Phoenix - Your AI AssistantAI-Powered Deal Origination & Opportunity Intelligence Platform
- Posted:
- Proposals: 53
- Remote
- #4506008
- OPPORTUNITY
- Open for Proposals
Bollington 
Description
Build an MVP AI-powered deal origination and opportunity intelligence platform to identify UK private company acquisition, succession, refinancing and distressed opportunities.
The platform should analyse public company data, identify high-probability opportunities, enrich decision-maker information, generate AI reports and support targeted outreach. This is an MVP validation project requiring a fast, practical build with scalable foundations, not an enterprise solution.
Core Objectives
The platform must:
• Collect company/director data
• Analyse and score opportunities
• Identify decision makers
• Enrich contacts
• Generate AI intelligence reports
• Create outreach recommendations
• Store opportunities in CRM
• Maintain continuously updated pipelines
Data Sources
Required:
• Companies House API
• Gazette Insolvency Feed
• Company websites
• Public web research
Preferred:
• LinkedIn enrichment
• Contact providers
• News feeds
• Business directories
Future:
Planning data, Land Registry/property ownership, email automation, workflows, dashboards, additional providers and AI agents.
Functional Requirements
1. Company Intelligence Engine
Retrieve, store and update:
• Company name, number, address and SIC codes
• Filing history, accounts and charges/mortgages
• Directors and shareholders where available
• Insolvency notices and Gazette events
• Website and content summaries
Maintain structured profiles for each company.
2. Opportunity Scoring Engine
Core IP component. Must be configurable, AI-independent and adjustable without code changes.
Required:
• Weighted and rule-based scoring
• Score explanations
• Confidence ratings
Scores:
Acquisition: revenue, EBITDA/profitability, growth, recurring income, sector attractiveness, leverage.
Succession: director age, ownership length, ownership concentration, management depth, succession indicators.
Refinancing: lender charges, debt profile, leverage, property ownership, maturity indicators.
Distress: insolvency notices, winding-up petitions, director resignations, overdue filings, negative trends.
Probability of Sale: founder age, ownership duration, succession indicators, growth plateau, market conditions.
Example:
Sale Score: 86/100
Reasons:
• Founder age estimated 67
• Sole shareholder
• 24 years ownership
• Stable profitability
• No succession structure identified
AI explains scores; scoring remains framework-driven.
3. Contact Enrichment
Identify/store:
• Founder, CEO, Managing Director, shareholders
• Email, telephone, website, LinkedIn
• Decision-maker information
Supports future outreach and relationship development.
4. AI Intelligence Briefs
Generate for high-ranking opportunities:
Company Summary:
Business description, financial overview, strengths.
Opportunity Summary:
Selection rationale, engagement potential, strategic rationale.
Engagement Angle:
Succession planning, growth capital, partnership, acquisition or refinancing
5. CRM
MVP CRM must support:
• Opportunity storage
• Search/filtering
• Notes and comments
• Status tracking
• Outreach tracking
• Score history
Workflow:
Identified → Qualified → Contacted → Conversation Started → Active → Mandated → Closed
6. Outreach Intelligence
Generate/store:
• Personalised emails
• LinkedIn messages
• Telephone briefs
No automated sending required
AI Architecture
Use model-agnostic architecture that remains operational if providers change
Support:
OpenAI, Anthropic Claude, Google Gemini, Meta Llama, DeepSeek, Qwen and future providers
Admin controls:
• Select AI provider
• Change providers without code changes
• Configure API keys
• Add models
Scoring must remain independent of AI
Technology
Backend: Python, FastAPI
Database: PostgreSQL, Supabase
Frontend: React, Next.js
Infrastructure: AWS, Vercel, Supabase
AI: Provider-agnostic APIs with future agent support
Scalability
Support future:
• Multi-agent workflows
• AI orchestration/MCP
• Additional APIs
• Email and workflow automation
• Large-scale analysis
• Advanced reporting
Future integrations:
CRM systems, enrichment providers, email systems, Land Registry, planning/property/commercial intelligence sources
Dashboard
Provide a simple user-friendly dashboard for non-technical sales/outreach users with navigation, opportunity views, filtering, pipeline management and AI insight access
Deliverables
• Working MVP
• Source code
• Deployment instructions
• Technical documentation
• Configurable scoring engine
• CRM
• Company intelligence engine
• Contact enrichment
• AI opportunity reports
• Outreach generation
• User administration
• Large-scale UK company analysis capability
Proposal Requirements
Include:
• Relevant examples
• Architecture
• Technology stack
• Cost estimate
• Delivery timeframe
• Support options
• MVP improvements
Budget
Open to proposals.
Preference for developers experienced in AI intelligence platforms, CRM systems, API integrations and scalable MVP delivery rather than enterprise builds
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Log inClarification Board Ask a Question
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Hi Matthew,
Could you please clarify the following to help us better understand the project scope?
1. Approximately how many UK companies do you expect the MVP to analyse initially, and what is your target refresh frequency (real-time, daily, weekly)?
2. Will you provide API access for any paid data enrichment providers (e.g., LinkedIn, contact databases), or should the MVP rely only on publicly available data initially?
3. Do you already have a preferred authentication system (Supabase Auth, Auth0, etc.) and user roles in mind, or should these be included as part of the MVP design?
4. Should the AI scoring weights and business rules be configurable through an admin interface, or is configuration via database/settings files sufficient for the MVP?
5. Do you have wireframes or UI/UX references for the dashboard and CRM, or would you like us to design the interface as part of the project?
Looking forward to your response.
Best,
VConn Pvt Ltd. -
Hey Matthew, one important design question before build is whether you want the MVP to score transaction likelihood, commercial attractiveness, and outreach priority as separate signals, rather than blending everything into one overall “opportunity score”. For example, a distressed company may have a high likelihood signal but low attractiveness, while a profitable founder-led company may be highly attractive but lower probability of sale. How would you want the first MVP to separate those signals so the platform does not produce confident-looking but commercially misleading rankings?
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The requirements, even for a MVP, are way beyond what the budget can cover. Is there any flexibility in cost or scope?
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One architectural question: would you prefer the opportunity scoring engine to support versioned scoring models (allowing historical opportunities to retain the rules that generated their score), or should score recalculation always use the latest scoring configuration?
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Which capability is the highest priority for the first usable version: opportunity scoring, enrichment, or AI report generation?