We build AI products — not features bolted on.

LLM-powered applications, autonomous agents, RAG pipelines, ML systems and production AI infrastructure. Platform-agnostic, deployed in your cloud, engineered to last.
AI app dev
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40+
AI systems in production
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Since 2022
Building LLM products
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ISO 27001
+ AWS certified
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#1
Clutch

AI products and systems we build

Eight AI product types. Each requires a different combination of models, architecture, and operational infrastructure.
01

LLM-Powered Applications

Applications where an LLM is the core product engine — document Q&A, AI writing tools, intelligent search, contract analysis, code review tools, research assistants. Full stack: LLM integration, RAG layer, UI, and operational infrastructure.

02

AI Agents

Autonomous systems that use tools, maintain memory, and complete multi-step tasks without human direction. Customer service, sales, document processing, and ops agents.

03

Agentic AI Pipelines

Multi-agent orchestrated systems — specialist agents coordinated by an orchestrator for complex workflows that exceed what a single agent can handle.

04

AI Chatbots

LLM-powered conversational interfaces — customer service, sales, internal ops, healthcare. RAG-grounded, multi-channel, intelligent handoff.

05

RAG Systems & Knowledge Bases

Connect your LLM to private data — product documentation, contracts, case history, technical manuals. Semantic retrieval, hybrid search, context-aware response generation.

06

Machine Learning Models

Custom models for prediction, classification, recommendation, computer vision. Trained on your data, deployed on SageMaker, monitored continuously.

07

AI Product Integration

Adding AI capabilities to existing products — smart search, document summarisation, sentiment analysis, personalisation, anomaly detection.
08

AI Infrastructure & LLMOps

Model deployment pipelines, prompt versioning, output monitoring, evaluation frameworks, cost management. AI products need a different operational model.

Who hires us

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CTOs building AI-native products, not AI features

Your product's value proposition is the AI capability itself. You need a team that has shipped AI products — with evaluation frameworks, LLMOps, and production failure mode experience — not a team doing their first RAG system on your budget.
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Enterprises adding AI to existing systems

Document intelligence, smart search, automated classification, personalisation. AI that integrates with your existing architecture, runs in your AWS environment, and passes your compliance review.
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Startups building their first AI product MVP

You need an architecture that works now and scales later. You need evaluation built in from day one. You need a team that will tell you when an approach won't work before you've spent six weeks on it.
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Regulated industries with data residency requirements

Healthcare, financial services, legal. Your AI product cannot send data to OpenAI's shared infrastructure. AWS Bedrock in your VPC is the architecture. We build it correctly for your compliance framework.

Not sure which AI product type is right for your use case?

45 minutes. We'll review your concept, recommend the right architecture — RAG, agent, fine-tuned model, or something simpler — and give you a clear view of cost and risk.

Platform-agnostic.
Evaluation-first.
Full-stack delivery.

Four things that distinguish a working AI product from a demo that degrades in production.
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Platform-agnostic, deployed in your cloud

We don't have a model vendor partnership that biases our recommendations. We recommend the right LLM for your problem — Anthropic, OpenAI, Llama, Gemini — and deploy via AWS Bedrock in your environment. Your data never leaves your infrastructure.
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We've been doing this since the technology was new

We shipped our first LangChain production system in early 2023, before most agencies had heard of RAG. We've seen LLM APIs change, frameworks evolve, and production failure modes that only appear at scale. That experience is the difference between a reliable product and a working demo.
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Evaluation frameworks, not just vibes

AI product quality can't be assessed by eyeballing a few outputs. We build rigorous evaluation frameworks: curated test datasets, automated scoring, regression suites on every deployment. You know your AI product is improving — not just changing.
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We cover the full stack

Most agencies wire up an OpenAI API call and build a chat interface. We have product design capability, ML engineering depth, backend infrastructure expertise, and mobile/web delivery — in-house. Complete AI products, not just the AI layer.

Building an AI productor adding an AI feature?

The architecture, evaluation requirements, and operational model are substantially different. Knowing which you're building determines the right approach.
Dimension AI Feature Add-On AI Product (EB Pearls)
Core value proposition Feature within existing product
✓ AI capability is the product itself
Evaluation framework Ad-hoc, manual review
✓ Automated test suites, regression on every deploy
LLMOps requirements Minimal — single API call
✓ Prompt versioning, output monitoring, cost controls
Model selection One model for all tasks
✓ Right model per task — cost, latency, capability
Data residency Shared cloud infrastructure
✓ Your AWS VPC — no data leaves your environment
Typical investment AUD $25K–$60K
AUD $60K–$400K+ depending on complexity

Our AI platform stack

★ marks our preferred production choice for Australian enterprise AI deployments.

LLM Platforms

  • ★ AWS Bedrock
  • ★AWS SageMaker
  • ★Anthropic Claude 3.5
  • OpenAI GPT-4o / o1
  • Meta Llama 3.3
  • Google Gemini 2.0

AI Frameworks

  • ★ LangGraph
  • ★LangChain
  • LlamaIndex
  • Hugging Face

Vector & Knowledge

  • ★pgvector / Pinecone
  • AWS OpenSearch
  • LangSmith
  • Weaviate

AI Backend

  • ★ Python / FastAPI
  • Docker + AWS ECS
  • AWS Lambda
  • Redis + DynamoDB

Your project is 100% protected

EB Pearls signs an NDA before any technical discussion begins. Your code, architecture, and data remain entirely yours.

✓ ISO 27001
✓ ISO 9001
✓ NDA First

From discovery
to production AI

Stage 01

AI Discovery & Architecture

Define the AI problem precisely. Select models and deployment architecture. Design evaluation framework. Assess data readiness. Architecture document with fixed-price quote.

Weeks 1–3

Stage 02

Prototype & Evaluation

Working prototype against representative inputs. Initial evaluation suite. Validate the approach before full investment. Identify model and architecture risks early.
Weeks 4-6

Stage 03

Product Development

Build the full product — AI layer, integrations, UI, backend, monitoring. Continuous evaluation. Weekly demos. Staging deployment for client review.

Weeks 7-16+

Stage 04

Production & LLMOps

Deploy to your cloud. Set up monitoring, alerting, cost controls. Evaluation cadence. Handover documentation and operational runbooks.

Final 3 weeks

How to work with us

Fixed-Price AI Project

Defined scope, price, and timeline. Best for well-scoped AI products where the use case, data, and success metrics are clear before we start.
AUD $25,000–$400,000+

AI Product Retainer

Dedicated AI engineering team on your roadmap continuously. Best for products iterating on live AI systems, expanding to new use cases, or managing ongoing LLMOps.
From AUD $18,000/month

AI Discovery Sprint

2-week fixed engagement to define your AI architecture, select models, design your evaluation framework, and produce a costed build plan before you commit.
AUD $12,000 flat

Every question answered.

Can't find what you need?

LLM-powered applications, AI agents, agentic AI pipelines, RAG systems, ML models, computer vision, voice AI, and document intelligence. Full AI product stack — not just API wrappers.

AI feature: AUD $25,000–$60,000. AI product MVP: $60,000–$150,000. Complex product with agents and MLOps: $150,000–$400,000+. Fixed-scope quotes after free technical discovery.

No — LLM-agnostic. We build on OpenAI, Anthropic, Gemini, Meta Llama, and Mistral. We recommend the right model based on performance, cost, latency, and compliance requirements.

RAG for grounding, evaluation frameworks against ground truth, output validation for low-confidence responses, and production monitoring with human-review sampling.

The operational practice for AI products — prompt versioning, output monitoring, evaluation frameworks, cost management, and incident response. AI products degrade differently than traditional software.

Curated test datasets, automated scoring against ground truth, regression suites on every deployment, and production monitoring with human-review sampling. Eyeballing outputs is not an evaluation strategy.

Bedrock deploys any major LLM within your AWS environment — data never leaves your infrastructure, existing compliance controls apply, no new vendor relationships, and model swaps don't require rebuilding.

An AI feature is a capability added to an existing product. An AI product is built around AI as its core value. The architecture, evaluation rigour, and operational requirements are substantially different.

RAG connects an LLM to your private data at inference time — retrieving relevant documents from your knowledge base to ground responses. You need it when your product must answer questions about your specific data accurately.

AI feature: 4–8 weeks. AI product MVP: 10–16 weeks. Complex product with agents: 20–32 weeks. Prototype and evaluation phase validates the approach before full investment.

Yes — smart search, document summarisation, sentiment analysis, personalisation, anomaly detection. We integrate AI where it genuinely improves the product and tell you where it doesn't.

For RAG: your documentation, FAQs, and policies. For ML: labelled historical data. For chatbots: representative conversations and escalation criteria. We audit data readiness in every discovery phase.
1 Your Information
2 Book Meeting
3 Confirmation

Build your AI product right.

45 minutes. We review your concept, recommend the right architecture, and give you a clear assessment of what's achievable, what it costs, and what risks to plan for.
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.