Seeking Senior Systems-Integration Partner for an AI Operations Platform

Seeking Senior Systems-Integration Partner for an AI Operations Platform Project Overview I am seeking a qualified technology company to help migrate, strengthen, and expand an already-developed AI and data-operations platform. This is not a greenfield project. Much of the operating logic, workflows, requirements, and existing system have already been developed. For confidentiality, the existing proprietary operating requirements will be referred to in this post as the Reference Framework. The selected partner’s role will be to: Review and understand the existing system Preserve the Reference Framework and current functionality Migrate existing workflows into a more reliable platform Replace fragile or manually maintained tools where appropriate Connect APIs, browser automation, AI models, databases, and workflows Improve reliability, monitoring, validation, and reporting Build inside client-controlled accounts and infrastructure Support future expansion into additional operational modules The goal is to move from a collection of individually maintained tools into a scalable, secure, auditable infrastructure platform that executes and enforces the existing Reference Framework. This is not simply a scraper or chatbot. It is an operational, data-validation, document-processing, and risk-analysis system. I am particularly interested in a partner capable of delivering a functional initial pilot in approximately four weeks, provided quality, security, accuracy, code ownership, and auditability are not sacrificed. If your team can deliver faster through parallel development or an improved approach, please explain how. Initial Platform Capabilities The platform should be able to: Collect information from complex public and government websites Search by address, parcel ID, owner, case number, or other identifiers Retrieve and preserve original documents Process PDFs, scanned records, and inconsistent formats Classify documents and extract structured information Compare information across multiple records Detect missing, conflicting, or incomplete information Apply the existing Reference Framework Assign confidence and risk classifications Route uncertain results to human review Produce dashboards, reports, exports, notifications, and follow-up tasks Preserve source URLs, page citations, timestamps, and audit history The system should be designed so that additional counties, data sources, workflows, and operational modules can be added without rebuilding the platform. Future expansion may include additional property, title, tax, lien, judgment, ownership, compliance, communications, CRM, contractor, funding, and document-generation workflows. The initial scope must be clearly defined, but applicants should understand that this may become a long-term platform and implementation relationship. Required Capabilities 1. Data and website ingestion The system should support: Public-record and government websites Legacy and inconsistent portals Search by parcel, owner, address, or case number Document and PDF downloads New-record or docket-change detection County-specific workflows Source preservation Retrieval timestamps Manual upload when automation fails Retry and failure handling The platform should use modular adapters so that adding or changing one data source does not break the entire system. Applicants may recommend browser automation, direct APIs, visual agents, or other appropriate approaches. 2. Document classification and extraction The platform should classify and process documents such as: Court filings Complaints Notices Judgments Tax and municipal records Liens Mortgages and assignments Bankruptcy and probate records Code violations Sale records Releases and satisfactions Court orders Docket events Unknown or unclassified documents It should extract: Property address Parcel number Case number Filing and recording dates Parties and creditors Amounts Court and jurisdiction Relevant deadlines Document completeness Source and page citations Original documents must be preserved for verification and audit purposes. 3. Reference Framework rules engine The Reference Framework must be implemented as a structured, version-controlled rules engine. Rules should be: Added, updated, tested, and approved independently Versioned with effective dates Traceable to the results they produce Capable of being updated without rebuilding the entire platform Tested against historical records Each rule should contain: Rule ID Required inputs Acceptable source documents Validation conditions Risk or operational consequence Confidence threshold Human-review requirement Version and approval history The system must distinguish between: Verified facts Missing information Conflicting information AI interpretation Rule-based results Human-approved conclusions The system must not invent legal, financial, or operational conclusions when required information is unavailable. It should clearly identify what is missing and route the matter for review. 4. AI extraction and analysis The AI layer should assist with: OCR and scanned-document processing Document classification Structured field extraction Docket and record summaries Cross-document comparisons Contradiction detection Missing-document detection Timeline and deadline extraction Rule explanations Structured JSON outputs Source citations The system should support model routing, testing, fallback options, and provider replacement. AI should assist with extraction and analysis, but the Reference Framework should control the final operational result. 5. Validation and confidence scoring The platform should include a validation and evaluation layer that can: Assign confidence scores Detect inconsistent fields Compare outputs against approved answers Identify unsupported conclusions Track model performance Detect changes in extraction quality Route low-confidence records to human review Preserve the reason for escalation Human review should be triggered when: Confidence falls below the approved threshold Documents conflict A required document is missing A document type is uncertain A deadline cannot be calculated reliably The source is incomplete or ambiguous The AI reaches a conclusion without adequate evidence 6. Manual-review cockpit The platform should provide a secure review interface. Reviewers should be able to see: Property and owner information Case and jurisdiction details Source documents Extracted facts Rules triggered Confidence scores Missing or conflicting information Recommended next steps Reviewer comments Approval status Complete change history Corrections must preserve the original extraction, corrected value, reviewer, date, and reason for the correction. 7. Reports and integrations The platform should produce: Dashboard results CSV exports JSON outputs PDF reports CRM records Notifications Follow-up tasks API outputs Reports should include: Property, owner, case, and jurisdiction information Documents collected and not found Extracted findings Relevant dates and deadlines Rules triggered Confidence and risk classification Unresolved issues Recommended next action Source citations Human approval when required Preferred Applications and Infrastructure The project may involve the following applications and technologies: Development and coding Cursor GitHub or equivalent client-controlled repository Browser automation and ingestion TinyFish.ai Skyvern.ai Crawl4AI Playwright or equivalent tools Workflow orchestration n8n.io FlowiseAI Custom orchestration code where appropriate AI models and AI routing Kimi OpenRouter Perplexity APIs Headroom Cumulus Labs or equivalent inference infrastructure Validation and evaluation Modaic.dev Custom evaluation and back-testing framework Database and review interface Supabase PostgreSQL Secure object storage for original documents API access and integrations Orthogonal, if appropriate for service discovery, unified API access, and agentic payments Nango, if appropriate for authentication, OAuth, synchronization, retries, and third-party integrations CRM, email, calendar, task, notification, and document-generation integrations as the platform expands These are preferred or previously identified tools, not a requirement to use every product. Applicants should recommend the final architecture and explain: Which tools should be used Which tools should not be used Which tools replace current systems Which tools are alternatives Which tools work together Recurring subscription and usage costs Data-security implications Vendor-lock-in risks Replacement options if a provider changes pricing or becomes unavailable Existing-System Migration The project should begin with an audit of the current system. The selected partner must: Review current code, workflows, tools, prompts, schemas, reports, and documentation Identify what should be preserved, replaced, or improved Create a written current-system-to-new-system mapping Migrate the Reference Framework into structured, version-controlled logic Run the current and new systems in parallel where practical Compare results against historical records Investigate discrepancies Obtain approval for material changes Complete a controlled production cutover Provide full documentation and handoff No existing rule, workflow, or important functionality should be removed without written explanation and approval. The new system must be tested for output parity against existing results. Applicants should explain: Which existing capabilities can be migrated directly Which should be replaced Which should be improved Why each change is recommended How the existing system will remain available during transition How data, rules, and historical results will be protected Security and Code Ownership All work must be performed inside accounts controlled by the client, including: Source-code repositories Cloud accounts Database File storage Deployment systems Monitoring Secrets management The client must retain administrator access at all times. I am open to working with one accountable implementation company, but no single outside individual may have unrestricted access to the complete codebase, all proprietary logic, production data, credentials, and deployment systems. The partner must use: Role-based access Protected branches Code review Two-person approval for production deployments and major rule changes Separate development and production environments Scoped or temporary credentials Secrets stored outside the codebase Access and deployment logs Backups Immediate access revocation upon termination The selected company and all personnel with access must sign appropriate confidentiality and intellectual-property agreements covering: Ownership of custom code and work product Protection of the Reference Framework No reuse for other clients No public disclosure or portfolio use without permission No use of client code, data, prompts, or workflows to train outside models Subcontractor disclosure Return or deletion of all copies upon termination Security-incident notification The client will own the custom code, workflows, integrations, prompts, schemas, reports, data, outputs, and implementation of the Reference Framework. Applicants must disclose any pre-existing proprietary components and explain what happens if the engagement ends. Required Team and Availability Please state how many qualified people can work on this project simultaneously. The ideal initial team would include approximately 4–6 active contributors, depending on the proposed approach: Senior technical lead or systems architect Backend and data engineer Browser-automation and ingestion engineer AI and document-processing engineer Workflow or review-interface engineer Part-time QA, DevOps, or security specialist A smaller team may be acceptable if it can deliver the required capabilities. A larger team may be appropriate if it is properly coordinated. Please provide: Names or roles Seniority and experience Number of people available simultaneously Hours per week Full-time or part-time allocation Whether team members are shared with other clients Any subcontractors Who is accountable for delivery Because the initial timeline is accelerated, explain how the work will be divided into parallel workstreams without giving one person unrestricted access to the entire system. Accelerated Four-Week Initial Phase The goal is to deliver a functional initial pilot in approximately one month. Applicants may propose a faster timeline if they can maintain quality, security, and accuracy. Days 1–3: Discovery and setup Review the existing system and Reference Framework Confirm the initial scope and data sources Establish client-controlled repositories and environments Define migration and acceptance criteria Identify tools to retain, replace, or integrate Begin ingestion and platform setup in parallel Days 4–7: First end-to-end workflow Connect the first data source Retrieve and preserve original records Create the initial data structure Begin document processing and extraction Demonstrate one complete workflow from source record to final result Begin comparison with existing outputs Week 2: Logic and workflow migration Convert priority requirements into version-controlled rules Add structured AI extraction Add source citations and confidence scoring Add missing and conflicting information states Continue historical output comparisons Begin review-interface development Week 3: Review and operational controls Complete the manual-review workflow Add reviewer corrections and audit history Add reports and exports Add retry and failure handling Add monitoring and cost tracking Test additional sources or document types Resolve discrepancies between the existing and new systems Week 4: Validation and handoff Complete historical back-testing Validate output parity Document and approve material differences Complete security and access review Deploy into client-controlled infrastructure Transfer code, prompts, schemas, workflows, rules, and documentation Train the client Begin controlled production use Provide the next-phase expansion plan The first month should be treated as an initial pilot. Applicants must state exactly what will be fully operational after four weeks and what will be delivered in later phases. Performance Metrics The system should be measured using: Document retrieval success rate Document classification accuracy Field-extraction accuracy Parcel and case-number matching accuracy Lien and judgment identification accuracy Deadline-calculation accuracy False-positive rate False-negative rate Manual-review rate Average processing time per record Cost per record Source-specific failure rates Percentage of outputs with citations Time required to add a new source Percentage of records processed without intervention Because this platform involves legal, financial, and operational risk, minimizing false negatives is more important than eliminating every manual review. The system should escalate uncertainty rather than hide it. Proposal Requirements Please provide: Company background and relevant experience Examples of comparable projects Proposed architecture and technology stack Existing-system migration approach Named team or defined roles Number of people available simultaneously Security and access-control plan Code-ownership approach Four-week timeline by day or week Deliverables by milestone Initial project price Recurring infrastructure and API costs Future expansion pricing Testing and acceptance criteria Maintenance and support options Subcontractor information Vendor-lock-in risks Offboarding and data-export process Explanation of how the timeline can be accelerated safely Explanation of how the existing logic and functionality will be preserved Please answer directly: Can you migrate and expand an already-developed operating system into client-controlled infrastructure while preserving the Reference Framework, preventing unrestricted access by any one individual, and delivering a functional initial pilot within approximately four weeks? Please describe your team, access model, migration process, timeline, testing approach, and ownership terms. I am open to using one accountable implementation company. An independent reviewer may be engaged separately for security, code ownership, implementation quality, and production-readiness validation. The right partner must be able to move quickly, work with an existing system, support future expansion, and preserve the client’s control over the code, data, infrastructure, and proprietary operating logic.