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Local AI-Powered Receipt & Invoice Organizer for Small Businesses
What I Built
I built a privacy-focused, lightweight Local Receipt & Invoice Organizer tailored for freelancers and small business owners. Managing everyday financial documents like receipts, bills, and invoices is often tedious and time-consuming.
This project uses open-weight AI models running locally to scan invoice documents, extract key structured information (such as vendor name, date, total amount, and line items), and auto-categorize them into secure local folders.
Key Features:
Why Open Innovation Matters
Open innovation is essential when handling sensitive user data like financial records.
Tech Stack & Approach
#devchallenge #hf26challenge #ai #opensource #software #coding #development #engineering #inclusive #community
Local AI-Powered Receipt & Invoice Organizer for Small Businesses
What I Built
For this week's Hacktoberfest Open-Source AI & Database Challenge, I built and submitted an official community blueprint to Kestra
: the Autonomous TimescaleDB + Gemini AI FinOps Sentinel.
It creates a real-time, automated financial surveillance system that monitors cloud spending timeseries in TimescaleDB Continuous Aggregates (CAGGs), detects statistical spending spikes, invokes Google Gemini 1.5 Flash for contextual root-cause diagnosis, and orchestrates remediation alerts directly into Slack.
🐙 Upstream Pull Request: kestra-io/blueprints (Timescale + Gemini Sentinel)
🐯 Database Engine: TimescaleDB / Tiger Cloud
🤖 AI Engine: Google Gemini 1.5 Flash API
⚡ Workflow Orchestrator: Kestra
The Problem: The "Silent Cloud Bill Surge"
Every engineering team has experienced the dreaded end-of-month cloud billing surprise:
Uncoordinated Provisioning: A developer spins up on-demand GPU clusters (p4d.24xlarge) or an unthrottled LLM embedding pipeline, forgetting to terminate it on Friday night.
Static Alert Fatigue: Traditional alerts rely on rigid threshold rules (e.g., "notify if spend > $1,000"). When baseline traffic naturally grows, alerts cry wolf and engineers ignore them.
Missing Context: A raw metric alert like [ALERT] Spend is $3,200 doesn't explain why it spiked or what to do, forcing on-call engineers to spend hours triaging raw AWS/GCP dashboards.
The Architecture
[TimescaleDB Hypertables & CAGGs]
│
▼ (Hourly Rollups)
[Statistical Z-Score Anomaly Isolation]
│
▼ (Anomaly JSON Payload)
[Google Gemini 1.5 Flash Root-Cause Diagnosis]
│
▼ (Structured JSON: Severity + Remedy)
[Kestra Declarative Flow Control (If Severity >= HIGH)]
┌─────┴─────┐
▼ ▼
[CRITICAL/HIGH]
[NOMINAL] │ │
├─► [Slack Block Kit Alert]
└─► [Kestra Audit Trail KV Store]
How It Works in Kestra
The entire pipeline is orchestrated as an event-driven declarative workflow in Kestra (timescale-gemini-financial-anomaly-sentinel.yaml):
The workflow triggers at the top of every hour (io.kestra.plugin.core.trigger.Schedule). It calculates rolling standard deviations over recent hourly buckets from Timescale continuous aggregates:
Extract from detect_timeseries_anomalies task
for cat in categories:
mean = sum(baseline_spends) / len(baseline_spends)
variance = sum((x - mean) ** 2 for x in baseline_spends) / len(baseline_spends)
std_dev = math.sqrt(variance) if variance > 0 else 1.0
z_score = (current_spend - mean) / std_dev
if z_score >= threshold:
anomalies.append({
"category": cat,
"current_spend_usd": round(current_spend, 2),
"historical_mean_usd": round(mean, 2),
"z_score": round(z_score, 2),
"percentage_spike": round(((current_spend - mean) / mean) * 100, 1),
"vendor_hints": ["AWS US-East GPU instances", "Overlapping on-demand EC2 clusters"]
})
Rather than sending a cryptic number to Slack, the anomaly vector is dispatched to Google Gemini 1.5 Flash. Gemini evaluates historical variance, correlates vendor hints, and outputs a structured incident response:
json
{
"severity": "CRITICAL",
"summary": "Cloud Infrastructure spend surged by 470% (Z-Score: 4.2) due to uncoordinated on-demand GPU instance provisioning.",
"remediation_action": "Audit active AWS us-east EC2 clusters and terminate orphaned p4d.24xlarge on-demand instances immediately.",
"urgency_score": 9
}
Kestra's io.kestra.plugin.core.flow.If evaluates the urgency returned by Gemini:
If CRITICAL or HIGH: It formats an interactive Slack Block Kit alert directed at #finops-alerts with the exact remediation steps, and commits the incident ID into Kestra's persistent KV store.
If LOW or MODERATE: It silently logs nominal status, completely eliminating alert fatigue.
yaml
Key Lessons & Hacktoberfest Takeaways
Continuous Aggregates in Timescale + AI = Real-Time FinOps: Calculating rolling metrics on raw transaction tables takes hundreds of milliseconds; Timescale's continuous aggregates pre-compute the time buckets, enabling sub-millisecond anomaly feeds into Gemini.
AI-Enriched Alerts Change Incident Response: An on-call engineer receiving a pre-diagnosed Slack notification with immediate action items can resolve incidents in minutes rather than spending hours chasing dashboard charts.
Open-Source Power: By packaging this as an upstream Kestra Blueprint, any team running Kestra and PostgreSQL can deploy this automated sentinel in under 5 minutes.
Built with TimescaleDB, Google Gemini, and Kestra for Hacktoberfest 2026!#devchallenge #tigerdata #hf26challenge #ai #software #coding #development #engineering #inclusive #community
Autonomous FinOps Sentinel: Orchestrating TimescaleDB Anomaly Detection with Google Gemini AI in Kestra
What I Built
CivicPulse Tricity (NagrikSetu AI) is an open-source, multilingual Digital Public Infrastructure (DPI) platform built for the Code for Communities Chandigarh hackathon (supported by Google for Developers and GDG Cloud Chandigarh).
It unifies grassroots citizen feedback across Chandigarh, Mohali, and Panchkula by accepting natural voice notes, WhatsApp messages, and web tickets in Punjabi (ਪੰਜਾਬੀ), Hindi (हिंदी), and English, standardizing them through Google Gemini 1.5, geocoding complaints into Le Corbusier sector grids, and generating executive Detailed Project Reports (DPRs) for municipal commissioners.
🌐 Live Demo Portal: shivysidinfo.tech/civicpulse
📊 7-Slide Pitch Deck: shivysidinfo.tech/civicpulse/deck
🐙 GitHub Repository (MIT): github.com/satanic47/civicpulse-tricity
The Problem It Solves
Governments across Indian urban centers struggle with fragmented citizen feedback loops. In the Chandigarh Tricity region:
Linguistic Barrier: Over 70% of grassroots citizens verbally communicate in Punjabi or Hindi, but traditional municipal portals force rigid, English-only dropdown forms.
Data Fragmentation: Grievances sit isolated across WhatsApp messages, call center logs, and social media without spatial clustering. Municipal engineers cannot distinguish isolated complaints from systemic infrastructure failures.
Budget Misalignment: Multi-crore municipal capital budgets are often allocated based on historical inertia rather than real-time demand hotspots and citizen safety urgency.
How Open-Source & Google AI Powers the Solution
[Citizen Voice / WhatsApp (Punjabi / Hindi / English)]
↓
[Google Gemini 1.5 Multilingual Normalization Engine]
↓
[Canonical Schema: Urgency (1-10) + Municipal Dept Routing]
↓
[Le Corbusier Sector Grid Centroid Geocoding (Sectors 1-60)]
↓
[Spatial Clustering & Sector Health Scoring]
↓
[Gemini Municipal Capital DPR & Budget Generator]
When a citizen speaks or texts in colloquial Punjabi ("ਸਾਡੇ ਸੈਕਟਰ 22 ਸ਼ਾਸਤਰੀ ਮਾਰਕੀਟ ਕੋਲ ਪਾਣੀ ਦੀ ਮੁੱਖ ਪਾਈਪ ਫਟ ਗਈ ਹੈ"), the Gemini 1.5 engine extracts the canonical intent, scores the hazard urgency (9/10), and assigns the municipal wing:
python
app/core/gemini_engine.py
prompt = f"""
Analyze this citizen civic report from Chandigarh Tricity:
Input: "{raw_input}"
Extract the following JSON:
Colloquial landmarks (e.g., Shastri Market, Patel Market, Kiran Cinema, PU Gate 2) are resolved against pre-computed polygon centroids for Chandigarh Sectors 1–60, Mohali Phases, and Panchkula:
python
app/core/geocoding.py
def resolve_sector(text: str) -> Dict[str, Any]:
# Regex extraction of sector numbers and Tricity landmarks
match = re.search(r'(?:sector|sec|ਸੈਕਟਰ|सेक्टर)\s*(\d{1,2})', text, re.IGNORECASE)
if match:
sec_num = int(match.group(1))
return SECTOR_CENTROIDS.get(f"Sector {sec_num}", DEFAULT_CENTROID)
return match_landmark(text)
Rather than leaving issues as open complaint tickets, CivicPulse groups incident clusters and synthesizes municipal Detailed Project Reports (DPRs) with estimated costs in Lakhs:
json
{
"dpr_id": "DPR-2026-001",
"title": "Sector 22 Shastri Market Underground Water Conduit Overhaul",
"sector_or_ward": "Sector 22 (Ward 14)",
"urgency_level": "CRITICAL",
"estimated_cost_lakhs": 42.5,
"priority_score": 94,
"roi_metric": "Prevents chronic road foundation waterlogging across 1.2 km commercial zone."
}
Validation & Automated Testing
The entire system is containerized with Docker and verified with 21/21 passing automated unit tests:
test_gemini_engine.py: Validates multilingual dialect triage and fallback handling.
test_geocoding.py: Verifies sector boundary and landmark resolution.
test_api.py: Tests REST endpoints, voice upload pipelines, and hotspot clustering.
What's Next
We are expanding CivicPulse Tricity with WhatsApp webhook integration via Twilio and offline local Gemma 2 inference for air-gapped municipal data centers.
Built with open weights, open APIs, and open-source civic pride for Hacktoberfest 2026!#devchallenge #hf26challenge #hacktoberfest2026 #ai #software #coding #development #engineering #inclusive #community
CivicPulse Tricity: Multilingual AI Digital Public Infrastructure Platform for Chandigarh