You will get A Multi-Agent AI Workflow with LangGraph for Your Business Process
Project details
A production-grade multi-agent AI workflow that handles your business process reliably, with confidence-based routing, human-in-the-loop checkpoints, and full audit trails.
WHY MULTI-AGENT?
Single-LLM solutions fail in predictable ways. They hallucinate. They overcommit. They can't tell you when they're uncertain. Multi-agent systems split responsibility across specialised agents, each with
one focused job, coordinated by a stateful orchestrator that knows when to escalate to humans.
WHAT THIS WORKS FOR
Customer support triage - classify, retrieve policy, draft response, review quality, escalate when uncertain
Lead qualification - score, enrich, route to right sales rep, flag for review
Content moderation - classify, retrieve guidelines, decide action, audit log
Document review - extract, validate, summarize, flag exceptions
Internal IT helpdesk - categorize, search knowledge base, draft response, escalate
WHAT YOU GET
Custom multi-agent workflow tailored to your process
Production Streamlit dashboard with agent trace visualization
Full source code on GitHub with documentation
Deployment guide & walkthrough explaining the architecture
WHY MULTI-AGENT?
Single-LLM solutions fail in predictable ways. They hallucinate. They overcommit. They can't tell you when they're uncertain. Multi-agent systems split responsibility across specialised agents, each with
one focused job, coordinated by a stateful orchestrator that knows when to escalate to humans.
WHAT THIS WORKS FOR
Customer support triage - classify, retrieve policy, draft response, review quality, escalate when uncertain
Lead qualification - score, enrich, route to right sales rep, flag for review
Content moderation - classify, retrieve guidelines, decide action, audit log
Document review - extract, validate, summarize, flag exceptions
Internal IT helpdesk - categorize, search knowledge base, draft response, escalate
WHAT YOU GET
Custom multi-agent workflow tailored to your process
Production Streamlit dashboard with agent trace visualization
Full source code on GitHub with documentation
Deployment guide & walkthrough explaining the architecture
AI Development Type
Knowledge Representation, Recommendation System, Software MaintenanceAI Tools
Keras, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$800
|
Standard
$1,400
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 10 days | 14 days | 21 days |
Number of Revisions | 3 | 3 | 5 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - |
About Sharon
AI Engineer | Reliable Customer Support Bots, RAG & Automation
Nairobi, Kenya - 9:53 am local time
What I actually help businesses with:
Reliable AI agents - customer support bots, document assistants, and internal tools with confidence-based routing and quality review loops, so wrong answers don't reach your users.
RAG pipelines that actually retrieve - hybrid search, source attribution, and graceful failure handling, so your AI says "I don't know" instead of inventing an answer.
Automation with real validation - data pipelines that catch corrupt records, pricing errors, and silent failures before they hit your dashboards or customers.
Production systems, not prototypes - provider-flexible architectures (Open-AI, Anthropic, Groq, local models) with fallback built in, so your system keeps running when an API goes down.
How I work:
I scope projects honestly. If AI isn't the right solution for your problem, I'll tell you and suggest what is. I write documentation alongside code. I build human-in-the-loop checkpoints for decisions that matter. I don't hand over a black box.
Recent work:
1. A multi-agent customer support system with 5 specialized agents and a Generator-Critic quality layer that reviews responses before they reach users
2. A document Q&A assistant with hybrid retrieval that handles the structural questions pure semantic search consistently misses
3. A real-time pricing pipeline with anomaly detection that caught a critical $0 order bug standard validation missed entirely
If you're building something that needs to work reliably, message me with your stack, the problem, and what "done" looks like. I'll tell you honestly whether I'm the right fit and if I'm not, who probably is.
Steps for completing your project
After purchasing the project, send requirements so Sharon can start the project.
Delivery time starts when Sharon receives requirements from you.
Sharon works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery call & architecture design
30-60 minute scoping call. Map your business process, identify agent responsibilities, define decision points and human-handoff thresholds.
State schema & data contracts
Design the shared state model using Pydantic. Define what each agent reads and writes, ensuring clean separation of concerns.