feat: Integrate Folk CRM API for investor synchronization and compatibility scoring

app/main.py

@@ -5,7 +5,7 @@ from db.db import Base, db_dependency, engine
 from dotenv import load_dotenv
 from fastapi import FastAPI, File, Form, UploadFile
 from pydantic import BaseModel
-from routers import companies, investors, projects
+from routers import companies, folk_crm, investors, projects
 from schemas.router_schemas import InvestmentResponse, PaginatedResponse
 from services.llm_parser import InvestorProcessor
 from services.querying import QueryProcessor
@@ -108,6 +108,7 @@ async def query_investors(request: QueryRequest):
 app.include_router(investors.router)
 app.include_router(companies.router)
 app.include_router(projects.router)
+app.include_router(folk_crm.router)
 
 if __name__ == "__main__":
     import uvicorn

app/routers/folk_crm.py

@@ -0,0 +1,190 @@
+from typing import List
+
+from db.db import get_db
+from db.models import InvestorTable
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel
+from services.crm import folk
+from sqlalchemy.orm import Session, selectinload
+
+router = APIRouter(prefix="/folk", tags=["Folk CRM"])
+
+
+class GroupResponse(BaseModel):
+    id: str
+    name: str
+
+
+class SyncInvestorsRequest(BaseModel):
+    investor_ids: List[int]
+    group_id: str
+
+
+class SyncResult(BaseModel):
+    investor_id: int
+    investor_name: str
+    company_id: str
+    company_name: str
+    team_members_synced: int
+    person_ids: List[str]
+
+
+class SyncInvestorsResponse(BaseModel):
+    success: bool
+    synced_count: int
+    results: List[SyncResult]
+    errors: List[dict]
+
+
+@router.get("/groups", response_model=List[GroupResponse])
+def get_folk_groups():
+    """Get all groups from Folk CRM.
+
+    Returns a list of groups with their id and name that can be used
+    to sync investors to Folk.
+    """
+    try:
+        groups_data = folk.get_groups()
+        items = groups_data.get("data", {}).get("items", [])
+
+        return [GroupResponse(id=item["id"], name=item["name"]) for item in items]
+    except Exception as e:
+        raise HTTPException(
+            status_code=500, detail=f"Failed to fetch groups from Folk: {str(e)}"
+        )
+
+
+@router.post("/sync-investors", response_model=SyncInvestorsResponse)
+def sync_investors_to_folk(
+    request: SyncInvestorsRequest, db: Session = Depends(get_db)
+):
+    """Sync investors to Folk CRM as companies with their team members as people.
+
+    Takes a list of investor IDs and a Folk group ID, then:
+    1. Creates each investor as a company in the specified Folk group
+    2. Creates each team member as a person linked to that company
+
+    Args:
+        investor_ids: List of investor IDs from the database
+        group_id: Folk group ID where investors should be added
+
+    Returns:
+        Summary of sync operation including successes and errors
+    """
+    # Fetch investors with their team members
+    investors = (
+        db.query(InvestorTable)
+        .options(
+            selectinload(InvestorTable.team_members),
+            selectinload(InvestorTable.sectors),
+        )
+        .filter(InvestorTable.id.in_(request.investor_ids))
+        .all()
+    )
+
+    if not investors:
+        raise HTTPException(
+            status_code=404, detail="No investors found with the provided IDs"
+        )
+
+    results = []
+    errors = []
+
+    for investor in investors:
+        try:
+            # Create company in Folk
+            company_data = folk.create_company(
+                name=investor.name,
+                group_id=request.group_id,
+                website=investor.website,
+                description=investor.description,
+                addresses=[investor.headquarters] if investor.headquarters else None,
+            )
+
+            company_id = company_data.get("data", {}).get("id")
+            if not company_id:
+                errors.append(
+                    {
+                        "investor_id": investor.id,
+                        "investor_name": investor.name,
+                        "error": "No company ID returned from Folk API",
+                    }
+                )
+                continue
+
+            # Create team members as people
+            person_ids = []
+            team_members_synced = 0
+
+            for member in investor.team_members:
+                try:
+                    # Extract first name and last name from full name
+                    name_parts = member.name.split(maxsplit=1)
+                    first_name = name_parts[0] if name_parts else member.name
+                    last_name = name_parts[1] if len(name_parts) > 1 else ""
+
+                    # Build URLs list from source_url if available
+                    urls_list = None
+                    if hasattr(member, "source_url") and member.source_url:
+                        urls_list = [member.source_url]
+
+                    # Build job title from title or role
+                    job_title = None
+                    if hasattr(member, "title") and member.title:
+                        job_title = member.title
+                    elif hasattr(member, "role") and member.role:
+                        job_title = member.role
+
+                    person_data = folk.create_person(
+                        first_name=first_name,
+                        last_name=last_name,
+                        email=member.email,
+                        company_id=company_id,
+                        group_id=request.group_id,
+                        urls=urls_list,
+                        jobTitle=job_title,
+                    )
+
+                    person_id = person_data.get("data", {}).get("id")
+                    if person_id:
+                        person_ids.append(person_id)
+                        team_members_synced += 1
+                except Exception as person_error:
+                    # Log person creation error but continue with other members
+                    errors.append(
+                        {
+                            "investor_id": investor.id,
+                            "investor_name": investor.name,
+                            "team_member_name": member.name,
+                            "error": f"Failed to create person: {str(person_error)}",
+                        }
+                    )
+
+            results.append(
+                SyncResult(
+                    investor_id=investor.id,
+                    investor_name=investor.name,
+                    company_id=company_id,
+                    company_name=company_data.get("data", {}).get(
+                        "name", investor.name
+                    ),
+                    team_members_synced=team_members_synced,
+                    person_ids=person_ids,
+                )
+            )
+
+        except Exception as e:
+            errors.append(
+                {
+                    "investor_id": investor.id,
+                    "investor_name": investor.name,
+                    "error": str(e),
+                }
+            )
+
+    return SyncInvestorsResponse(
+        success=len(results) > 0,
+        synced_count=len(results),
+        results=results,
+        errors=errors,
+    )

app/routers/investors.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 from db.db import get_db
-from db.models import FundTable, InvestorTable, SectorTable
+from db.models import FundTable, InvestorTable, ProjectTable, SectorTable
 from fastapi import APIRouter, Depends, HTTPException, Query
 from pydantic import BaseModel
 from schemas.router_schemas import (
@@ -12,6 +12,7 @@ from schemas.router_schemas import (
     PaginatedResponse,
     SectorMinimal,
 )
+from services.compatibility_score import calculate_project_investor_compatibility
 from sqlalchemy.orm import Session, selectinload
 
 router = APIRouter(tags=["Investor Routes"])
@@ -46,12 +47,17 @@ class InvestorUpdate(BaseModel):
 def read_investors(
     page: int = Query(1, ge=1, description="Page number (starts at 1)"),
     page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
+    project_id: Optional[int] = Query(
+        None, description="Optional project ID for compatibility scoring"
+    ),
     db: Session = Depends(get_db),
 ):
     """Get all investors with their funds as separate entries (paginated)
 
     Each investor-fund combination is returned as a separate row.
     An investor with 3 funds will appear as 3 entries.
+
+    If project_id is provided, calculates compatibility scores for each investor.
     """
     # Calculate offset
     offset = (page - 1) * page_size
@@ -59,6 +65,18 @@ def read_investors(
     # Get total count
     total_count = db.query(InvestorTable).count()
 
+    # Load project if project_id provided
+    project = None
+    if project_id is not None:
+        project = (
+            db.query(ProjectTable)
+            .options(selectinload(ProjectTable.sector))
+            .filter(ProjectTable.id == project_id)
+            .first()
+        )
+        if not project:
+            raise HTTPException(status_code=404, detail="Project not found")
+
     # Get paginated results
     investors = (
         db.query(InvestorTable)
@@ -66,7 +84,8 @@ def read_investors(
             selectinload(InvestorTable.portfolio_companies),
             selectinload(InvestorTable.team_members),
             selectinload(InvestorTable.sectors),
-            selectinload(InvestorTable.funds),
+            selectinload(InvestorTable.funds).selectinload(FundTable.investment_stages),
+            selectinload(InvestorTable.funds).selectinload(FundTable.sectors),
         )
         .offset(offset)
         .limit(page_size)
@@ -76,6 +95,13 @@ def read_investors(
     # Transform to InvestmentResponse format (one row per investor-fund combination)
     investment_responses = []
     for investor in investors:
+        # Calculate compatibility score if project provided
+        compatibility_score = 1.0
+        if project is not None:
+            compatibility_score = calculate_project_investor_compatibility(
+                project=project, investor=investor, use_funds=True
+            )
+
         # Get top 3 portfolio companies (id and name only)
         portfolio_companies = [
             CompanyMinimal(id=company.id, name=company.name)
@@ -110,7 +136,7 @@ def read_investors(
                     stage_focus=stage_focus,
                     portfolio_companies=portfolio_companies,
                     sectors=fund_sectors,
-                    compatibility_score=1.0,
+                    compatibility_score=compatibility_score,
                 )
                 investment_responses.append(investment_response)
         else:
@@ -125,7 +151,7 @@ def read_investors(
                 stage_focus=None,
                 portfolio_companies=portfolio_companies,
                 sectors=[],
-                compatibility_score=1.0,
+                compatibility_score=compatibility_score,
             )
             investment_responses.append(investment_response)
 
@@ -156,14 +182,31 @@ def filter_investors(
     max_aum: Optional[int] = Query(None, description="Maximum AUM"),
     page: int = Query(1, ge=1, description="Page number (starts at 1)"),
     page_size: int = Query(10, ge=1, le=100, description="Items per page (max 100)"),
+    project_id: Optional[int] = Query(
+        None, description="Optional project ID for compatibility scoring"
+    ),
     db: Session = Depends(get_db),
 ):
     """Filter investors based on various criteria (paginated)
 
     Returns investor-fund combinations as separate rows.
     Queries the funds table to find matching funds.
+
+    If project_id is provided, calculates compatibility scores for each investor.
     """
 
+    # Load project if project_id provided
+    project = None
+    if project_id is not None:
+        project = (
+            db.query(ProjectTable)
+            .options(selectinload(ProjectTable.sector))
+            .filter(ProjectTable.id == project_id)
+            .first()
+        )
+        if not project:
+            raise HTTPException(status_code=404, detail="Project not found")
+
     # Start with base query on funds table
     query = db.query(FundTable).options(
         selectinload(FundTable.investor).selectinload(
@@ -212,6 +255,13 @@ def filter_investors(
     for fund in funds:
         investor = fund.investor
 
+        # Calculate compatibility score if project provided
+        compatibility_score = 1.0
+        if project is not None:
+            compatibility_score = calculate_project_investor_compatibility(
+                project=project, investor=investor, use_funds=True
+            )
+
         # Get top 3 portfolio companies (id and name only)
         portfolio_companies = [
             CompanyMinimal(id=company.id, name=company.name)
@@ -243,7 +293,7 @@ def filter_investors(
             stage_focus=stage_focus,
             portfolio_companies=portfolio_companies,
             sectors=fund_sectors,
-            compatibility_score=1.0,
+            compatibility_score=compatibility_score,
         )
         investment_responses.append(investment_response)
 

app/services/compatibility_score.py

@@ -0,0 +1,509 @@
+"""
+Compatibility Score Service
+
+This module calculates compatibility scores between projects and investors.
+The scoring system evaluates multiple dimensions to determine how well a project
+matches with an investor's investment criteria.
+"""
+
+from typing import List, Optional, Tuple
+
+from db.models import FundTable, InvestorTable, ProjectTable
+
+
+def calculate_project_investor_compatibility(
+    project: ProjectTable, investor: InvestorTable, use_funds: bool = True
+) -> float:
+    """
+    Calculate compatibility score between a project and an investor.
+
+    Args:
+        project: The project to evaluate
+        investor: The investor to compare against
+        use_funds: If True, evaluates against investor's funds. If False, uses investor-level data.
+
+    Returns:
+        A score between 0 and 1, where 1 is perfect match
+
+    Scoring breakdown (out of 100 points):
+        - Investment Stage Match: 30 points
+        - Sector Overlap: 30 points
+        - Geographic Match: 20 points
+        - Valuation/Check Size Fit: 20 points
+    """
+    if use_funds and investor.funds:
+        # Calculate score for each fund and return the highest
+        max_score = 0.0
+        for fund in investor.funds:
+            fund_score = _calculate_project_fund_compatibility(project, fund)
+            max_score = max(max_score, fund_score)
+        return max_score
+    else:
+        # Use investor-level data (fallback)
+        return _calculate_project_investor_direct_compatibility(project, investor)
+
+
+def calculate_project_investors_compatibility(
+    project: ProjectTable, investors: List[InvestorTable], use_funds: bool = True
+) -> List[Tuple[InvestorTable, float]]:
+    """
+    Calculate compatibility scores between a project and multiple investors.
+
+    Args:
+        project: The project to evaluate
+        investors: List of investors to compare against
+        use_funds: If True, evaluates against investors' funds. If False, uses investor-level data.
+
+    Returns:
+        List of tuples (investor, score) sorted by score descending
+    """
+    scored_investors = []
+
+    for investor in investors:
+        score = calculate_project_investor_compatibility(project, investor, use_funds)
+        scored_investors.append((investor, score))
+
+    # Sort by score descending
+    scored_investors.sort(key=lambda x: x[1], reverse=True)
+
+    return scored_investors
+
+
+def _calculate_project_fund_compatibility(
+    project: ProjectTable, fund: FundTable
+) -> float:
+    """
+    Calculate compatibility score between a project and a specific fund.
+
+    Scoring breakdown:
+        - Investment Stage Match: 30 points (all or nothing if stage exists)
+        - Sector Overlap: 30 points (proportional to overlap)
+        - Geographic Match: 20 points (exact=20, partial=10, none=0)
+        - Valuation/Check Size Fit: 20 points (proportional to fit)
+
+    Returns:
+        A score between 0 and 1
+    """
+    total_score = 0
+    max_score = 100
+
+    # 1. Investment Stage Match (30 points)
+    stage_score = 0
+    if project.stage and fund.investment_stages:
+        # Check if project stage matches any of the fund's investment stages
+        fund_stage_names = {stage.name for stage in fund.investment_stages}
+        # Convert project.stage enum to string for comparison
+        project_stage_name = (
+            project.stage.value
+            if hasattr(project.stage, "value")
+            else str(project.stage)
+        )
+
+        if project_stage_name in fund_stage_names:
+            stage_score = 30
+        else:
+            # Partial credit for adjacent stages
+            stage_score = _calculate_stage_proximity(
+                project_stage_name, fund_stage_names
+            )
+
+    total_score += stage_score
+
+    # 2. Sector Overlap (30 points)
+    sector_score = 0
+    if project.sector and fund.sectors:
+        project_sector_ids = {sector.id for sector in project.sector}
+        fund_sector_ids = {sector.id for sector in fund.sectors}
+
+        if project_sector_ids and fund_sector_ids:
+            common_sectors = project_sector_ids.intersection(fund_sector_ids)
+            # Score based on what percentage of project sectors are covered by fund
+            overlap_ratio = len(common_sectors) / len(project_sector_ids)
+            sector_score = int(30 * overlap_ratio)
+
+    total_score += sector_score
+
+    # 3. Geographic Match (20 points)
+    geo_score = 0
+    if project.location and fund.geographic_focus:
+        project_location_lower = project.location.lower()
+        fund_geo_lower = fund.geographic_focus.lower()
+
+        # Exact match
+        if project_location_lower == fund_geo_lower:
+            geo_score = 20
+        # Partial match (one contains the other)
+        elif (
+            project_location_lower in fund_geo_lower
+            or fund_geo_lower in project_location_lower
+        ):
+            geo_score = 10
+        # Check for common geographic terms
+        elif _check_geographic_overlap(project_location_lower, fund_geo_lower):
+            geo_score = 5
+
+    total_score += geo_score
+
+    # 4. Valuation/Check Size Fit (20 points)
+    valuation_score = 0
+    if project.valuation and fund.check_size_lower and fund.check_size_upper:
+        # Check if project valuation falls within or near the check size range
+        # Typically, check size is a fraction of valuation (e.g., 10-20%)
+        # We'll assume check size represents potential investment amount
+
+        if fund.check_size_lower <= project.valuation <= fund.check_size_upper:
+            # Valuation is within the check size range (might be too small)
+            valuation_score = 10
+        else:
+            # Check if the check size is reasonable for this valuation
+            # Typical investment is 10-30% of valuation
+            reasonable_valuation_min = fund.check_size_lower * 3  # Investing ~33%
+            reasonable_valuation_max = fund.check_size_upper * 10  # Investing ~10%
+
+            if (
+                reasonable_valuation_min
+                <= project.valuation
+                <= reasonable_valuation_max
+            ):
+                # Perfect fit
+                valuation_score = 20
+            elif project.valuation < reasonable_valuation_min:
+                # Project might be too small
+                ratio = (
+                    project.valuation / reasonable_valuation_min
+                    if reasonable_valuation_min > 0
+                    else 0
+                )
+                valuation_score = int(10 * ratio)
+            else:
+                # Project might be too large
+                ratio = (
+                    reasonable_valuation_max / project.valuation
+                    if project.valuation > 0
+                    else 0
+                )
+                valuation_score = int(10 * ratio)
+
+    total_score += valuation_score
+
+    # Convert to 0-1 scale
+    return total_score / max_score
+
+
+def _calculate_project_investor_direct_compatibility(
+    project: ProjectTable, investor: InvestorTable
+) -> float:
+    """
+    Calculate compatibility using investor-level data (fallback when no funds available).
+
+    Uses the same scoring system but with investor-level attributes.
+    """
+    total_score = 0
+    max_score = 100
+
+    # 1. Investment Stage - Skip this since investors don't have a direct stage field
+    # We could add 30 points to other categories, but for consistency, we'll leave it as 0
+    stage_score = 0
+    total_score += stage_score
+
+    # 2. Sector Overlap (30 points)
+    sector_score = 0
+    if project.sector and investor.sectors:
+        project_sector_ids = {sector.id for sector in project.sector}
+        investor_sector_ids = {sector.id for sector in investor.sectors}
+
+        if project_sector_ids and investor_sector_ids:
+            common_sectors = project_sector_ids.intersection(investor_sector_ids)
+            overlap_ratio = len(common_sectors) / len(project_sector_ids)
+            sector_score = int(30 * overlap_ratio)
+
+    total_score += sector_score
+
+    # 3. Geographic Match (20 points)
+    geo_score = 0
+    if project.location and investor.geographic_focus:
+        project_location_lower = project.location.lower()
+        investor_geo_lower = investor.geographic_focus.lower()
+
+        if project_location_lower == investor_geo_lower:
+            geo_score = 20
+        elif (
+            project_location_lower in investor_geo_lower
+            or investor_geo_lower in project_location_lower
+        ):
+            geo_score = 10
+        elif _check_geographic_overlap(project_location_lower, investor_geo_lower):
+            geo_score = 5
+
+    total_score += geo_score
+
+    # 4. Valuation/Check Size Fit (20 points)
+    valuation_score = 0
+    if project.valuation and investor.check_size_lower and investor.check_size_upper:
+        reasonable_valuation_min = investor.check_size_lower * 3
+        reasonable_valuation_max = investor.check_size_upper * 10
+
+        if reasonable_valuation_min <= project.valuation <= reasonable_valuation_max:
+            valuation_score = 20
+        elif project.valuation < reasonable_valuation_min:
+            ratio = (
+                project.valuation / reasonable_valuation_min
+                if reasonable_valuation_min > 0
+                else 0
+            )
+            valuation_score = int(10 * ratio)
+        else:
+            ratio = (
+                reasonable_valuation_max / project.valuation
+                if project.valuation > 0
+                else 0
+            )
+            valuation_score = int(10 * ratio)
+
+    total_score += valuation_score
+
+    # Convert to 0-1 scale
+    return total_score / max_score
+
+
+def _calculate_stage_proximity(project_stage: str, fund_stages: set) -> int:
+    """
+    Calculate proximity score between project stage and fund stages.
+    Awards partial credit for adjacent investment stages.
+
+    Stage progression: SEED -> SERIES_A -> SERIES_B -> SERIES_C -> GROWTH -> LATE_STAGE
+
+    Returns:
+        Score from 0-15 (half credit for adjacent stages)
+    """
+    stage_order = ["SEED", "SERIES_A", "SERIES_B", "SERIES_C", "GROWTH", "LATE_STAGE"]
+
+    try:
+        project_idx = stage_order.index(project_stage)
+    except ValueError:
+        return 0
+
+    # Check for adjacent stages
+    adjacent_stages = []
+    if project_idx > 0:
+        adjacent_stages.append(stage_order[project_idx - 1])
+    if project_idx < len(stage_order) - 1:
+        adjacent_stages.append(stage_order[project_idx + 1])
+
+    for stage in fund_stages:
+        if stage in adjacent_stages:
+            return 15  # Half credit for adjacent stage
+
+    return 0
+
+
+def _check_geographic_overlap(location1: str, location2: str) -> bool:
+    """
+    Check for common geographic terms between two locations.
+
+    Examples:
+        - "San Francisco, CA" and "California" -> True
+        - "New York" and "USA" -> True (if both contain USA/US)
+        - "London, UK" and "United Kingdom" -> True
+    """
+    # Common geographic groupings
+    geo_groups = [
+        ["usa", "us", "united states", "america"],
+        ["uk", "united kingdom", "britain"],
+        ["california", "ca"],
+        ["new york", "ny"],
+        ["texas", "tx"],
+        ["europe", "eu"],
+        ["asia", "asian"],
+        ["africa", "african"],
+    ]
+
+    for group in geo_groups:
+        found_in_1 = any(term in location1 for term in group)
+        found_in_2 = any(term in location2 for term in group)
+        if found_in_1 and found_in_2:
+            return True
+
+    return False
+
+
+def get_top_compatible_investors(
+    project: ProjectTable,
+    investors: List[InvestorTable],
+    limit: int = 10,
+    min_score: float = 0.0,
+    use_funds: bool = True,
+) -> List[Tuple[InvestorTable, float]]:
+    """
+    Get the top N most compatible investors for a project.
+
+    Args:
+        project: The project to find investors for
+        investors: List of all available investors
+        limit: Maximum number of investors to return
+        min_score: Minimum compatibility score threshold (0-1)
+        use_funds: If True, evaluates against investors' funds
+
+    Returns:
+        List of tuples (investor, score) sorted by score descending,
+        limited to 'limit' items and filtered by min_score
+    """
+    scored_investors = calculate_project_investors_compatibility(
+        project, investors, use_funds
+    )
+
+    # Filter by minimum score
+    filtered_investors = [
+        (investor, score) for investor, score in scored_investors if score >= min_score
+    ]
+
+    # Return top N
+    return filtered_investors[:limit]
+
+
+def get_compatibility_score_breakdown(
+    project: ProjectTable, investor: InvestorTable, fund: Optional[FundTable] = None
+) -> dict:
+    """
+    Get a detailed breakdown of the compatibility score components.
+
+    Useful for debugging or showing users why a particular score was calculated.
+
+    Returns:
+        Dictionary with score components and explanations
+    """
+    if fund:
+        total_score = 0
+
+        # Stage score
+        stage_score = 0
+        stage_match = False
+        if project.stage and fund.investment_stages:
+            fund_stage_names = {stage.name for stage in fund.investment_stages}
+            project_stage_name = (
+                project.stage.value
+                if hasattr(project.stage, "value")
+                else str(project.stage)
+            )
+            if project_stage_name in fund_stage_names:
+                stage_score = 30
+                stage_match = True
+            else:
+                stage_score = _calculate_stage_proximity(
+                    project_stage_name, fund_stage_names
+                )
+
+        # Sector score
+        sector_score = 0
+        matching_sectors = []
+        if project.sector and fund.sectors:
+            project_sector_ids = {sector.id for sector in project.sector}
+            fund_sector_ids = {sector.id for sector in fund.sectors}
+            if project_sector_ids and fund_sector_ids:
+                common_sectors = project_sector_ids.intersection(fund_sector_ids)
+                matching_sectors = [
+                    s.name for s in fund.sectors if s.id in common_sectors
+                ]
+                overlap_ratio = len(common_sectors) / len(project_sector_ids)
+                sector_score = int(30 * overlap_ratio)
+
+        # Geographic score
+        geo_score = 0
+        geo_match_type = "none"
+        if project.location and fund.geographic_focus:
+            project_location_lower = project.location.lower()
+            fund_geo_lower = fund.geographic_focus.lower()
+            if project_location_lower == fund_geo_lower:
+                geo_score = 20
+                geo_match_type = "exact"
+            elif (
+                project_location_lower in fund_geo_lower
+                or fund_geo_lower in project_location_lower
+            ):
+                geo_score = 10
+                geo_match_type = "partial"
+            elif _check_geographic_overlap(project_location_lower, fund_geo_lower):
+                geo_score = 5
+                geo_match_type = "regional"
+
+        # Valuation score
+        valuation_score = 0
+        valuation_fit = "unknown"
+        if project.valuation and fund.check_size_lower and fund.check_size_upper:
+            reasonable_valuation_min = fund.check_size_lower * 3
+            reasonable_valuation_max = fund.check_size_upper * 10
+            if (
+                reasonable_valuation_min
+                <= project.valuation
+                <= reasonable_valuation_max
+            ):
+                valuation_score = 20
+                valuation_fit = "perfect"
+            elif project.valuation < reasonable_valuation_min:
+                ratio = (
+                    project.valuation / reasonable_valuation_min
+                    if reasonable_valuation_min > 0
+                    else 0
+                )
+                valuation_score = int(10 * ratio)
+                valuation_fit = "too_small"
+            else:
+                ratio = (
+                    reasonable_valuation_max / project.valuation
+                    if project.valuation > 0
+                    else 0
+                )
+                valuation_score = int(10 * ratio)
+                valuation_fit = "too_large"
+
+        total_score = stage_score + sector_score + geo_score + valuation_score
+
+        return {
+            "total_score": total_score / 100,
+            "breakdown": {
+                "stage": {
+                    "score": stage_score,
+                    "max_score": 30,
+                    "match": stage_match,
+                    "project_stage": project.stage.value if project.stage else None,
+                    "fund_stages": [s.name for s in fund.investment_stages]
+                    if fund.investment_stages
+                    else [],
+                },
+                "sector": {
+                    "score": sector_score,
+                    "max_score": 30,
+                    "matching_sectors": matching_sectors,
+                    "project_sectors": [s.name for s in project.sector]
+                    if project.sector
+                    else [],
+                    "fund_sectors": [s.name for s in fund.sectors]
+                    if fund.sectors
+                    else [],
+                },
+                "geography": {
+                    "score": geo_score,
+                    "max_score": 20,
+                    "match_type": geo_match_type,
+                    "project_location": project.location,
+                    "fund_geography": fund.geographic_focus,
+                },
+                "valuation": {
+                    "score": valuation_score,
+                    "max_score": 20,
+                    "fit": valuation_fit,
+                    "project_valuation": project.valuation,
+                    "fund_check_size_range": f"{fund.check_size_lower}-{fund.check_size_upper}"
+                    if fund.check_size_lower
+                    else None,
+                },
+            },
+        }
+    else:
+        # Investor-level breakdown (simplified)
+        return {
+            "total_score": _calculate_project_investor_direct_compatibility(
+                project, investor
+            ),
+            "note": "Using investor-level data (no specific fund selected)",
+        }

app/services/crm.py

@@ -0,0 +1,260 @@
+import os
+import sys
+
+import requests
+
+
+class FolkAPI:
+    BASE_URL = "https://api.folk.app/v1"
+
+    def __init__(self, api_key: str):
+        self.headers = {"Authorization": f"Bearer {api_key}"}
+
+    def get_groups(self):
+        """Fetch all groups from Folk."""
+        url = f"{self.BASE_URL}/groups"
+        response = requests.get(url, headers=self.headers)
+        response.raise_for_status()
+        return response.json()
+
+    def create_company(
+        self,
+        name: str,
+        group_id: str = None,
+        website: str = None,
+        linkedin_url: str = None,
+        description: str = None,
+        emails=None,
+        phones=None,
+        addresses=None,
+        urls=None,
+        custom_field_values=None,
+        groups=None,
+        **kwargs,
+    ):
+        """Create a company (investor) in a specific group.
+
+        This method builds a payload matching Folk's Create Company API:
+        https://developer.folk.app/api-reference/companies/create-a-company
+
+        It keeps backward compatibility with the previous `group_id`,
+        `website` and `linkedin_url` arguments.
+        """
+        url = f"{self.BASE_URL}/companies"
+
+        # Build the top-level payload expected by Folk
+        data = {"name": name}
+        if description:
+            data["description"] = description
+
+        # Groups: prefer explicit `groups`, else fall back to `group_id`
+        if groups:
+            # Accept either list of ids or list of dicts
+            formatted = []
+            for g in groups:
+                if isinstance(g, dict) and g.get("id"):
+                    formatted.append({"id": g["id"]})
+                else:
+                    formatted.append({"id": str(g)})
+            data["groups"] = formatted
+        elif group_id:
+            data["groups"] = [{"id": group_id}]
+
+        # Helper to normalize single or multiple inputs into lists
+        def _to_list(val):
+            if val is None:
+                return None
+            if isinstance(val, (list, tuple)):
+                return [v for v in val if v is not None]
+            return [val]
+
+        # URLs: include website and linkedin_url if provided and merge with urls
+        urls_list = _to_list(urls) or []
+        if website:
+            urls_list.append(website)
+        if linkedin_url:
+            urls_list.append(linkedin_url)
+        if urls_list:
+            data["urls"] = urls_list
+
+        # Emails/phones/addresses
+        emails_list = _to_list(emails)
+        if emails_list:
+            data["emails"] = emails_list
+        phones_list = _to_list(phones)
+        if phones_list:
+            data["phones"] = phones_list
+        addresses_list = _to_list(addresses)
+        if addresses_list:
+            data["addresses"] = addresses_list
+
+        # Custom field values follow the API's structure
+        if custom_field_values:
+            data["customFieldValues"] = custom_field_values
+
+        # Allow passing any additional top-level fields via kwargs (careful)
+        for k, v in kwargs.items():
+            # don't overwrite keys we explicitly set
+            if k not in data:
+                data[k] = v
+
+        response = requests.post(url, headers=self.headers, json=data)
+        response.raise_for_status()
+        return response.json()
+
+    def create_person(
+        self,
+        first_name: str,
+        last_name: str,
+        email: str = None,
+        company_id: str = None,
+        group_id: str = None,
+        companies=None,
+        emails=None,
+        phones=None,
+        addresses=None,
+        urls=None,
+        custom_field_values=None,
+        groups=None,
+        **kwargs,
+    ):
+        """Create a person in the workspace.
+
+        Builds payload matching Folk's Create Person API: use camelCase
+        keys (firstName, lastName, groups, companies, emails, etc.).
+        Keeps backward compatibility with `company_id` and `group_id`.
+        """
+        url = f"{self.BASE_URL}/people"
+
+        data = {"firstName": first_name, "lastName": last_name}
+
+        # Groups: explicit `groups` preferred, else fallback to `group_id`
+        if groups:
+            formatted = []
+            for g in groups:
+                if isinstance(g, dict) and g.get("id"):
+                    formatted.append({"id": g["id"]})
+                else:
+                    formatted.append({"id": str(g)})
+            data["groups"] = formatted
+        elif group_id:
+            data["groups"] = [{"id": group_id}]
+
+        # Companies: keep backward compatibility with company_id
+        if companies:
+            formatted = []
+            for c in companies:
+                if isinstance(c, dict):
+                    formatted.append(c)
+                elif isinstance(c, str):
+                    # treat as id
+                    formatted.append({"id": c})
+            if formatted:
+                data["companies"] = formatted
+        elif company_id:
+            data["companies"] = [{"id": company_id}]
+
+        # Helper to normalize into lists
+        def _to_list(val):
+            if val is None:
+                return None
+            if isinstance(val, (list, tuple)):
+                return [v for v in val if v is not None]
+            return [val]
+
+        emails_list = _to_list(emails) or []
+        if email:
+            emails_list.insert(0, email)
+        if emails_list:
+            data["emails"] = emails_list
+
+        phones_list = _to_list(phones)
+        if phones_list:
+            data["phones"] = phones_list
+        addresses_list = _to_list(addresses)
+        if addresses_list:
+            data["addresses"] = addresses_list
+        urls_list = _to_list(urls)
+        if urls_list:
+            data["urls"] = urls_list
+
+        if custom_field_values:
+            data["customFieldValues"] = custom_field_values
+
+        # Allow passthrough of other top-level fields in kwargs
+        for k, v in kwargs.items():
+            if k not in data:
+                data[k] = v
+
+        response = requests.post(url, headers=self.headers, json=data)
+        response.raise_for_status()
+        return response.json()
+
+
+# Prefer getting the API key from the environment. If not set, fall back to the
+# existing (hard-coded) key so behavior is unchanged for now.
+DEFAULT_API_KEY = "FOLKfIGXuv74ML9EAajxyiUR39ePaNrZ"
+api_key = os.environ.get("FOLK_API_KEY", DEFAULT_API_KEY)
+
+folk = FolkAPI(api_key=api_key)
+
+
+def example_flow():
+    # Step 1: Get groups
+    groups = folk.get_groups()
+    print(groups)
+
+    # Safely dig into the returned structure. The API returns groups under
+    # groups['data']['items'] (not groups['data'][0]). Handle missing/empty.
+    items = groups.get("data", {}).get("items", [])
+    if not items:
+        print("No groups returned by Folk API.")
+        sys.exit(1)
+
+    # Choose the first group as an example
+    group_id = items[0].get("id")
+    if not group_id:
+        print("No id found for the first group item.")
+        sys.exit(1)
+
+    # Step 2: Choose a group_id and create a company
+    company = folk.create_company(
+        name="2050 Investment Partners",
+        group_id=group_id,
+        website="https://2050.com",
+        linkedin_url="https://linkedin.com/company/2050-investments",
+    )
+
+    # Step 3: Add a person to the same group or company
+    person = folk.create_person(
+        first_name="John",
+        last_name="Doe",
+        email="john@2050.com",
+        company_id=company.get("data", {}).get("id"),
+        group_id=group_id,
+    )
+
+    print("Created company:", company)
+    print("Created person:", person)
+
+
+if __name__ == "__main__":
+    try:
+        example_flow()
+    except requests.HTTPError as e:
+        # Try to include response body for easier debugging if available
+        resp = getattr(e, "response", None)
+        if resp is not None:
+            try:
+                body = resp.text
+            except Exception:
+                body = "<unreadable response body>"
+            print("HTTP error while talking to Folk API:", e)
+            print("Response status:", resp.status_code)
+            print("Response body:", body)
+        else:
+            print("HTTP error while talking to Folk API:", e)
+        sys.exit(1)
+    except Exception as e:  # pragma: no cover - top-level safety
+        print("Unexpected error:", e)
+        sys.exit(1)

app/services/querying.py

@@ -1,8 +1,8 @@
 import os
-from typing import List
+from typing import List, Optional
 
 from db.db import DATABASE_URL, get_db
-from db.models import FundTable, InvestorTable
+from db.models import FundTable, InvestorTable, ProjectTable
 from langchain import hub
 from langchain_community.agent_toolkits import SQLDatabaseToolkit
 from langchain_community.utilities import SQLDatabase
@@ -16,6 +16,8 @@ from schemas.router_schemas import (
 )
 from sqlalchemy.orm import selectinload
 
+from services.compatibility_score import calculate_project_investor_compatibility
+
 # Connect to SQLite
 prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
 db = SQLDatabase.from_uri(DATABASE_URL)
@@ -44,8 +46,15 @@ class QueryProcessor:
             prompt=system_message_updated,
         )
 
-    def process_query(self, question: str) -> PaginatedResponse[InvestmentResponse]:
-        """Process a query using the LLM and return investment response data."""
+    def process_query(
+        self, question: str, project_id: Optional[int] = None
+    ) -> PaginatedResponse[InvestmentResponse]:
+        """Process a query using the LLM and return investment response data.
+
+        Args:
+            question: The natural language query to process
+            project_id: Optional project ID for compatibility scoring
+        """
         # Let the LLM handle all database interactions and filtering to get fund IDs
         response = self.agent.invoke(
             {"messages": [("user", question)]},
@@ -60,7 +69,7 @@ class QueryProcessor:
         fund_ids = self._extract_fund_ids_from_response(ai_response)
 
         # Fetch full fund data with investor relationships using the IDs
-        return self._fetch_funds_by_ids(fund_ids)
+        return self._fetch_funds_by_ids(fund_ids, project_id)
 
     def _extract_fund_ids_from_response(self, ai_response: str) -> List[int]:
         """Extract fund IDs from AI response."""
@@ -85,10 +94,15 @@ class QueryProcessor:
         return fund_ids
 
     def _fetch_funds_by_ids(
-        self, fund_ids: List[int]
+        self, fund_ids: List[int], project_id: Optional[int] = None
     ) -> PaginatedResponse[InvestmentResponse]:
         """Fetch funds with all their relationships from the database using fund IDs.
-        Constructs response similar to read_investors but starting from funds."""
+        Constructs response similar to read_investors but starting from funds.
+
+        Args:
+            fund_ids: List of fund IDs to fetch
+            project_id: Optional project ID for compatibility scoring
+        """
         if not fund_ids:
             return PaginatedResponse(
                 items=[],
@@ -102,6 +116,16 @@ class QueryProcessor:
         db_session = next(get_db())
 
         try:
+            # Load project if project_id provided
+            project = None
+            if project_id is not None:
+                project = (
+                    db_session.query(ProjectTable)
+                    .options(selectinload(ProjectTable.sector))
+                    .filter(ProjectTable.id == project_id)
+                    .first()
+                )
+
             # Query funds with all necessary relationships loaded
             funds = (
                 db_session.query(FundTable)
@@ -127,6 +151,13 @@ class QueryProcessor:
             for fund in funds:
                 investor = fund.investor
 
+                # Calculate compatibility score if project provided
+                compatibility_score = 1.0
+                if project is not None:
+                    compatibility_score = calculate_project_investor_compatibility(
+                        project=project, investor=investor, use_funds=True
+                    )
+
                 # Get top 3 portfolio companies (id and name only)
                 portfolio_companies = [
                     CompanyMinimal(id=company.id, name=company.name)
@@ -158,7 +189,7 @@ class QueryProcessor:
                     stage_focus=stage_focus,
                     portfolio_companies=portfolio_companies,
                     sectors=fund_sectors,
-                    compatibility_score=1.0,
+                    compatibility_score=compatibility_score,
                 )
                 investment_responses.append(investment_response)