Quick pipeline you can run in a few hours
Start by quarantining incoming files. Route email attachments, uploads and shared folders into a controlled inbox or a quarantine bucket (eg. a restricted S3/Blob container or a locked SharePoint folder). Reject or isolate any unexpected file types and record who submitted the file and which CRM record it belongs to (use a CRM ID in the filename or metadata).
Next, extract and normalise text and data. Use Tesseract or a cloud OCR (Google/Vision, AWS Textract) for scans and images, and simple parsers for spreadsheets and emails. Normalise encoding, dates and common field names so later steps work reliably.
Finally in this stage, auto-classify file type and likely sensitivity with a lightweight classifier (a few regexes plus a small ML model or low-code rules in Make/Zapier). Tag files as invoice/contract/photo/email and as low/medium/high sensitivity so downstream processing can follow different rules.
Make the files safe and useful for AI
Apply lightweight automated PII redaction and quality checks before any embeddings or AI calls. Start with deterministic rules (UK phone, NI, email, sort codes, card patterns) and remove or mask obvious matches. Keep a confidence score: if the redactor is uncertain, flag the file for human review rather than removing it automatically.
Keep originals encrypted and access‑restricted for compliance. Store the extracted text separately from the original binary and keep an immutable audit log (who accessed, who changed tags, automated decisions) so you can answer privacy queries and audits.
When content is clean enough, chunk and index it for retrieval (RAG). Chunk by paragraph, table or logical section; keep chunks reasonably small so context windows stay focused. Use embeddings from a hosted provider or an on‑prem/local SentenceTransformers model and a simple vector store (Pinecone, Weaviate, Milvus or even a small SQLite-backed approach for very small datasets).
Link, review, cost and retention guidance
Always link processed outputs back to the CRM record: include the CRM ID in metadata and add a short activity note to the contact or case in HubSpot, Salesforce, Marketo or Pardot so teams can find the source. Use webhooks or scheduled syncs to avoid two‑way conflicts—treat the file pipeline as a one‑way feed into the CRM.
Build a small human‑review queue for edge cases. Prefer an existing ticket board or a CRM task list rather than a new system: reviewers see the file, the redaction/confidence metadata, and can approve, correct or securely delete. Keep review steps short and visible so the team trusts the pipeline.
Costs and retention — practical rules of thumb: expect OCR and embedding calls to be the main variable costs. Batch OCR and embeddings overnight where possible, and pilot with 1–2k documents to estimate monthly spend. For sensitive items keep originals no longer than legally required (common small‑team defaults: 30–90 days for transient attachments, 6–24 months for contracts/financial docs) and prune vectors and extracted text on the same schedule. If cost is a concern, use open‑source OCR and local embedding models on a small VM and move to hosted services once you have steady volumes.
If you want a hand scoping a pragmatic, low‑cost build and a short pilot to test costs and controls, Optira can help set it up and document the review, retention and audit steps.