You will get AI maturity assessment, prioritised use cases and roadmap with ROI estimate
Project details
Most AI programmes stall in the same place. There's activity - a pilot, a chatbot, a proof of concept that demoed well - and nothing the business actually relies on. The blocker is rarely the model. It's that nobody has decided which use cases are worth funding, in what order, and what has to exist underneath them first.
This engagement answers that. I run discovery with your teams, score candidate use cases on feasibility, ROI and delivery complexity, and hand back a costed, sequenced roadmap. Where a high-value use case depends on data foundations or governance that don't exist yet, that dependency gets sequenced in rather than discovered halfway through a build.
I've done this at enterprise scale: a board-adopted AI roadmap for an ASX-listed retailer, six business units aligned on one sequence, 30+ use cases prioritised, and ~$150M in identified cost-savings potential. Before founding Prople I built and ran Quantium's 150-person global AI and data delivery organisation.
You get a prioritised shortlist, an ROI estimate per use case, and a sequence with dependencies made explicit - enough to take to a board, or to start building from.
This engagement answers that. I run discovery with your teams, score candidate use cases on feasibility, ROI and delivery complexity, and hand back a costed, sequenced roadmap. Where a high-value use case depends on data foundations or governance that don't exist yet, that dependency gets sequenced in rather than discovered halfway through a build.
I've done this at enterprise scale: a board-adopted AI roadmap for an ASX-listed retailer, six business units aligned on one sequence, 30+ use cases prioritised, and ~$150M in identified cost-savings potential. Before founding Prople I built and ran Quantium's 150-person global AI and data delivery organisation.
You get a prioritised shortlist, an ROI estimate per use case, and a sequence with dependencies made explicit - enough to take to a board, or to start building from.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorch, TensorFlowAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$2,800
|
Standard
$4,200
|
Advanced
$7,000
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 2 | 2 | 3 |
AI Model Integration | - | - | - |
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code | - | - | - |
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
SC
Sue C.
Aug 6, 2026
You will get a production readiness review of your AI system with a clear fix plan
If you want to move beyond an impressive demo to a bulletproof, production-ready AI system, hire sareen immediately. Sareen's deep expertise in evaluation harnesses, failure analysis, and practical architecture turned our use-cases into a reliable, enterprise-grade workflow—saving us months of trial and error.
Sareen has that rare ability, he is someone who knows both how to build production systems and how to keep them honest. His architecture review and evaluation framework are indispensable for any team serious about deploying reliable AI.
Sareen, you were an absolute pleasure to work with, our team are very much looking forward to our next piece of work we've contracted with you.
Sue Cody, Managing Director
CODYGroup
"He doesn't just evaluate AI systems; he understands how they actually fail in production. His review gave us immediate clarity on our retrieval gaps, failure points, and guardrails, providing a clear roadmap to turn a prototype into a high-accuracy, production-grade product."
Sareen has that rare ability, he is someone who knows both how to build production systems and how to keep them honest. His architecture review and evaluation framework are indispensable for any team serious about deploying reliable AI.
Sareen, you were an absolute pleasure to work with, our team are very much looking forward to our next piece of work we've contracted with you.
Sue Cody, Managing Director
CODYGroup
"He doesn't just evaluate AI systems; he understands how they actually fail in production. His review gave us immediate clarity on our retrieval gaps, failure points, and guardrails, providing a clear roadmap to turn a prototype into a high-accuracy, production-grade product."
About Sareen
AI Implementation Expert - Agentic AI, RAG & LLM Systems in Production
Sydney, Australia - 2:02 pm local time
15+ years across generative AI, data science and enterprise delivery. Clients bring me in when there is AI activity but nothing meaningful in production, and the gap between a prototype that demos well and a system the business can actually rely on needs closing.
WHAT I DO
Agentic AI and multi-agent systems, designed and built through to production, with the evaluation harness and observability built alongside the architecture rather than bolted on afterwards.
Real-time RAG pipelines covering ingestion, retrieval, orchestration, model integration and APIs, with model and vector-store choices made against latency, cost, security and scalability.
AI strategy and roadmaps: use-case prioritisation on feasibility, ROI and delivery complexity, costed and sequenced for an executive team or a board.
Fractional AI leadership, embedded inside your team as the senior technical decision-maker for AI.
SELECTED WORK
A board-adopted enterprise AI roadmap for an ASX-listed retailer: 6 business units aligned, 30+ use cases prioritised, ~$150M in identified cost-savings potential.
Agentic AI shipped to production at 72% straight-through processing and 96% accuracy, with triage workflows routing the rest to human review.
The RAG and agentic pipeline behind a crash-detection system for a growth-stage tech business building safety equipment, fine-tuned on live sensor data and deployed through a full MLOps stack.
An Azure lakehouse, ETL and BI rebuild for a global manufacturer in under 8 weeks, harmonising 3 ERP systems and unblocking 3 stalled LLM use cases.
Production LLM, RAG and agentic systems delivered in 6-8 weeks per engagement, sustaining 92% task accuracy and 70% weekly active usage within 6 weeks of launch.
TECHNICAL DEPTH
Generative AI and agents: LLM application development, production RAG architecture, agentic and multi-agent workflow design, context engineering, fine-tuning (LoRA/QLoRA), prompt engineering, evaluation frameworks and LLM observability. LangChain, LangGraph, LlamaIndex, Claude and OpenAI agent SDKs, Hugging Face, and open-weight models including Llama and Mistral.
Engineering and architecture: Python and FastAPI, API design and integration, workflow orchestration, vector databases (Pinecone, Weaviate, FAISS), Docker and Kubernetes, AWS, Azure and GCP, plus the production concerns that decide whether any of it survives: latency, reliability, monitoring, security and cost control. MLOps and LLMOps for regulated environments.
Data platform: lakehouse and warehouse design (Snowflake, Databricks, Azure Synapse, BigQuery), ETL and ELT with dbt, Airflow and Fivetran, and BI delivery in Power BI, Tableau and Looker.
Strategy and advisory: AI maturity assessment, discovery and use-case prioritisation, 12-24 month roadmaps, ROI modelling, AI governance and responsible-AI guardrails, and executive stakeholder alignment.
BACKGROUND
Before Prople I built Quantium's global delivery organisation from the ground up, 150 people across data science, customer and product analytics, data engineering and product engineering, delivering 60+ client engagements across Australia, the US and the UK in retail, consumer, banking, health and insurance.
I care most about what a business actually relies on well after the initial demo euphoria ends. For builds needing more capacity than one person, I have a delivery team behind me through Prople, my AI consultancy, so an engagement can scale without you changing partners.
If you have AI activity but nothing in production, send me where it is stuck and I will come back with what I would do first - whether or not that turns into an engagement.
Steps for completing your project
After purchasing the project, send requirements so Sareen can start the project.
Delivery time starts when Sareen receives requirements from you.
Sareen works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery with the teams in scope
Structured interviews covering what the work is today, where the time goes, and what has already been tried and stalled.
Use-case identification and scoring
Candidate use cases scored on feasibility, ROI and delivery complexity, so the shortlist is defensible rather than intuitive.