Extend an Existing Evidence-Based Organization Profile & Capability Extraction System

@anirudhat

The heart of this is the evidence hierarchy — VERIFIED, DOCUMENT_EVIDENCED, DECLARED, UNKNOWN — and making absolutely sure an assumption can never be silently promoted to a verified fact, which I'd enforce at the type level so it's structurally impossible, not just a convention. Working within your existing models, tests and provenance concepts, I'd build the v0.1 module against the ORGANIZATION_INTAKE_V1 contract as a bounded component: each profile field carries its own evidence tag plus a pointer back to the source (KRS record, a specific span in an uploaded statute, or a questionnaire answer). KRS numbers I'd treat as the authoritative structured source, document claims I'd extract from the PDF layer you already have and mark DOCUMENT_EVIDENCED with the exact locator, and anything unsupported defaults to UNKNOWN. I'll match your typing and test conventions and cover the promotion rules with tests. I can start immediately.

@Prutheus

Your second sentence is the one that made me read the rest: "we do not want the existing system redesigned." That is the right instruction, and it is the one most bidders will quietly ignore — a bounded v0.1 component that fits an existing architecture is a different discipline from building something new. How I would work: - Read the existing code first — the typed models, the decision logic, the provenance concepts — and write back what I understood before proposing anything. If my reading is wrong, that is cheap to correct at that point and expensive later. - Build the profiling component behind the interface your downstream analysis already expects, matching your existing conventions rather than importing mine. - Evidence and provenance carried through every derived field. In an evidence-based system an unsourced value is worse than a missing one, because it cannot be checked and it silently propagates. - Extraction kept deterministic where the input is structured, with a model used only where genuine language understanding is needed — and its output validated against the document rather than trusted. - Tests in the style already in the repository, covering the extraction contract, not just the happy path. Proof: Python is my production stack — FastAPI services with SQLAlchemy 2.0 and pytest suites in commercial use, plus document-processing and OCR pipelines running unattended for industrial clients. I have bid at your posted maximum; I suspect the real scope warrants more and I would rather quote honestly once I have seen the repository. Question: can you share the codebase or a redacted extract of the organization model? Martin

@toriquldev123

Hello There! I’m Md Toriqul Islam, an experienced Python and document-intelligence developer specializing in structured data extraction, LLM workflows, schema validation, provenance, PDF processing, and test-driven development. I understand you need a bounded extension to an existing evidence-backed organization profiling system—not a rewrite. I’ll safely add the agreed profile capabilities while preserving the existing model, interfaces, evidence semantics, anti-leakage safeguards, and downstream behavior. I’m skilled in Python, document parsing, LLM structured outputs, schema-driven extraction, PostgreSQL, pytest, provenance tracking, validation, and fail-closed architectures. I’ll use deterministic extraction where reliable, LLM-assisted extraction for complex documents, and validation layers to ensure unsupported or ambiguous facts remain UNKNOWN. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@muneeb6767

I’d extend your existing Python capability layer in-place, preserving the current Ngo model, provenance contracts, VERIFIED/DOCUMENT_EVIDENCED/DECLARED/UNKNOWN semantics, and fail-closed behavior; deterministic parsing first, LLM-assisted extraction only where justified, with strict validation and anti-leakage tests. I can handle PDF/document intelligence, structured extraction, schema-driven Python models, pytest, and evidence-backed pipelines, with clean integration and documentation rather than a rewrite; estimated 5–7 days / 25–40 hours for a bounded v0.1 scope after reviewing the repository and acceptance cases.

@shakilan4

Hi — this is a good fit for extending an existing evidence-based system without changing its architecture. I’d work directly with your current models and interfaces, adding the agreed organization capabilities while preserving VERIFIED, DOCUMENT_EVIDENCED, DECLARED, and UNKNOWN semantics. I’d use deterministic extraction where possible and LLM-assisted extraction for complex documents like Polish statutes, with validation and provenance attached to every positive fact. Anti-leakage tests would ensure unrelated free text can’t become stable organization capabilities. Two quick questions: Which organization fields are the highest priority for v0.1? Do you already have representative documents and expected outputs for testing? Shakila Naz

@engruhulajom

Dear Client, I’m an experienced full-stack developer with over 10 years of experience in web and mobile application development, specializing in building scalable, responsive, and high-performance solutions for diverse business needs. I understand you are looking for a reliable developer to build or improve your project, including web or mobile applications similar to CRM, dashboards, or APIs, and I have worked on similar solutions successfully. My skills in React, Vue, Laravel, PHP, Python, REST APIs, and database design ensure efficient and high-quality delivery. Feel free to share more details or ask questions. I’m ready to refine my approach to match your exact requirements. Looking forward to working with you. Best regards, Md Ruhul Ajom

@danielboggs

Hi, This fits me perfectly, and I'll get it done! Your project immediately caught my attention — this is exactly the kind of work I enjoy and excel at. I've built up solid experience in this area and I'm genuinely excited about the opportunity to contribute. I'd invite you to browse my profile and past work to get a sense of what I bring to the table. I'm available to start promptly and happy to jump on a quick call to align on your expectations. Let's make this happen!

@adriank06

Hello, Evidence-backed profiling fails when LLM extraction isn’t constrained by schema validation at the parsing layer. I’d implement a three-stage pipeline: deterministic KRS/structured source parsing → LLM extraction with JSON-schema enforcement for statutes → post-extraction rule-based validation that downgrades unsupported claims to UNKNOWN before they touch the profile model. VERIFIED facts come only from authoritative APIs (e.g., Polish KRS registry); DOCUMENT_EVIDENCED requires page-level citations with hash-verified passages; DECLARED is questionnaire-only with explicit user attribution. Anti-leakage tests would inject adversarial free-text into document streams and assert zero profile field mutations without valid evidence chains. Polish statute processing needs domain-specific prompt templates—not generic translation—to preserve legal terminology during extraction. Do you have sample KRS API responses and annotated statute excerpts for test fixtures? Share existing organization model schema and one representative statute so I can confirm extraction boundaries align with your current type system before implementation.

@AdilCodes

With years of experience as a software engineer, specializing in Python, I'm confident I can deliver the organization profiling module you need for your existing system. My background in document intelligence and data modeling is especially pertinent to this project. Understanding the need for an evidence-based approach to organization profiling, my skills will ensure authoritative sources are verified, relevant documents are evidenced and user-declared information is acknowledged. In addition, my experience with integrating systems for downstream analysis aligns well with your goal for v0.1 of the project. I understand that there is 'no assumption into verified fact' policy. With me, you can be sure of a safe and reliable bounded-organization profile intelligence 0.1 component in your system. While not mandatory for me, I believe my native Polish fluency will come in handy when incorporating the language-specific statutes or registration-related documents into the module. My proficiency with modern LLMs and translation tools facilitates document processing and provenance/evidence management effectively. So, let's collaborate to add value to your project while maintaining topnotch standards!

@shayankhan333

Hello, I will extend your existing Python capability layer to safely populate the agreed high-value fields from KRS, statutes, and questionnaires, preserving VERIFIED, DOCUMENT_EVIDENCED, DECLARED, and UNKNOWN semantics without touching the Ngo model. I can start today. I will combine deterministic parsing for structured sources with schema-constrained LLM extraction for Polish statutes, keeping every candidate quote, page, and hash bound to provenance before validation. Questions: 1) Which fields are in-scope for v0.1? 2) Preferred LLM provider, or provider-agnostic? Looking forward to discussing further. Regards, Shayan.

@Maxxionn

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@lugich

Thanks for detail. I have solid experience in document intelligence, RAG extraction, Python document parsing. So I know this workflow well. I hope to discuss about what you want from my proposal on detail chat. Thanks Lugic

@juaqn07

Hello What I need is you, and what you need is me. Together, we are mutually beneficial, and over time, that collaboration becomes a valuable asset. Juan