Add 4 AI bookkeeping features

Feature 7: Bank Rec Auto-Match — AiSuggestMatches endpoint scores uncleared
transactions vs statement ending balance; AI Auto-Match panel in Reconcile.cshtml
with confidence highlights and Apply All button.

Feature 8: Late Payment Prediction — PredictLatePayments endpoint scores open AR
customers by risk (high/medium/low) using historical avg-days-to-pay + late rate;
rendered as badge table in AR Aging view via ar-aging-ai.js.

Feature 9: Natural Language Financial Queries — FinancialQuery GET page + RunFinancialQuery
POST; 12-month context snapshot pre-loaded; answers grounded in real data with
supporting facts, follow-up suggestions, session history, and example chips.

Feature 10: Recurring Bill Detection — RunRecurringDetection scans 12 months of bills
for vendor payment patterns (monthly/quarterly/annual); card grid view in Bills/RecurringDetection.cshtml
with confidence badges, next-expected-date, and suggested actions.

Supporting: 4 new DTO groups in AccountingAiDtos.cs, 4 method signatures in
IAccountingAiService.cs, 4 implementations in AccountingAiService.cs, 4 new
AiFeatures constants, 2 new Landing page AI report cards.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/PowderCoating.Infrastructure/Services/AccountingAiService.cs

@@ -902,4 +902,454 @@ Account Spend Trends (this month vs historical):
             return new AnomalyDetectionResult { Success = false, ErrorMessage = "An error occurred while running the analysis." };
         }
     }
+
+    // ── Feature 7: Bank Rec Auto-Match ────────────────────────────────────────
+
+    /// <summary>
+    /// Suggests which uncleared bank rec transactions to mark as cleared to close the gap
+    /// between the current running balance and the statement ending balance. The items list
+    /// includes both deposits and payments with their direction tag so Claude can reason about
+    /// net effect. Confidence scores reflect how cleanly each item contributes to reaching the
+    /// target ending balance — items that together sum close to the required difference score
+    /// higher than items that alone overshoot. MaxTokens is 1024; the response is typically
+    /// compact because we only need entity-type/id pairs plus a short reason per item.
+    /// </summary>
+    public async Task<AutoMatchResult> AutoMatchReconciliationAsync(AutoMatchRequest request)
+    {
+        var apiKey = GetApiKey();
+        if (apiKey == null)
+            return new AutoMatchResult { Success = false, ErrorMessage = "Anthropic API key is not configured." };
+
+        try
+        {
+            var systemPrompt = @"You are a bank reconciliation assistant for a powder coating business.
+Given a list of uncleared transactions and a target statement ending balance, suggest which transactions
+to mark as cleared so that: Beginning Balance + cleared deposits - cleared payments = Statement Ending Balance.
+
+Respond ONLY with a valid JSON object — no markdown, no explanation.
+
+Schema:
+{
+  ""suggestedCleared"": [
+    {
+      ""entityType"": ""Payment"" | ""BillPayment"" | ""Expense"",
+      ""entityId"": number,
+      ""confidence"": number (0.0 to 1.0),
+      ""reason"": ""string — one sentence why this item should be cleared""
+    }
+  ],
+  ""insights"": [""string"", ...]
+}
+
+Rules:
+- Select the combination of items whose net effect (deposits minus payments) gets closest to the difference needed
+- Difference needed = statementEndingBalance - beginningBalance
+- confidence 0.9-1.0: item clearly belongs in this period (date and amount both fit)
+- confidence 0.6-0.89: likely but not certain
+- confidence below 0.6: possible but uncertain — include only if needed to close the gap
+- insights: 2-4 observations about patterns or items that need manual review
+- Do NOT suggest clearing items you are uncertain about just to force a zero balance";
+
+            var itemsJson = JsonSerializer.Serialize(request.UnclearedItems);
+            var needed = request.StatementEndingBalance - request.BeginningBalance;
+
+            var userPrompt = $@"Suggest which transactions to clear for this bank reconciliation.
+
+Beginning Balance: {request.BeginningBalance:F2}
+Statement Ending Balance: {request.StatementEndingBalance:F2}
+Difference needed (deposits - payments): {needed:F2}
+
+Uncleared transactions:
+{itemsJson}";
+
+            var client = new AnthropicClient(apiKey);
+            var messageParams = new MessageParameters
+            {
+                Model = Model,
+                MaxTokens = 1024,
+                SystemMessage = systemPrompt,
+                Messages = new List<Message>
+                {
+                    new Message
+                    {
+                        Role = RoleType.User,
+                        Content = new List<ContentBase> { new TextContent { Text = userPrompt } }
+                    }
+                }
+            };
+
+            var response = await SendAsync(client, messageParams);
+            var rawText = response.FirstMessage?.Text
+                ?? response.Content?.OfType<TextContent>().FirstOrDefault()?.Text
+                ?? "";
+            if (string.IsNullOrWhiteSpace(rawText))
+                return new AutoMatchResult { Success = false, ErrorMessage = "Empty response from AI." };
+
+            var raw = StripJsonFences(rawText);
+            var parsed = JsonSerializer.Deserialize<ClaudeAutoMatchResponse>(raw, JsonOpts);
+            if (parsed == null)
+                return new AutoMatchResult { Success = false, ErrorMessage = "Could not parse AI response." };
+
+            return new AutoMatchResult
+            {
+                Success = true,
+                SuggestedCleared = (parsed.SuggestedCleared ?? new()).Select(s => new AutoMatchSuggestion
+                {
+                    EntityType = s.EntityType,
+                    EntityId   = s.EntityId,
+                    Confidence = s.Confidence,
+                    Reason     = s.Reason
+                }).ToList(),
+                Insights = parsed.Insights ?? new()
+            };
+        }
+        catch (OperationCanceledException)
+        {
+            _logger.LogWarning("Claude AI bank rec auto-match timed out after 60 seconds");
+            return new AutoMatchResult { Success = false, ErrorMessage = "The AI service did not respond in time. Please try again." };
+        }
+        catch (Exception ex)
+        {
+            _logger.LogError(ex, "Error running bank rec auto-match with AI");
+            return new AutoMatchResult { Success = false, ErrorMessage = "An error occurred while running auto-match." };
+        }
+    }
+
+    // ── Feature 8: Late Payment Prediction ────────────────────────────────────
+
+    /// <summary>
+    /// Predicts payment risk per open AR customer by combining current overdue status with
+    /// historical behavior metrics (avg days to pay, late rate). The late rate is pre-calculated
+    /// as LateInvoicesAllTime / TotalInvoicesAllTime so Claude receives a 0–1 ratio rather than
+    /// raw counts, which produces more consistent confidence scoring across customers with very
+    /// different invoice volumes. Risk levels are validated against the three allowed values and
+    /// default to "medium" when Claude returns anything outside the expected set.
+    /// </summary>
+    public async Task<LatePaymentPredictionResult> PredictLatePaymentsAsync(LatePaymentPredictionRequest request)
+    {
+        var apiKey = GetApiKey();
+        if (apiKey == null)
+            return new LatePaymentPredictionResult { Success = false, ErrorMessage = "Anthropic API key is not configured." };
+
+        try
+        {
+            var systemPrompt = @"You are an accounts receivable risk analyst for a powder coating business.
+Given open AR data and each customer's historical payment behavior, predict payment risk for each customer.
+Respond ONLY with a valid JSON object — no markdown, no explanation.
+
+Schema:
+{
+  ""predictions"": [
+    {
+      ""customerName"": ""string"",
+      ""riskLevel"": ""high"" | ""medium"" | ""low"",
+      ""estimatedDaysToPayment"": number,
+      ""reasoning"": ""string — one sentence explaining the prediction""
+    }
+  ],
+  ""insights"": [""string"", ...]
+}
+
+Rules:
+- riskLevel ""high"": customer has a history of late payment AND is already overdue, or has a very high late rate
+- riskLevel ""medium"": customer is overdue but has reasonable historical performance, or is current but has a spotty history
+- riskLevel ""low"": customer typically pays on time and is not severely overdue
+- estimatedDaysToPayment: realistic estimate of additional days until payment, based on history and overdue status
+- insights: 2-4 portfolio-level observations (e.g. which customers need immediate follow-up)
+- Only include predictions for customers with open invoices";
+
+            var customersJson = JsonSerializer.Serialize(request.Customers.Select(c => new
+            {
+                c.CustomerName,
+                c.TotalOwed,
+                c.AvgDaysToPay,
+                LatePaymentRate = c.TotalInvoicesAllTime > 0
+                    ? Math.Round((double)c.LateInvoicesAllTime / c.TotalInvoicesAllTime, 2)
+                    : 0,
+                c.OpenInvoices
+            }));
+
+            var userPrompt = $@"Predict payment risk for open AR customers of {request.CompanyName}.
+
+Customer data (includes historical payment behavior):
+{customersJson}";
+
+            var client = new AnthropicClient(apiKey);
+            var messageParams = new MessageParameters
+            {
+                Model = Model,
+                MaxTokens = 1024,
+                SystemMessage = systemPrompt,
+                Messages = new List<Message>
+                {
+                    new Message
+                    {
+                        Role = RoleType.User,
+                        Content = new List<ContentBase> { new TextContent { Text = userPrompt } }
+                    }
+                }
+            };
+
+            var response = await SendAsync(client, messageParams);
+            var rawText = response.FirstMessage?.Text
+                ?? response.Content?.OfType<TextContent>().FirstOrDefault()?.Text
+                ?? "";
+            if (string.IsNullOrWhiteSpace(rawText))
+                return new LatePaymentPredictionResult { Success = false, ErrorMessage = "Empty response from AI." };
+
+            var raw = StripJsonFences(rawText);
+            var parsed = JsonSerializer.Deserialize<ClaudeLatePaymentResponse>(raw, JsonOpts);
+            if (parsed == null)
+                return new LatePaymentPredictionResult { Success = false, ErrorMessage = "Could not parse AI response." };
+
+            var validRiskLevels = new[] { "high", "medium", "low" };
+            var predictions = (parsed.Predictions ?? new()).Select(p => new LatePaymentPrediction
+            {
+                CustomerName          = p.CustomerName,
+                RiskLevel             = validRiskLevels.Contains(p.RiskLevel?.ToLowerInvariant()) ? p.RiskLevel!.ToLowerInvariant() : "medium",
+                EstimatedDaysToPayment = p.EstimatedDaysToPayment,
+                Reasoning             = p.Reasoning
+            }).ToList();
+
+            return new LatePaymentPredictionResult
+            {
+                Success     = true,
+                Predictions = predictions,
+                Insights    = parsed.Insights ?? new()
+            };
+        }
+        catch (OperationCanceledException)
+        {
+            _logger.LogWarning("Claude AI late payment prediction timed out after 60 seconds");
+            return new LatePaymentPredictionResult { Success = false, ErrorMessage = "The AI service did not respond in time. Please try again." };
+        }
+        catch (Exception ex)
+        {
+            _logger.LogError(ex, "Error predicting late payments with AI");
+            return new LatePaymentPredictionResult { Success = false, ErrorMessage = "An error occurred while predicting payment risk." };
+        }
+    }
+
+    // ── Feature 9: Natural Language Financial Queries ─────────────────────────
+
+    /// <summary>
+    /// Answers a free-text financial question using a pre-loaded snapshot of the company's
+    /// financial data. The context object is serialized to JSON and embedded in the user prompt
+    /// so Claude has concrete numbers to reason over rather than fabricating estimates. The
+    /// system prompt explicitly constrains Claude to the data provided and forbids it from
+    /// making up figures outside the snapshot — this prevents hallucination of specific dollar
+    /// amounts. RelevantFacts is a list of supporting data points Claude pulled from the context
+    /// to justify the answer, displayed below the answer in the UI so users can verify.
+    /// MaxTokens is raised to 1500 to accommodate answers with multiple supporting facts.
+    /// </summary>
+    public async Task<FinancialQueryResult> AnswerFinancialQueryAsync(FinancialQueryRequest request)
+    {
+        var apiKey = GetApiKey();
+        if (apiKey == null)
+            return new FinancialQueryResult { Success = false, ErrorMessage = "Anthropic API key is not configured." };
+
+        try
+        {
+            var systemPrompt = @"You are a financial analyst assistant for a powder coating business.
+Answer plain-English financial questions using ONLY the data provided in the context.
+Respond ONLY with a valid JSON object — no markdown, no explanation.
+
+Schema:
+{
+  ""answer"": ""string — direct, plain-English answer to the question"",
+  ""followUpSuggestion"": ""string — one optional follow-up question the user might want to ask next, or null"",
+  ""relevantFacts"": [""string"", ...]
+}
+
+Rules:
+- answer: be direct and specific with dollar amounts and percentages from the data
+- If the data does not contain enough information to answer the question, say so clearly in the answer
+- Do NOT invent or estimate figures that are not in the provided data
+- relevantFacts: 2-5 specific data points from the context that support the answer (formatted as ""Label: $X"" or ""Label: X%"")
+- followUpSuggestion: suggest the natural next question the user would want to ask, or null if not obvious
+- Keep the answer under 100 words — be concise";
+
+            var contextJson = JsonSerializer.Serialize(request.Context);
+            var userPrompt = $@"Question: {request.Question}
+
+Financial context:
+{contextJson}";
+
+            var client = new AnthropicClient(apiKey);
+            var messageParams = new MessageParameters
+            {
+                Model = Model,
+                MaxTokens = 1500,
+                SystemMessage = systemPrompt,
+                Messages = new List<Message>
+                {
+                    new Message
+                    {
+                        Role = RoleType.User,
+                        Content = new List<ContentBase> { new TextContent { Text = userPrompt } }
+                    }
+                }
+            };
+
+            var response = await SendAsync(client, messageParams);
+            var rawText = response.FirstMessage?.Text
+                ?? response.Content?.OfType<TextContent>().FirstOrDefault()?.Text
+                ?? "";
+            if (string.IsNullOrWhiteSpace(rawText))
+                return new FinancialQueryResult { Success = false, ErrorMessage = "Empty response from AI." };
+
+            var raw = StripJsonFences(rawText);
+            var parsed = JsonSerializer.Deserialize<ClaudeFinancialQueryResponse>(raw, JsonOpts);
+            if (parsed == null)
+                return new FinancialQueryResult { Success = false, ErrorMessage = "Could not parse AI response." };
+
+            return new FinancialQueryResult
+            {
+                Success              = true,
+                Answer               = parsed.Answer ?? string.Empty,
+                FollowUpSuggestion   = parsed.FollowUpSuggestion,
+                RelevantFacts        = parsed.RelevantFacts ?? new()
+            };
+        }
+        catch (OperationCanceledException)
+        {
+            _logger.LogWarning("Claude AI financial query timed out after 60 seconds");
+            return new FinancialQueryResult { Success = false, ErrorMessage = "The AI service did not respond in time. Please try again." };
+        }
+        catch (Exception ex)
+        {
+            _logger.LogError(ex, "Error answering financial query with AI");
+            return new FinancialQueryResult { Success = false, ErrorMessage = "An error occurred while answering your question." };
+        }
+    }
+
+    // ── Feature 10: Recurring Bill Detection ──────────────────────────────────
+
+    /// <summary>
+    /// Analyzes 6–12 months of historical bills to detect recurring payment patterns per vendor.
+    /// Bills are grouped by vendor in the prompt so Claude can see the full chronological series
+    /// for each vendor at a glance. The confidence field ("high"/"medium"/"low") reflects how
+    /// regular the cadence is — a bill appearing every 28–32 days for 6 consecutive months is
+    /// high confidence; 2–3 occurrences at similar amounts is medium. NextExpectedDateIso is
+    /// calculated by Claude from the pattern's most recent date plus the detected period length.
+    /// MaxTokens is 1500 to accommodate multi-vendor response objects with multiple patterns.
+    /// </summary>
+    public async Task<RecurringBillDetectionResult> DetectRecurringBillsAsync(RecurringBillDetectionRequest request)
+    {
+        var apiKey = GetApiKey();
+        if (apiKey == null)
+            return new RecurringBillDetectionResult { Success = false, ErrorMessage = "Anthropic API key is not configured." };
+
+        try
+        {
+            var systemPrompt = @"You are a recurring expense analyst for a powder coating business.
+Analyze the provided bill history to detect recurring payment patterns per vendor.
+Respond ONLY with a valid JSON object — no markdown, no explanation.
+
+Schema:
+{
+  ""patterns"": [
+    {
+      ""vendorName"": ""string"",
+      ""frequency"": ""monthly"" | ""quarterly"" | ""biannual"" | ""annual"" | ""irregular"",
+      ""typicalAmount"": number,
+      ""nextExpectedDateIso"": ""YYYY-MM-DD or null"",
+      ""confidence"": ""high"" | ""medium"" | ""low"",
+      ""description"": ""string — one sentence describing the pattern"",
+      ""suggestedAction"": ""string — one specific action to take, or null""
+    }
+  ],
+  ""insights"": [""string"", ...]
+}
+
+Rules:
+- Only report patterns with at least 2 occurrences
+- monthly: bills occurring every 25–35 days
+- quarterly: bills occurring every 80–100 days
+- biannual: bills occurring every 170–195 days
+- annual: bills occurring roughly once per year
+- irregular: a vendor bills regularly but the cadence is inconsistent
+- confidence ""high"": 4+ occurrences with consistent timing (within ±5 days of the period)
+- confidence ""medium"": 2–3 occurrences with consistent timing, or 4+ with variable timing
+- confidence ""low"": pattern is weak but worth monitoring
+- nextExpectedDateIso: estimate based on the last bill date + the detected period; null if irregular or low confidence
+- suggestedAction: e.g. ""Set a monthly reminder for this bill"" or ""Create a recurring bill template"" or null
+- insights: 2-4 portfolio-level observations about the company's recurring expense profile
+- If no recurring patterns are found, return an empty patterns array";
+
+            // Group bills by vendor for clarity in the prompt
+            var grouped = request.Bills
+                .GroupBy(b => b.VendorName)
+                .Select(g => new
+                {
+                    VendorName = g.Key,
+                    Bills = g.OrderBy(b => b.DateIso).Select(b => new { b.DateIso, b.Amount, b.BillNumber, b.Memo })
+                });
+
+            var billsJson = JsonSerializer.Serialize(grouped);
+
+            var userPrompt = $@"Detect recurring bill patterns for {request.CompanyName}.
+Data covers the last 6–12 months of bills, grouped by vendor.
+
+Bill history by vendor:
+{billsJson}";
+
+            var client = new AnthropicClient(apiKey);
+            var messageParams = new MessageParameters
+            {
+                Model = Model,
+                MaxTokens = 1500,
+                SystemMessage = systemPrompt,
+                Messages = new List<Message>
+                {
+                    new Message
+                    {
+                        Role = RoleType.User,
+                        Content = new List<ContentBase> { new TextContent { Text = userPrompt } }
+                    }
+                }
+            };
+
+            var response = await SendAsync(client, messageParams);
+            var rawText = response.FirstMessage?.Text
+                ?? response.Content?.OfType<TextContent>().FirstOrDefault()?.Text
+                ?? "";
+            if (string.IsNullOrWhiteSpace(rawText))
+                return new RecurringBillDetectionResult { Success = false, ErrorMessage = "Empty response from AI." };
+
+            var raw = StripJsonFences(rawText);
+            var parsed = JsonSerializer.Deserialize<ClaudeRecurringBillResponse>(raw, JsonOpts);
+            if (parsed == null)
+                return new RecurringBillDetectionResult { Success = false, ErrorMessage = "Could not parse AI response." };
+
+            var validConfidence = new[] { "high", "medium", "low" };
+            var validFrequency = new[] { "monthly", "quarterly", "biannual", "annual", "irregular" };
+
+            return new RecurringBillDetectionResult
+            {
+                Success  = true,
+                Patterns = (parsed.Patterns ?? new()).Select(p => new RecurringBillPattern
+                {
+                    VendorName          = p.VendorName,
+                    Frequency           = validFrequency.Contains(p.Frequency?.ToLowerInvariant()) ? p.Frequency!.ToLowerInvariant() : "irregular",
+                    TypicalAmount       = p.TypicalAmount,
+                    NextExpectedDateIso = p.NextExpectedDateIso,
+                    Confidence          = validConfidence.Contains(p.Confidence?.ToLowerInvariant()) ? p.Confidence!.ToLowerInvariant() : "medium",
+                    Description         = p.Description,
+                    SuggestedAction     = p.SuggestedAction
+                }).ToList(),
+                Insights = parsed.Insights ?? new()
+            };
+        }
+        catch (OperationCanceledException)
+        {
+            _logger.LogWarning("Claude AI recurring bill detection timed out after 60 seconds");
+            return new RecurringBillDetectionResult { Success = false, ErrorMessage = "The AI service did not respond in time. Please try again." };
+        }
+        catch (Exception ex)
+        {
+            _logger.LogError(ex, "Error detecting recurring bills with AI");
+            return new RecurringBillDetectionResult { Success = false, ErrorMessage = "An error occurred while analyzing bill patterns." };
+        }
+    }
 }