u/Adventurous_Chip3199

I'm building a vendor due-diligence and monitoring tool for UK companies. I'm not a risk professional, so I'd value input from people who are.

Inputs: Companies House (status, overdue accounts and confirmation statements, charges, insolvency, officer resignations, disqualified officers), UK Sanctions List fuzzy matching on the company, directors and PSCs, and financial figures from iXBRL/PDF (net assets, profit/loss, current ratio).

Current logic:

• Six categories, each starting at 100 and losing points for specific red flags: registration, filing, governance, insolvency, financial health, sanctions.

• Composite = weighted average (20/15/10/10/25/20%).

• Grades: A ≥ 90, B ≥ 75, C ≥ 55, D ≥ 30, E below.

• Any "critical" flag (sanctions match, dissolved, active insolvency, disqualified officer) forces grade E.

• Categories with no data are excluded and the weights renormalized.

  score = sum(category_score * weight) / sum(weights of assessed categories) grade = "E" if any critical flag else band(score)

The problem: almost everything I screen comes out 100/A. Working through the numbers, the maximum deduction without a critical flag is about 20 points, so C and D can't happen. It's effectively binary: A/B or E.

What I've tried: I screened through 13 different companies, while also providing incomplete data on bulk uploads, still each company I screen comes out with an almost perfect score with all the data intact so, either companies house or gazette connections are not working properly or I am freaking out for having unrealistic expectations.

Questions:

1. Is blending hard compliance gates (sanctions, dissolution) and probabilistic financial risk into one number a design mistake? Should they be separate outputs?
2. How do practitioners treat "no data" versus "clean"? Is a separate confidence indicator standard?
3. Should I calibrate weights against outcomes, such as back-testing on companies that later went insolvent or dissolved? Is Companies House data enough for that?
4. Any frameworks, papers, or open-source examples for third-party risk scoring, especially UK or SME-focused?

Not looking for code review. Happy to share more detail on the logic :)

I'm building a vendor due-diligence and monitoring tool for UK companies. I'm not a risk professional, so I'd value input from people who are.

Inputs: Companies House (status, overdue accounts and confirmation statements, charges, insolvency, officer resignations, disqualified officers), UK Sanctions List fuzzy matching on the company, directors and PSCs, and financial figures from iXBRL/PDF (net assets, profit/loss, current ratio).

Current logic:

• Six categories, each starting at 100 and losing points for specific red flags: registration, filing, governance, insolvency, financial health, sanctions.
• Composite = weighted average (20/15/10/10/25/20%).
• Grades: A ≥ 90, B ≥ 75, C ≥ 55, D ≥ 30, E below.
• Any "critical" flag (sanctions match, dissolved, active insolvency, disqualified officer) forces grade E.
• Categories with no data are excluded and the weights renormalized.

​

score = sum(category_score * weight) / sum(weights of assessed categories)
grade = "E" if any critical flag else band(score)

The problem: almost everything I screen comes out 100/A. Working through the numbers, the maximum deduction without a critical flag is about 20 points, so C and D can't happen. It's effectively binary: A/B or E.

What I've tried: I screened through 13 different companies, while also providing incomplete data on bulk uploads, still each company I screen comes out with an almost perfect score with all the data intact so, either companies house or gazette connections are not working properly or I am freaking out for having unrealistic expectations.

Questions:

1. Is blending hard compliance gates (sanctions, dissolution) and probabilistic financial risk into one number a design mistake? Should they be separate outputs?
2. How do practitioners treat "no data" versus "clean"? Is a separate confidence indicator standard?
3. Should I calibrate weights against outcomes, such as back-testing on companies that later went insolvent or dissolved? Is Companies House data enough for that?
4. Any frameworks, papers, or open-source examples for third-party risk scoring, especially UK or SME-focused?

Not looking for code review. Happy to share more detail on the logic :)

Hey everyone, ​I’ve been building automations for a while, and I keep hitting a ceiling with standard drag-and-drop tools like n8n or Zapier when it comes to heavy data streams, rate limits, and custom logic.
​To push past those limits, I’ve been building an event-driven "Vendor Compliance & Risk Monitoring System" , and I want to get feedback from startup founders, agency owners, and ops leads on what features actually make a system like this "production-grade" in the real world.

​The Stack & Architecture I Built:

#​Instead of relying on third-party SaaS APIs that charge $0.10–$0.50 per lookup, I built custom microservices to handle the heavy lifting:

#​Custom Python Microservice (OpenSanctions XML/CSV Engine): Downloads and parses massive OpenSanctions datasets locally. Runs fuzzy matching against vendor databases without incurring per-lookup API costs.

#​Real-Time Streaming Listener (Companies House API): Instead of polling endpoints every few days, a Python daemon listens directly to the Companies House WebSocket stream. It catches officer resignations, address updates, and filing status changes the second they hit the official record.

#​London Gazette & News Sentiment Scrapers: Uses the Gazette API (for insolvency and court notices) + an RSS/Google News parser to detect early signals of vendor restructuring or mass layoffs.

#​Supabase (PostgreSQL) + n8n Orchestration: Supabase acts as the state store (logging risk history, vendor records, confidence scores). n8n handles the clean middleware layer—routing alert notifications to Slack/Email and triggering updates.

#​Internal Dashboard: A custom-embedded UI giving internal teams a consolidated risk profile for every vendor.

​My Question to Founders & Ops Leads: ​Standard n8n workflows work great for moving a row from Typeform to Google Sheets, but they break down when processing gigabytes of raw data or handling complex, multi-source state machines. ​What problems do you face regularly that CANNOT be solved with a simple 5-node n8n workflow? What processes in your business actually required a full Python/DB backend?

​For those managing vendors or third-party risk: What data points or risk signals actually matter to you day-to-day? (e.g., County Court Judgments / CCJs, credit limit drops, executive turnover, sanctions)? ​What would take a system like this from "cool developer project" to "enterprise/production-ready"? Is it automated retry handling, SOC2-style audit logs, deeper role-based access control (RBAC), or something else? ​Would love a honest roast of the architecture or any insights on where the real operational pain points lie. Thank you 🙏

Hey everyone, I’m currently in my 3rd semester of college and aiming to build a career in DevOps or Cloud Engineering as a fresher. I know entry-level DevOps roles can be extremely competitive in India, so I want to start prepping early and build a solid portfolio before campus placements and off-campus internship hiring begin. Current Status & Skills: -->Tech Stack / Learning: Currently practicing DSA in Java and learning basic Linux CLI commands (file systems, permissions, processes).

Project Built: Vendor Compliance Checker & Notifier

• What it does: Displays vendor data to clients and automatically monitors vendor compliance scores. If a score drops below a threshold (e.g., due to mass resignations or compliance breaches), it sends an automated alert to the client.
• Tech Stack: Python for the backend logic/data processing, n8n for workflow automation, and the entire app is containerized and running in Docker.
• Caveat: It’s a simple project for now—no GitHub Actions/CI/CD pipelines or cloud infrastructure set up yet, but it works end-to-end locally in Docker. Questions for Experienced Engineers & Seniors:

How do I upgrade this project into a "legit" DevOps portfolio piece? -->Should I add a GitHub Actions CI/CD pipeline, deploy it to AWS (EC2/ECS), or manage the infrastructure using Terraform? What would impress recruiters most? -->Is Java DSA relevant for DevOps roles, or should I shift focus? -->I’m doing DSA in Java because of standard college curriculum/SDE prep, but I prefer Python and Bash for scripting. Should I keep grinding Java DSA or focus 80% of my time on Linux, Scripting, and AWS/Docker?

What should my roadmap look like for the next 2–3 semesters? What tools/concepts should I prioritize next (e.g., Kubernetes, Prometheus/Grafana, Terraform, or AWS certs)? Is it realistic to land a remote or off-campus Cloud/DevOps internship in 4th/5th semester with 2-3 solid end-to-end projects? Where should I look beyond LinkedIn/Wellfound? Would love to get feedback from anyone working in DevOps, SRE, or Cloud Infrastructure in India. Any advice on what to fix or focus on next would mean a lot. Thanks in advance 😊

Hey everyone, I’m currently in my 3rd semester of college and aiming to build a career in DevOps or Cloud Engineering as a fresher. I know entry-level DevOps roles can be extremely competitive in India, so I want to start prepping early and build a solid portfolio before campus placements and off-campus internship hiring begin. Current Status & Skills: -->Tech Stack / Learning: Currently practicing DSA in Java and learning basic Linux CLI commands (file systems, permissions, processes).

Project Built: Vendor Compliance Checker & Notifier

• What it does: Displays vendor data to clients and automatically monitors vendor compliance scores. If a score drops below a threshold (e.g., due to mass resignations or compliance breaches), it sends an automated alert to the client.
• Tech Stack: Python for the backend logic/data processing, n8n for workflow automation, and the entire app is containerized and running in Docker.
• Caveat: It’s a simple project for now—no GitHub Actions/CI/CD pipelines or cloud infrastructure set up yet, but it works end-to-end locally in Docker. Questions for Experienced Engineers & Seniors:

How do I upgrade this project into a "legit" DevOps portfolio piece? -->Should I add a GitHub Actions CI/CD pipeline, deploy it to AWS (EC2/ECS), or manage the infrastructure using Terraform? What would impress recruiters most? -->Is Java DSA relevant for DevOps roles, or should I shift focus? -->I’m doing DSA in Java because of standard college curriculum/SDE prep, but I prefer Python and Bash for scripting. Should I keep grinding Java DSA or focus 80% of my time on Linux, Scripting, and AWS/Docker?

What should my roadmap look like for the next 2–3 semesters? What tools/concepts should I prioritize next (e.g., Kubernetes, Prometheus/Grafana, Terraform, or AWS certs)? Is it realistic to land a remote or off-campus Cloud/DevOps internship in 4th/5th semester with 2-3 solid end-to-end projects? Where should I look beyond LinkedIn/Wellfound? Would love to get feedback from anyone working in DevOps, SRE, or Cloud Infrastructure in India. Any advice on what to fix or focus on next would mean a lot. Thanks in advance 😊

Hello everyone — I'm an n8n developer building an automated vendor verification/onboarding workflow aimed at UK-based startups and small businesses. The pitch is simple: before a company brings on a new vendor/supplier, the workflow checks them against Companies House (active status, name match, registration validity) as a first line of defense against fraudulent vendors and — just as importantly — vendors who chronically delay on services or payment terms. Basically a lightweight due-diligence layer that most small teams skip because they don't have the time or headcount to do it manually.

The core verification (company status, registration number validity, name matching) is working well. But I've hit a few walls trying to make this genuinely valuable rather than a novelty check, and I'd love input from anyone who's built something similar or worked with UK company data at scale.

Problem 1: iXBRL format chaos across company age/size

I'm trying to pull filed accounts data (net assets, filing regularity, etc.) to build a "compliance health" signal. The issue is the iXBRL documents Companies House returns are wildly inconsistent depending on:

• which UK-GAAP/FRS taxonomy version was active when the company filed
• company size category (micro-entity accounts vs small vs full accounts have very different levels of detail — some literally have no P&L)
• older paper-filed accounts that only exist as scanned PDFs, no structured data at all

Has anyone found a workable middle ground here — a parsing library that handles multiple taxonomy versions reasonably well, or a smarter strategy than "try to parse everything, flag whatever fails"? I'm currently planning to just build for the 2-3 most common patterns and route anything else to manual review, but curious if there's a better-trodden path.

Problem 2: Using accounts/filing data to flag "lazy" or high-risk vendors

Beyond just "is this company active," I want to flag vendors likely to delay on payment or delivery — think late filing patterns (late accounts, late confirmation statements) as a proxy signal. Is filing lateness actually a decent proxy for operational reliability in your experience, or is that a weak signal that's going to generate a lot of false positives? Open to being told this approach is naive.

Problem 3: Making this actually worth paying for

Basic verification (status + name match) is neat but thin — it's not something a small business would pay recurring money for once the novelty wears off. I'm looking to layer in things like scheduled re-verification, filing/compliance health scoring, maybe eventually sanctions/PSC screening — but I want to avoid over-building features nobody actually wants. If you run a small business or have bought a similar compliance/verification tool before — what would actually make you pull out a card for this, versus just doing a manual Companies House search yourself once and calling it done?

Appreciate stories about business problems from your end , library recommendations, or simple reality checks. Happy to share more about the n8n/FastAPI setup if useful context.

Hey everyone — I'm building a project (n8n workflow + a bit of custom backend) aimed at UK startups and small businesses, meant to sit in front of your vendor/supplier onboarding process.

The idea: before you bring on a new vendor, it automatically checks them against Companies House — confirms they're a real, active, registered company, that the name and details they gave you match the official record, and flags anything suspicious before you commit to a contract or payment terms.

The goal is basically a lightweight layer of protection against two things: outright scam/shell vendors, and — maybe more common — vendors who are technically legitimate but chronically unreliable (late deliveries, payment disputes, going quiet mid-project).

I've got the core verification working, but honestly, "we checked they're a real company" is a thin pitch on its own — most people could do that manually with a five-minute Companies House search if they really needed to. So I'm trying to figure out what would make this genuinely worth paying for on an ongoing basis, rather than a one-time novelty check.

If you run a small business, or have ever gotten burned by a vendor/supplier/contractor:

• What actually would have warned you in advance, if anything? Late payments elsewhere, bad reviews, company instability, something else entirely?
• Would you want ongoing monitoring (e.g. getting alerted if a vendor you already work with starts showing warning signs — filing lateness, status changes, etc.) or is a one-time check at onboarding enough?
• Is there a specific type of vendor risk you care about more than others — payment/delivery delays, financial instability, fraud, data security, something industry-specific?
• Have you ever paid for a similar due-diligence/verification tool before? What made it worth it (or not)?
• Genuinely — is this something you'd pay for at all, or is it solving a problem that isn't painful enough to justify a subscription. Appreciate any honest input, including "this isn't something I'd ever pay for."

Hey everyone,

I've been doing freelance automation work (n8n workflows + custom backend logic) and want to build a genuinely impressive portfolio project — something that actually demonstrates skill rather than just another "connect Sheets to Slack" template. Looking for suggestions from devls and business owners on what would actually be valuable to build and display in my portfolio.

• n8n workflow automation — building multi-step automations (webhooks, scheduled triggers, conditional logic, API integrations)
• Docker & Docker Compose — containerizing services, multi-container orchestration, writing efficient Dockerfiles
• Python microservices — building FastAPI backends that handle the heavy lifting (data processing, pandas-based cleaning/analysis) that n8n itself can't do natively with the help of external libraries.
• VPS deployment — setting up Linux servers, SSH, Docker installs, port/firewall config, deploying containerized apps to production
• Data pipelines — CSV/Excel cleaning, validation, transformation, syncing to Google Sheets/Drive
• API integration — connecting third-party services (Google Workspace, messaging platforms, webhooks) into working end-to-end systems

I understand the general architecture pattern (trigger → process → output) well, and I'm comfortable gluing services together and writing the backend logic myself, not just dragging pre-built nodes around.

I keep seeing suggestions that require skills I don't have yet (e.g., someone mentioned building an automated AI video generator or a socil media intern. cool idea, but not something my current stack supports).

So — given what I listed above, what's a project that would actually:

1. Be genuinely useful to a real business (not just a toy demo)
2. Push my current skills further rather than just repeating what I already know
3. Look impressive to a potential client browsing a portfolio, not just to other developers

Would love any suggestions, even rough ones. Happy to share more details on my stack if it helps narrow it down. Thank you :)

Hey everyone — I'm a CS student building out a portfolio of n8n-based automations, and I'd genuinely like feedback from people who freelance/consult in this field before I put more time into a direction that might be off.

Quick context on my approach, since I know "I built an automation" can mean very different things:

n8n's built-in Code node runs on Pyodide (Python-in-the-browser via WASM), which is fine for light logic but has real limits — no real filesystem, restricted package support, no persistent state. So instead, for anything that needs actual data processing (pandas, scoring logic, dedup, etc.), I run a separate Python microservice (Fast API, in its own Docker container) that the n8n workflow calls over HTTP. n8n handles the orchestration/triggers/routing, the Python service handles the heavy lifting. saving on token costs for minor data filtration while also having less chances of the workflow breaking in the middle of the night.

The other piece I'm exploring: instead of building automations from scratch every time, I'm looking at popular/public n8n templates and modifying them — via the Python microservice layer — to fit current, specific business needs (updated scoring logic, different data sources, extra validation, etc.) rather than just using them as-is. The idea is treating well-known templates as a starting scaffold, not the finished product, and using code to adapt them to what a real client actually needs.

What I'd love input on from people actually doing this for clients:

1. Are there automations/templates you've used that have quietly broken or gone stale (API changes, deprecated nodes, a service that changed its auth method, etc.) that you haven't had time to fix?
2. Is there a specific automation you wish existed, or an existing one you'd want modified for your workflow, that you'd be open to describing here?
3. For anyone who's used the Python-microservice-alongside-n8n pattern — any pitfalls I should know about before I lean on it more heavily?

overwhelming as it is, I want to work with professionals or even small business owners who are currently facing this problem even as a student who hasn't shipped much yet.