test the idea, identify where AI genuinely adds value versus where it's the wrong tool, and shape the use case into something buildable. Arriving with a vague direction is fine — arriving with no direction means we'll spend the session on discovery work, which is still useful but less focused.

You'll receive a written summary within three business days covering the recommended approach, model choice, realistic timeline, and investment range. There's no obligation to engage us afterwards — roughly a third of clients take the recommendation and build it themselves or with another team. If you do want to proceed with EB Pearls, the $2,500 is credited toward the first engagement.

Yes. Send us yours or we'll send ours — either works. We'll have it signed before the session.

Most fail commercially, not technically. The most common causes we see: accuracy standards weren't defined before build (so nobody knew what "good" looked like until users complained), no cost monitoring (so infrastructure bills tripled in the first month), model drift that went undetected for months, data architecture not designed for compliance, and vendors who delivered the build and disappeared. We address all six in every engagement.

We design data architecture and privacy controls in Week 1, not as an afterthought. For regulated industries, we can deploy to Australian-region cloud infrastructure with no data leaving Australian borders. We're ISO 27001 certified and build systems that are compliant with the Australian Privacy Act, HIPAA, ASIC requirements, and government data frameworks depending on your sector.

An LLM integration takes 6–10 weeks. A custom AI product takes 12–20 weeks. Enterprise agentic automation takes 16–40 weeks depending on complexity and the number of workflows being automated. The Discovery Session produces a detailed timeline specific to your project before you commit to the engagement.

No. The session is designed for founders, product leaders, and operators — not engineers. We'll translate between the technical and commercial sides and make sure the written recommendation is something you can share with both your board and your dev team.

A senior AI engineer with production experience shipping AI systems — not a sales rep, not an account manager. Depending on your use case, a second specialist (data, compliance, or a specific domain) may join.
1 Your information
2 Book Meeting
3 Confirmation

The first conversation costs you nothing. A wrong AI decision costs you everything.

Whether you're exploring a use case, ready to build, or have AI that's underperforming, the first step is a straight conversation.
Contact EB Pearls
What to expect on your call

What to expect

  1. 1 Share a few details
    Complete the form with your contact details and what you need help with.
  2. 2 Book your free discovery call
    Once you submit the form, choose a time that suits you for your discovery call.
  3. 3 Privacy comes first
    Sign an optional NDA to ensure the highest privacy level and protection of your idea.
  4. 4 Discovery call
    We’ll discuss your goals, the support you need and answer your questions. If we’re a good fit, we’ll outline the next steps.

What to expect

  1. 1 Share a few details
    Complete the form with your contact details and what you need help with.
  2. 2 Book your free discovery call
    Once you submit the form, choose a time that suits you for your discovery call.
  3. 3 Privacy comes first
    Sign an optional NDA to ensure the highest privacy level and protection of your idea.
  4. 4 Discovery call
    We’ll discuss your goals, the support you need and answer your questions. If we’re a good fit, we’ll outline the next steps.