13 ways to take repetitive document & knowledge processing (ocr, idp, rag) work off your team, with real examples and the ones worth piloting first. Every figure is an attributed industry source, not our own claim.
Automatically sorts incoming documents and emails by type (invoice, claim, complaint, application) and sends each to the right team, queue or system.
Problem it removes
A shared inbox or physical mailroom where staff manually open, categorise and forward everything. Misrouting causes delays, and volume spikes create backlogs.
How it is built
Classification model (often LLM-based) that reads structure, language and metadata to decide document type, plus OCR for scans. Sits in front of downstream automations as the routing layer. Rules and confidence thresholds control auto-route vs. review.
Example
Typical scenario: a services firm auto-classifies a shared inbox so invoices, complaints and applications each reach the right queue. In insurance, Aviva reported case-routing accuracy improved 30% and complaints down 65% across a wider AI claims programme.
Scales
A small firm classifies one inbox; enterprises run a full digital mailroom across channels feeding many downstream systems. Cheap to start, high leverage as a front door to other IDP pipelines.
Typical industry figure
Indicative: fewer misroutes and faster first-touch. Often the enabling step that unlocks larger savings downstream. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Document summarisation & meeting notes
SME
low build Good first pilot
Condenses long documents, reports, email threads or meeting transcripts into concise summaries with key points, decisions and action items.
Problem it removes
People drown in long documents and back-to-back meetings, then lose the decisions and follow-ups. Reading and note-taking eat hours that could go to the actual work.
How it is built
LLM summarisation, often with RAG for grounding on long or multi-document inputs, plus a transcription front end (Otter, Zoom, Read.ai) for meetings. Structured output: summary, decisions, action items, owners.
Example
Typical scenario: a team auto-summarises meetings into shareable notes with action items owned by name. Vendors cite users saving several hours weekly with AI note-taking.
Scales
Works from a single user up to org-wide rollout. The lightest, safest starting automation; scales by wiring summaries into the tools people already use.
Typical industry figure
Indicative (vendor figures): ~4 hours per week saved per heavy meeting-goer; ~20–25% gains in meeting productivity and follow-through. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Forms & structured-data capture (application/order/PO)
SME
low build Good first pilot
Reads structured and semi-structured forms (applications, order forms, purchase orders, delivery notes) and turns them into clean records in the target system.
Problem it removes
Staff hand-key data from paper and PDF forms into systems all day. It is dull, slow and error-prone, and volume spikes create backlogs.
How it is built
Template-aware IDP: OCR plus field extraction tuned to known form layouts, with validation rules and confidence scoring. Straightforward integration into the CRM/ERP/order system. Simpler than free-form document extraction because layouts are predictable.
Example
Typical scenario: a distributor auto-captures purchase orders and delivery notes into its ERP, matching them to open orders and flagging discrepancies for review.
Scales
A small firm automates one high-volume form; enterprises cover many form types across departments. Scales linearly and predictably because forms are structured.
Typical industry figure
Indicative: broad IDP evidence of 30–50% less manual processing time, with per-document cost falling sharply at high automation rates (McKinsey, via Auxis). Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Invoice & accounts-payable capture (IDP)
SME
medium build Good first pilot
Reads incoming supplier invoices (PDF, scan, email attachment) and pulls out supplier, invoice number, line items, VAT and totals, then matches them to the purchase order and pushes clean data into the accounting or ERP system.
Problem it removes
Manual keying of invoices is slow, error-prone and expensive. Finance teams re-type the same fields from hundreds of different invoice layouts, chase mismatches, and pay late because approvals stall.
How it is built
Intelligent Document Processing (IDP): OCR to read the page, an LLM or trained extraction model to identify fields across varied layouts, confidence scoring to auto-pass clean invoices and flag the rest for a human. Wired into the ERP (Xero, SAP, NetSuite) via API or an RPA bridge. Two- or three-way PO matching on top.
Example
Datamatics case: a large European manufacturer automated processing of ~140,000 invoices a year with IDP plus RPA, reporting faster processing and reduced manual effort.
Scales
Small firms use an off-the-shelf capture tool with light tuning; enterprises train custom models on high supplier volume and add straight-through processing targets. Scales cleanly with invoice volume because per-document cost falls as automation rate rises.
Typical industry figure
Indicative: manual AP cost of roughly GBP 8–15 per invoice falls to roughly GBP 1–3 at high straight-through rates; 30–50% less manual processing time (McKinsey, cited via Auxis). First-year ROI cited in the 30–200% range. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Internal knowledge assistant (RAG over company docs)
SME
medium build Good first pilot
A chat assistant that answers staff questions from the company's own documents (policies, SOPs, wikis, past tickets, contracts) and cites the source it used, instead of guessing. At scale this becomes permission-aware enterprise search that spans every connected system (Drive, SharePoint, Slack, Confluence, CRM) and returns one synthesised, cited answer rather than a list of links.
Problem it removes
Employees waste hours hunting across SharePoint, Confluence, Slack and Drive for answers that already exist. Knowledge lives in people's heads and leaves when they do. New joiners take months to become useful.
How it is built
RAG (retrieval-augmented generation): documents are chunked and indexed in a vector database; a user question retrieves the most relevant passages, which an LLM turns into a grounded, cited answer. Access controls mirror existing permissions so people only see what they should. Built on the company's existing content sources; larger deployments add connectors across many systems and, often, a knowledge graph. Platforms in the space: Glean, Microsoft Copilot, Coveo.
Example
Typical scenario: a services firm indexes its policy library and project archive so consultants get cited answers in seconds, and later connects Drive, Slack, Confluence and SharePoint into a single search across everything.
Scales
A small team can start with one content source and a few dozen documents; enterprises index millions of documents across many systems with permission-aware retrieval. Value grows with document volume, number of sources connected, and staff headcount. Enterprise search is the largest RAG segment in 2025 (Data Nucleus).
Typical industry figure
Indicative: employees spend up to ~30% less time searching for information (Forrester, cited via GoSearch); McKinsey estimates ~1.8 hours per day lost to search today. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Customer-support answer bot (RAG deflection)
SME
medium build Good first pilot
A customer-facing assistant that resolves routine questions from your help centre and knowledge base, handing the hard ones to a human with full context.
Problem it removes
Support teams drown in repetitive questions (password resets, billing, how-to) that already have documented answers. Response times slip, costs rise, and agents burn out on low-value tickets.
How it is built
RAG knowledge assistant over help-centre articles and past resolved tickets, plus integrations (custom actions/webhooks) to take real actions like order lookups. Deployed in the existing support tool (Intercom, Zendesk). Confidence thresholds decide auto-answer vs. human handoff.
Example
BQE Software reported an 86% AI resolution rate across 180,000+ questions, with 64% of help-centre interactions handled by AI. GEMA combined a 24/7 assistant with Confluence content, reporting work avoided equal to roughly 3 FTEs (vendor case study).
Scales
Small firms launch same-day on an existing help centre; enterprises add deep system integrations and multilingual coverage. Deflection rate rises as the knowledge base is cleaned and expanded.
Typical industry figure
Indicative: RAG support deployments typically deflect ~40–50% of routine tickets (2026 median ~41%, top quartile ~59%); simple categories like password resets exceed 70% (Fini Labs). Watch deflected-but-unresolved tickets becoming repeat tickets. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Contract review & clause extraction
SME
medium build Good first pilot
Reads contracts and pulls out key terms (parties, dates, renewal, liability, payment, non-standard clauses), flags risky or missing language, and compares against your playbook.
Problem it removes
Manual contract review is slow and inconsistent. Lawyers spend hours on routine NDAs and MSAs; obligations get missed; outside-counsel spend balloons; renewals slip past unnoticed.
How it is built
Document extraction plus LLM analysis, often with RAG over a legal-clause knowledge base. Trained to recognise clause types and deviations from standard positions. Outputs a structured summary and risk flags for a lawyer to confirm. Named tools in the space: Kira, Luminance, Harvey.
Example
Typical scenario: a mid-market legal team automates NDA triage against its playbook and builds a clause library, cutting time on routine agreements. Vendors report M&A due-diligence review dropping from weeks to hours.
Scales
A small business automates NDA triage; mid-market builds a clause library and obligation tracking; enterprises run it across thousands of contracts and M&A due diligence. Scales with contract volume and template standardisation.
Typical industry figure
Indicative (vendor figures): ~80% reduction in review time with high accuracy; example ~GBP 60K direct saving on 200 NDAs per year (Virtasant); 14–60% reduction in external legal spend. Human sign-off remains essential. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Compliance & policy Q&A assistant
SME
medium build Good first pilot
Lets staff ask questions about internal policies, regulations and standard operating procedures and get grounded, cited answers, so they follow the current rule rather than an outdated memory of it.
Problem it removes
Policies and regulations change and live in dense PDFs nobody reads. People act on stale information, or repeatedly interrupt compliance and HR teams with the same questions.
How it is built
RAG over the policy and regulatory document set, with strict citation so every answer links to the source clause and version. Access controls and a clear disclaimer that it assists rather than replaces official sign-off.
Example
Typical scenario: an operations team asks the assistant about expense, data-protection and safety policies and gets cited answers, cutting queries to compliance. Compliance and legal are cited as strong RAG-ROI domains because wrong answers are costly and sources are required (Data Nucleus).
Scales
A small firm covers its HR and health-and-safety policies; regulated enterprises cover large regulatory libraries with version control. Value rises with policy volume and audit pressure.
Typical industry figure
Indicative: fewer repeat queries to specialist teams and faster, more consistent policy adherence; sits within the ~30% search-time reduction range cited for internal knowledge assistants. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Document translation & multilingual processing
SME
medium build
Extracts and translates content from documents in many languages and, where useful, structures it, so multilingual paperwork can be processed in one workflow.
Problem it removes
Cross-border operations receive documents in languages local staff cannot read. Manual translation and re-keying is slow, expensive, and a bottleneck for global teams.
How it is built
OCR plus machine translation, combined with extraction/IDP so the same pipeline captures and translates fields. LLMs handle mixed-language and non-standard documents that older OCR could not. Confidence flags route uncertain items to a human.
Example
Typical scenario: a logistics or trading firm ingests multilingual invoices and shipping documents, translating and extracting fields in one pass. Healthcare and insurance vendors note modern AI OCR now handles multilingual and handwritten content older systems could not.
Scales
Helps any firm with cross-border document flow; a natural fit for a multilingual studio. Scales across languages and document types with the same pipeline.
Typical industry figure
Indicative: removes a manual translation bottleneck and lets one workflow cover many markets; inherits the 30–50% processing-time reductions seen across IDP. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Insurance / claims document automation
ME
high build
Ingests claims paperwork, forms and correspondence, extracts the relevant fields, classifies the document type, and feeds validated data straight into the core claims system.
Problem it removes
Claims arrive as messy multi-page bundles in many formats. Manual intake and keying create long turnaround times, backlogs, and inconsistent routing, frustrating customers who are already stressed.
How it is built
OCR plus NLP/IDP pipeline: digitise, extract and classify unstructured data, confidence-score, then route validated records into the claims platform. Exceptions go to a human. Often paired with a digital mailroom front end.
Example
Datamatics case: a global risk-management provider cut claims turnaround time by 76% using OCR and AI data capture. A Nordic insurer (EY case) used OCR and NLP to classify unstructured data into its core claims system.
Scales
Best fit for medium and large insurers with high document volume and defined claims workflows. Scales across product lines once the extraction and routing pipeline is proven on one line.
Typical industry figure
Indicative: turnaround-time reductions of ~50–76% in cited cases; broader IDP evidence shows 30–50% less manual processing time. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
KYC / onboarding document verification
ME
high build
Captures identity documents and onboarding paperwork, extracts and validates the data, checks authenticity, and routes clean cases to approval while flagging anything suspicious.
Problem it removes
Manual KYC is slow (multi-day onboarding), costly, and inconsistent. Delays lose customers at signup; errors create compliance and fraud exposure.
How it is built
OCR extraction plus document-authenticity checks, optional biometric/liveness matching, sanctions and database screening, AI risk scoring, and automated approve-or-escalate routing. Orchestrated into the onboarding flow.
Example
Typical scenario: a fintech automates ID capture, extraction and screening to shrink onboarding from days to minutes, with suspicious cases escalated for manual review.
Scales
Fits regulated mid-market and enterprise firms (banks, fintech, gaming). Scales across products and jurisdictions once the verification and screening pipeline is in place.
Typical industry figure
Indicative (vendor figures): up to ~70% cost reduction in KYC operations; verification time cut ~78% and onboarding costs down up to ~48%; some bank cases cite full ROI within 12–18 months. Compliance sign-off still required. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Healthcare clinical document extraction
ME
high build
Reads clinical paperwork (referrals, prior-authorisation forms, faxes, medical records) including handwriting, and turns it into structured data for review or auto-adjudication.
Problem it removes
Health plans and providers receive high volumes of unstructured, payer-specific forms and faxes. Manual review is slow, delays care decisions, and ties up clinical staff on data entry.
How it is built
OCR plus computer vision (including handwriting and mark detection) feeding clinical NLP that structures the data, with confidence scoring and HIPAA-compliant handling. Pre-populates or auto-adjudicates cases, escalating exceptions.
Example
HealthEdge built a modular OCR pipeline for prior authorisation, reporting high extraction accuracy (~99% vendor-reported vs. ~85% human), 20,000+ documents processed, and sub-one-minute reviews. Naviant cites ~30% faster prior-auth processing.
Scales
Fits payers, large providers and health-tech. Scales across form types and payer variants once the pipeline is validated; strict compliance and accuracy bars raise the build effort.
Typical industry figure
Indicative (vendor figures): one case cites ~73% reduction in processing time per document and ~70% productivity gain; throughput times halved by some plans. Treat as indicative.
Indicative third-party figures, not TelarLabs results.
Financial statement spreading & extraction
ME
high build
Converts financial statements and bank statements (PDFs, scans, spreadsheets) into analysis-ready figures mapped to the lender's template, with totals and cross-checks validated automatically.
Problem it removes
Credit analysts manually re-key financials from inconsistent layouts into spreading templates. It is slow, error-prone, and delays lending decisions.
How it is built
Advanced OCR that preserves rows and columns, machine learning to classify line items across layouts, mapping to the lender template, and validation of totals and cross-statement links with confidence flags. Learns from analyst corrections over time.
Example
Typical scenario: a commercial lender auto-spreads borrower financials into its credit template, cutting analyst keying and speeding credit decisions.
Scales
Fits banks, lenders and credit teams. Scales across borrowers and statement types; accuracy improves with volume as the model learns from corrections.
Typical industry figure
Indicative (vendor figures, e.g. Scry AI, Evalueserve Spreadsmart): ~70% faster extraction from financial statements, plus fewer keying errors and faster credit decisions. Treat as indicative.
