You will get AI invoice and document checking - code does the math, AI explains
Project details
You will get an invoice-checking system where the arithmetic is done by code and the judgment calls are done by AI - never the other way round. Deterministic matching verifies totals, prices, and quantities against your reference data; the AI layer resolves fuzzy vendor names and writes plain-English findings; and everything lands in an exceptions report so you review 30 flagged rows instead of re-reading 1,000. Built on production experience: my own document-scoring pipeline runs at 75.5% measured accuracy against a 4,610-item reference corpus, with uncertain cases routed to human review rather than silently passed. Six years of enterprise data engineering means the pipeline is tested, replayable, and auditable - same input, same result, every time.
AI Algorithms
Large Language ModelAI Applications
Anomaly Detection, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$400
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 1 | 2 | 2 |
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 |
About Varun
AI Agent & LLM Engineer | Multi-Agent Systems | RAG | Python | GCP
Ghaziabad, India - 6:31 pm local time
MULTI-AGENT SYSTEMS
I built and deployed a conversational BI agent on Google ADK + Azure OpenAI + Looker SDK 4.0, running on Google Cloud. A root router delegates to revenue, pipeline and dashboard sub-agents, and the whole system is read-only by design so it cannot mutate production data.
AI-POWERED DEVELOPER TOOLING
StackGuard: a multi-model PR reviewer on GitHub Actions that parses Dataform SQLX changes, traces downstream Looker dependencies through dependency-graph analysis, and posts risk comments before a breaking change ships. Runs across a 233-repo estate.
RAG AND DOCUMENT INTELLIGENCE
Scolit.AI, my own product: an exam-grading system pairing OCR for page geometry with an LLM for semantics, RAG over a 4,610-question marking-scheme corpus, rubric snapshots frozen at lock time, and an AI-proposes-human-decides workflow enforced at the database grant level.
WHY MY AGENTS DO NOT HALLUCINATE YOUR DATA AWAY
12+ years in enterprise BI and data engineering: LookML, BigQuery, row-level security serving thousands of users across 22 personas, and dashboard migrations validated with SQL parity checks rather than eyeballed charts. That background is why I build validation layers, structured outputs and access control into agent systems - I have spent years being the person accountable when a number is wrong.
STACK
Python | Google ADK | MCP and FastMCP | RAG and vector retrieval | Cloud Run | BigQuery | LookML | Dataform | GCP | Claude, GPT and Gemini
14 projects delivered on Upwork at a 5.0 rating. I work fast, communicate clearly, and deliver production-ready systems.
Steps for completing your project
After purchasing the project, send requirements so Varun can start the project.
Delivery time starts when Varun receives requirements from you.
Varun works on your project following the steps below.
Revisions may occur after the delivery date.
Sample review and rules
I review your sample, agree the checks that matter, and define the exception rules.
Build and verify
Pipeline built and run on your real data; we compare findings against a month you already checked by hand.