You will get Product and Use Case Validation Demo
Rising Talent
Project details
We deliver a demo-first, working MVP—not slideware. In one engagement you get a microservice/serverless build wired with CI/CD + IaC, run-books, observability, and cost guardrails, plus a clear demo storyboard, problem statement, key requirements, use-case analysis, and a product-market fit assessment.
Business outcomes
Faster time-to-learn and time-to-value: the MVP proves the riskiest assumptions with real endpoints, data paths, and KPIs—and leaves you a 2-sprint execution plan.
Risk mitigation
Policy-as-code, reproducible IaC, gated CI/CD, rollbacks, secrets management, logging/metrics/tracing, and budget alerts reduce delivery, security, and cost risk from day one.
Built to scale
A modular, API-first microservice/serverless architecture that can grow horizontally, swap components, and plug into event streams, data lakehouse, or analytics—without rework.
Future-ready
Patterns make it easy to incorporate new tech (LLMs/RAG, vector search, feature flags, zero-trust) as you scale, preserving guardrails.
What you walk away with
Running MVP + pipelines + infra code, run-books, observability, cost controls, demo narrative, PMF findings, and a prioritized backlog—ready first beta.
Business outcomes
Faster time-to-learn and time-to-value: the MVP proves the riskiest assumptions with real endpoints, data paths, and KPIs—and leaves you a 2-sprint execution plan.
Risk mitigation
Policy-as-code, reproducible IaC, gated CI/CD, rollbacks, secrets management, logging/metrics/tracing, and budget alerts reduce delivery, security, and cost risk from day one.
Built to scale
A modular, API-first microservice/serverless architecture that can grow horizontally, swap components, and plug into event streams, data lakehouse, or analytics—without rework.
Future-ready
Patterns make it easy to incorporate new tech (LLMs/RAG, vector search, feature flags, zero-trust) as you scale, preserving guardrails.
What you walk away with
Running MVP + pipelines + infra code, run-books, observability, cost controls, demo narrative, PMF findings, and a prioritized backlog—ready first beta.
AI Algorithms
Convolutional Neural Network, Feedforward Neural Network, Large Language Model, Linear Discriminant Analysis, Multilayer Perceptron, Multimodal Large Language Model, Radial Basis Function Network, Regression Analysis, Restricted Boltzmann Machine, Transformer ModelAI Applications
AI Chatbot, AI Mobile App Development, AI-Enhanced Classification, Conversational AI, Machine Translation, Natural Language Understanding, Object Detection, Sequence Modeling, Synthetic Data Generation, Text Recognition, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, Jasper AI, Microsoft 365 Copilot, NVIDIA AI Platform, PyTorch, TensorFlowAI Models
AlphaCode, BLOOM, ChatGPT, DALL-E, Dolly, GPT-Neo, LaMDA, LLaMA, Midjourney AI, Naive Bayes Classifier, OpenAI Codex, Stable DiffusionWhat's included $17,500
These options are included with the project scope.
$17,500
- Delivery Time 30 days
- Number of Revisions 3
Frequently asked questions
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
DC
David C.
Jun 3, 2026
🔸 Project Manager for AI Automation — Manage Client Delivery at a Fast-Growing Agency
It was a pleasure working with Chieng. He has a wealth of knowledge and experience with agentic solutions and AI automations, and comes with an existing team of subcontractors he can work well with if extra capacity is required. He was proactive with ideas and was flexible and willing to work on adjacent tasks to help the business. I would gladly recommend Chieng to future employers.
About Chieng
AI Products | Agentic Automations | CRMs & ERPs | Pipelines | BI | GTM
100%
Job Success
Irving, United States - 11:55 pm local time
Fractional Head of AI Products & Agentic Automations for Startup-SMB–Mid-Market.
Provide AI agents, multi-CRM & ERP implementation & automations & BI copilots and data agents.
Outcomes: +10–25% win rate, -40–70% manual work, MVP ≤30 days.
Revenue Acceleration: Product market fit (PMF) to Go-To-Market (GTM) in ≤120 days.
Business Experience: Startup to IPO to M&A
I help founders and VPs ship auditable AI that pays for itself—fast. Ex-Global VP of Innovation (Blue Prism) and Head of Product at neuralapps.ai; led 300+ demos/pilots across regulated industries - healthcare, banking & financial services, oil & gas, and manufacturing. I design and deliver agentic apps, multi-CRM & ERP automations (Zoho | HubSpot | Salesforce | HighLevel | Oracle | SAP | Dynamic 365), and BI copilots with governance built in.
What I deliver:
• Agentic apps & chatbots: sales ops, support, BI copilots (Power BI, IBM Cognos, Looker, Tableu).
• Multi-CRM & ERP automation & Revenue Operations: leads→deals, routing, campaigns, commissions, social.
• Data pipelines & observability: Snowflake, SQL Server/Azure SQL, Redshift; lineage & logs.
• Risk & compliance: ISO 27001/42001-aligned guardrails, evidence-by-design.
Business outcomes:
• +10–25% qualified pipeline & win rate
• −40–70% manual effort
• 30-day MVPs with cost guardrails, runbooks, and handoff.
How I work:
Fixed-price milestones, clear SLAs, and measurable KPIs. I partner with SMB & mid-market teams needing enterprise-grade AI—without enterprise bloat.
Starter: 1-week discovery sprint to validate use cases, ROI, and a 30-day build plan.
Platforms (Hands-On & Architecture)
- ERP/Finance & Ops: Oracle Cloud ERP, NetSuite, PeopleSoft, SAP ECC / S/4HANA, Infor (CloudSuite/CSI), MRP
- CRM/RevOps/Service: Salesforce, Zoho CRM, Odoo, ServiceNow (ITSM/HRSD/CSM), Demand/LeadGen
Cloud & Data: AWS (EKS, Lambda, API GW, MSK, RDS/Aurora, DynamoDB, S3/Glue/Redshift), Azure (AKS, Functions, APIM, Event Hubs/Service Bus, Synapse, Purview), Postgres/SQL Server, Redis, MongoDB
Integration & APIs: REST/GraphQL/gRPC, Kafka/Event Hubs, iPaaS/ESB, outbox/inbox, saga orchestration, RPA
DevSecOps & Platform: Terraform/Bicep, Helm, GitHub Actions/Azure DevOps, GitOps (Argo/Flux), OPA/Gatekeeper, Trivy/Snyk, Vault/Key Vault, Prometheus/Grafana
AI & MLOps: RAG/Vector DBs, MLflow, feature stores, model risk, guardrails, prompt governance, observability
Steps for completing your project
After purchasing the project, send requirements so Chieng can start the project.
Delivery time starts when Chieng receives requirements from you.
Chieng works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff: problem, KPIs, access, and acceptance criteria
Facilitated kickoff to confirm problem, users, KPIs, must-haves, and demo “win.” Collect data/APIs, cloud/repo access, compliance constraints. Finalize storyboard, scope, risks, and a 2-week plan with owners.
Use-case validation & solution sketch (demo-first thinking)
Prioritize top 3 jobs-to-be-done. Map happy path + edge cases. Define success metrics, cost guardrails, and mock payloads. Draft UX flow and sequence diagram to align on the smallest testable slice.