You run a 51-200 person fintech in Austria. Your finance and accounting team handles invoice processing, vendor reconciliation, and payment queries through SAP or Microsoft Dynamics ERP. Every week, a portion of your support tickets are routine: \u2018What is the status of invoice INV-2024-0847?\u2019, \u2018Why was vendor X\u2019s payment delayed?\u2019, \u2018What are the payment terms for this GL account?\u2019 Each of these consumes 8-15 minutes of an analyst\u2019s time, and the cost per ticket compounds across departments as you scale. The problem is not that your ERP is broken. It is that the knowledge needed to answer these questions is locked inside the ERP, and your team has to open the system, search, and interpret the data manually. A retrieval-augmented knowledge assistant built on pgvector embeddings search, integrated into your existing ERP via its API, can answer 60-75% of these queries without a human opening the system. The goal is not to replace your ERP. It is to lower the cost per support ticket by removing the manual search-and-interpret step from the workflow, while keeping a human in the loop for anything that touches money or a contract.<\/p>\n
Before you start step 1, confirm the following are in place:<\/p>\n
Run a process audit on your invoice-handling workflow. Map every step from invoice receipt to payment, and tag each step with the time it consumes and the error rate. For a typical Austrian fintech, the audit reveals that 40-60% of the cycle time is spent on data entry, status lookups, and reconciliation checks that do not require judgment. Identify the three to five workflows where the manual search-and-interpret step is the bottleneck. Document the ERP objects involved: which SAP tables or Dynamics entities hold the invoice, vendor, and GL account data. This audit output becomes the scope for the pilot. Do not skip this step. If you build the RAG assistant on the wrong workflow, the pilot will not reduce cost per ticket, and you will have spent a month on a system nobody uses.<\/p>\n
Design the pgvector schema that will store your invoice and ERP data as embeddings. Create a PostgreSQL table with a Write the ingestion pipeline that pulls invoice and ERP data from SAP or Dynamics, extracts the relevant fields, generates the natural-language description, computes the embedding, and inserts the row into the pgvector table. For SAP, use the OData API or a BAPI call to read the invoice header and line items. For Dynamics, use the Web API. The pipeline runs on a schedule: nightly for new invoices, and on-demand when a finance team member triggers a re-index. The embedding model is called for each new chunk. If you are using OpenAI\u2019s text-embedding-3-small, the cost is approximately $0.02 per 1,000 tokens, which is negligible for a 51-200 person firm. If you are using a local model on your own hardware, the cost is zero but the latency is higher. Log every ingestion run with a timestamp and a row count so you can audit the data flow later.<\/p>\n Build the query interface that a finance team member will use. The user types a question in natural language, for example: \u2018What is the status of invoice INV-2024-0847 and when is it due?\u2019 The system embeds the question, runs a cosine-similarity search against the pgvector index, retrieves the top 5-8 chunks, and passes them as context to the LLM. The LLM is prompted to answer in the language of the query (German, English, or another supported language) and to cite the specific invoice number and GL account it is referencing. The response is displayed in a lightweight dashboard or integrated into your existing helpdesk. If the question involves a payment action, a vendor master data change, or a contract modification, the system flags it for human approval. The approver sees the assistant\u2019s draft, the retrieved context, and a one-click approve or reject button. This step takes one to two weeks and is where the human-in-the-loop design becomes operational.<\/p>\n Run the pilot for four to six weeks on the single workflow you scoped in step 1. Measure the cycle time and error rate for every invoice-related ticket that passes through the assistant. Compare the numbers against your baseline from the prerequisites. The target is a 30-45% reduction in cycle time and a measurable drop in error rate. Track the escalation rate: how often does the assistant flag a query for human approval, and how often does the approver reject the assistant\u2019s draft? If the escalation rate is above 20%, your retrieval thresholds are too loose or your natural-language descriptions in the pgvector table are too vague. Tune the top-k parameter and the similarity threshold. If the error rate does not drop, check whether the LLM is hallucinating invoice numbers or GL accounts that do not exist in the retrieved context. The pilot output is a one-page report with the before\/after numbers, the escalation rate, and the list of queries that the assistant could not answer. This report is what you use to justify the rollout to additional departments.<\/p>\n","protected":false},"excerpt":{"rendered":" A three-month, step-by-step guide to deploying a pgvector-based RAG assistant over SAP or Dynamics ERP for invoice processing in an Austrian fintech, with measured cost-per-ticket baselines.<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rank_math_title":"Deploying a pgvector RAG Assistant for Invoice Processing in an Austrian Fintech","rank_math_description":"A three-month, step-by-step guide to deploying a pgvector-based RAG assistant over SAP or Dynamics ERP for invoice processing in an Austrian fintech, with measured cost-per-ticket baselines.","rank_math_focus_keyword":"multilingual support coverage invoice processing","_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","_yoast_wpseo_focuskw":"","pll_lang":"en","geo_jsonld":"{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@id\":\"https:\/\/blog.forfis.com\/blog\/pgvector-rag-invoice-assistant-austrian-fintech-sap-dynamics\/#article\",\"@type\":\"Article\",\"author\":{\"@id\":\"https:\/\/blog.forfis.com#org\"},\"dateModified\":\"2026-10-06T00:00:17.973036724+00:00\",\"datePublished\":\"2026-10-06T00:00:17.973036724+00:00\",\"description\":\"A three-month, step-by-step guide to deploying a pgvector-based RAG assistant over SAP or Dynamics ERP for invoice processing in an Austrian fintech, with measured cost-per-ticket baselines.\",\"headline\":\"Deploying a pgvector RAG Assistant for Invoice Processing in an Austrian Fintech\",\"inLanguage\":\"en\",\"keywords\":[\"Scaling Across Departments\",\"pgvector Embeddings Search\",\"Retrieval-Augmented Knowledge Assistant\",\"Finance and Accounting\",\"51-200\",\"None\",\"Managed AI Operations\",\"Fintech and Payments\",\"SAP or Microsoft Dynamics ERP\",\"English\",\"Multilingual Support Coverage\",\"Austria\",\"3 months\",\"Invoice Processing\"],\"mainEntityOfPage\":\"https:\/\/blog.forfis.com\/blog\/pgvector-rag-invoice-assistant-austrian-fintech-sap-dynamics\/\",\"publisher\":{\"@id\":\"https:\/\/blog.forfis.com#org\"}},{\"@id\":\"https:\/\/blog.forfis.com\/blog\/pgvector-rag-invoice-assistant-austrian-fintech-sap-dynamics\/#faq\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"A retrieval-augmented knowledge assistant in this context is an LLM-based system that answers finance and accounting queries by first retrieving relevant chunks from your SAP or Dynamics ERP data, invoice PDFs, and internal policy documents via pgvector, then generating a grounded response. It does not replace the ERP; it sits in front of it, reducing the number of tickets that require a human analyst to open the system and search manually. For a 51-200 person fintech in Austria, this typically means the assistant handles 60-75% of routine invoice-status and reconciliation questions, escalating the rest to a human with full context attached.\"},\"name\":\"What is a retrieval-augmented knowledge assistant for invoice processing?\"},{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"pgvector is a PostgreSQL extension that stores and queries high-dimensional vector embeddings. In this architecture, you embed invoice line items, ERP transaction records, and policy documents into 1536-dimensional vectors (for OpenAI's text-embedding-3-small) or 768-dimensional vectors (for a local model like BGE-M3). At query time, the assistant embeds the user's question, runs a cosine-similarity search against the pgvector index, retrieves the top 5-8 chunks, and passes them as context to the LLM. This keeps answers grounded in your actual data rather than the model's training corpus, which is critical when a finance team asks about a specific vendor's payment terms or a particular GL account's reconciliation status.\"},\"name\":\"How does pgvector embeddings search work in this stack?\"},{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"For a 51-200 person fintech in Austria, the realistic timeline is three months. Month 1 covers the process audit, data mapping from SAP or Dynamics, and the pgvector schema design. Month 2 is the fixed-scope pilot: one invoice-processing workflow, one department, with a measured before\/after baseline on cycle time and error rate. Month 3 is rollout to additional departments and the handoff to managed operations. This assumes your ERP APIs are accessible and your invoice data is in a structured or semi-structured format. If you are running on-premises SAP with no API layer, add two to three weeks for the integration middleware.\"},\"name\":\"How long does a three-month deployment take for a mid-size fintech?\"},{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"The cost reduction comes from three sources. First, the assistant handles routine queries (invoice status, payment due dates, vendor master data lookups) that previously consumed 8-15 minutes of a finance analyst's time per ticket. Second, the RAG layer reduces hallucination risk, meaning fewer escalations and rework. Third, the managed operations model means Forfis monitors the system, re-triggers embeddings when new invoice data lands, and tunes the retrieval thresholds, so your internal team does not need a dedicated ML engineer. For a 51-200 person firm, the typical result is a 30-45% reduction in cost per support ticket within the first quarter, with the pilot baseline providing the before\/after numbers to justify the rollout.\"},\"name\":\"How does this reduce cost per support ticket?\"},{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes, and it is a core requirement for the Austrian market. The assistant's LLM layer is configured to handle queries in German, English, and any additional languages your finance team or vendors use. The pgvector index stores multilingual embeddings, so a German-language query about a vendor's payment terms retrieves the same relevant chunks as an English-language query about the same vendor. The LLM is prompted to respond in the language of the query. For a fintech operating across DACH or serving international payment partners, this eliminates the need for separate language-specific knowledge bases and reduces the ticket backlog caused by language barriers.\"},\"name\":\"Can the assistant handle multilingual support for an Austrian fintech?\"},{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"The human-in-the-loop design means the assistant drafts a response or classification, but any action that touches money, a contract, or a payment instruction requires a human approval. In practice, this means the assistant can answer a question like 'What is the status of invoice INV-2024-0847?' without approval, but it cannot initiate a payment, modify a vendor's bank details, or approve a credit note. The approval workflow is built into the integration layer: the assistant flags the action, a human reviews it in the ERP or a lightweight approval dashboard, and the system logs the decision. This keeps the system within the boundaries of your existing internal controls without requiring a compliance overhaul.\"},\"name\":\"How does the human-in-the-loop model work for finance operations?\"},{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"The architecture is model-agnostic. For the LLM layer, Forfis uses OpenAI's GPT-4o or Anthropic's Claude 3.5 Sonnet where response quality and multilingual accuracy matter. For the embedding layer, pgvector works with any embedding model: OpenAI's text-embedding-3-small for cloud-based deployments, or an open-weight model like BGE-M3 running on your own hardware if your data residency requirements prevent sending invoice content to a third-party API. The RAG pipeline, the pgvector schema, and the ERP integration layer are identical regardless of which model you choose. This means you can start with a cloud model for speed and migrate to an on-premises model later without re-architecting the system.\"},\"name\":\"Which LLM models and embedding models are used in this stack?\"},{\"@type\":\"Question\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"The managed operations model means Forfis handles the ongoing maintenance after the three-month deployment. This includes monitoring the pgvector index for drift as new invoice data is ingested, re-running embeddings when your ERP schema changes, tuning the retrieval top-k and similarity thresholds based on ticket feedback, and updating the LLM prompts as your finance team's question patterns evolve. Your internal team retains ownership of the business logic and approval workflows, but the ML infrastructure, model updates, and performance monitoring are Forfis's responsibility. 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Each row represents a chunk of invoice data: the invoice number, vendor name, GL account, amount, due date, and a short natural-language description of the transaction. For example, a row might look like: invoice_id: INV-2024-0847, vendor: 'Muster GmbH', gl_account: '4000', amount: 1250.00, due_date: '2024-09-15', description: 'Monthly SaaS subscription payment'<\/code>. The description field is critical: it is what the LLM will use to ground its answer. Write it in plain language, not in ERP field codes. This step takes two to three days and is the foundation of the entire system.<\/p>\nStep 3: Ingest ERP Data and Generate Embeddings<\/h2>\n
Step 4: Build the RAG Query Layer with Human-in-the-Loop Approval<\/h2>\n
Step 5: Run the Pilot and Measure the Before\/After Baseline<\/h2>\n