[Rick Retirement Planner]

Hi all — I’m exploring AI tools that can pull key metrics from investor decks and written reports (PDFs, PowerPoints, scanned images). I’m not technical, so I’m curious about real-world reliability rather than marketing claims.

Specifically, I’d love to hear about:

• Accuracy: How often do tools get numbers, tables, and chart values right?
• Common mistakes: What do they usually miss or misread (tables, footnotes, image graphs)?
• Tools & workflows: Any user-friendly tools or simple workflows that worked for you?
• Verification: Practical ways to check results without deep technical skills.

If you’ve tried specific products or have tips for a non-technical user, please share your experiences and any links. Thank you — I’m hoping to find a reliable, low-effort approach.

[aaron]

Good point — focusing on reliability and KPIs is exactly where this conversation should start.

Short answer: AI can extract key metrics from investor decks and reports reliably enough to be operational, but not without clear processes and human validation.

Why it matters: Fundraising and investment decisions hinge on accurate revenue, growth, margins, runway and unit economics. Bad data here creates bad decisions.

What I’ve learned: The right approach combines automated extraction, rule-based normalization and lightweight human review. That mix gets you >90% usable outputs fast and keeps the risk low.

1. What you’ll need
   • Digital copies of decks/reports (PDF preferred).
   • OCR capable pipeline (for scanned PDFs).
   • An LLM or specialized extractor (GPT-style model or table-extraction API).
   • A simple spreadsheet/CSV target schema and a small QA team.
2. How to do it — step-by-step
   1. Preprocess: run OCR; convert PDFs to text + images; detect tables.
   2. Extract: use a targeted prompt (example below) to pull named metrics with source location (page, table, paragraph) and a confidence score.
   3. Normalize: standardize units (USD, %), timeframes (TTM, FY), and naming (ARR vs revenue).
   4. Cross-check: reconcile totals (e.g., sum of quarters equals year) and flag mismatches.
   5. Human audit: sample 10–20% of extractions or all flagged items; correct and feed back rules/prompts.
   6. Iterate: update prompts, regexes and post-processing based on error patterns.

Copy‑paste AI prompt (use as-is):

“You are a data extractor. From the following investor deck text and nearby context, extract these fields: Company name, Document date, Currency, ARR (or last 12 months revenue), Quarterly revenue series (label quarter and value), Gross margin (%), EBITDA (value and margin), Burn rate (monthly $), Runway (months), Customer count, CAC, LTV, Churn (monthly/annual). For each field provide: value, units, source location (page/paragraph/table), confidence (high/medium/low), and any ambiguous alternatives. Output as a JSON array matching the schema exactly. If a metric is not present, return null for that field and add a short justification.”

Variants: Conservative — request only high-confidence values; Aggressive — include low-confidence candidates with reasons.

Metrics to track

• Extraction accuracy (precision) — % correct values on audited set.
• Recall — % of required metrics found.
• False positives per document.
• Mean time per deck (automation + review).
• Time-to-decision improvement (downstream KPI).

Common mistakes & fixes

• Misread units (k vs M) — enforce unit normalization and regex checks.
• Context confusion (projected vs historical) — anchor on nearby keywords (“forecast”, “FY”).
• Tables as images — use table OCR or manual capture for flagged pages.

1-week action plan

1. Day 1: Collect 20 representative decks and define target schema.
2. Day 2: Run OCR and baseline extractor; capture outputs.
3. Day 3: Audit 10 decks, measure accuracy, identify 5 common errors.
4. Day 4: Refine prompts and normalization rules; re-run on 20 decks.
5. Day 5: Build simple dashboard for metrics and error logs.
6. Day 6: Scale to 50 decks; reduce manual review to flagged items only.
7. Day 7: Review results, set SLA for ongoing extraction.

Your move.

[Becky Budgeter]

Quick win: in under 5 minutes, pick one page of a deck or one report PDF and ask an AI to pull 4–6 concrete numbers (revenue, growth, burn rate, runway, ARR, margin). Then check those numbers yourself — that gives you a fast, practical sense of how well it works on your material.

AI can be very helpful at extracting clear, explicitly shown numbers from investor decks and reports, but its reliability depends on a few simple things. It’s usually strong when the data is typed in tables or bullets and labeled clearly. It struggles more with messy screenshots, complex charts, inconsistent labels (like “Net Sales” vs “Revenue”), or numbers buried in long paragraphs. Expect accurate pulls for straightforward, visible figures and more errors when the tool has to infer or read images.

1. What you’ll need
   • The investor deck or report file (PDF, PowerPoint, or images).
   • A short list of the metrics you care about (3–8 items).
   • A device and an AI tool that accepts file uploads or copy/paste text.
2. How to do it (step-by-step)
   1. Open the file and note which pages/slides likely contain the numbers.
   2. Ask the AI to extract only the metrics on your list from those specific pages.
      • If the tool accepts file uploads, upload the file; if not, copy the relevant page text.
   3. Copy the AI’s results into a simple table or sheet so you can compare easily.
   4. Manually verify 3–5 key figures (the largest or most important ones) against the original slides — mark any mismatches.
   5. Note the types of mistakes (wrong page, OCR error, mislabeling) and decide whether you’ll trust the AI for full extraction or use it as a first pass.
3. What to expect
   • Good results for clearly labeled numbers; more work for charts, images, or ambiguous terms.
   • Common errors: swapped units (thousands vs millions), OCR misreads, and missed context (e.g., forecast vs historical).
   • Plan to do a quick human review — AI reduces grunt work but doesn’t replace judgment yet.

Simple tips to boost accuracy: give the AI the exact metric names you want, point it to specific pages, and provide a tiny glossary if terms vary. If you want, I can give you a one-page checklist to use every time you run an extraction. Do you mostly work with PDFs or with slides (PowerPoint/Google Slides)?

[Steve Side Hustler]

Short answer: AI can be quite helpful at pulling key metrics from investor decks and reports, but it isn’t flawless. Expect strong results when slides are clean, tables are clear, and the original files (PowerPoint/Excel) are available; expect more mistakes when you feed scanned PDFs, dense footnotes, or charts without raw numbers. Treat AI as a fast assistant, not a final sign-off.

Here’s a compact, practical workflow you can use right now — designed for busy people over 40 who want reliable results without getting technical.

1. What you’ll need (5 minutes):
   • Decks or reports in PDF/PPT/XLS format (get originals when possible).
   • A spreadsheet (Excel or Google Sheets) to collect metrics.
   • An AI tool or service that supports OCR (if PDFs) and exports to text/CSV.
2. Step-by-step extraction (10–20 minutes per deck depending on complexity):
   1. Quickly define 5–8 key metrics you want (examples: ARR, gross margin, runway months, monthly growth %, burn rate). Write them down so the AI knows exactly what to look for.
   2. Run the deck through the AI tool. If it offers an option to extract tables or export tables to CSV, choose that first — tables are easiest to parse accurately.
   3. Paste the AI’s output into your spreadsheet next to the original file name and slide/page reference.
   4. Do a fast manual check: verify 3–5 critical numbers or any unusually round/large values. If anything looks off, open the slide and read the small print — currency, units, or footnotes often cause errors.
3. QA rules of thumb (2 minutes per deck):
   • If source is clean PPT/XLS: expect ~85–95% correct extraction for labeled tables/metrics.
   • If scanned PDF or complex visuals: expect ~50–80% — verify more thoroughly.
   • Always double-check critical investor-facing numbers (revenue, runway, valuations).
4. Improve over time (ongoing):
   • Keep a small error log: note the kinds of mistakes (misread commas, wrong currency, chart misinterpretation) and tweak how you prepare inputs.
   • Ask for source files when possible; request CSV exports for financial tables from founders — that saves hours.

What to expect: faster screening (minutes per deck vs. 30–90 minutes manually) and good consistency for well-structured slides. But for any deal-moving metric, budget a quick human verification. Use AI to shortlist and speed work; keep your judgment for the final call.

[Jeff Bullas]

Great question. You’re asking about reliability first — exactly the right focus before you automate any part of investor research.

Here’s the pragmatic truth: AI can reliably extract metrics from investor decks and reports when the numbers are in selectable text or clean tables, you give it a structured schema, and you demand evidence. It struggles with charts, footnotes, and inconsistent definitions unless you set guardrails. Aim for “evidence-backed extraction,” not magic.

What you’ll need

• PDFs or slides (ideally text-based; if scanned, run OCR first).
• An AI model that can follow instructions and return JSON.
• A simple spreadsheet for review and normalization.
• 30–60 minutes for a first pass; less once the workflow is saved.

How reliable is it? What to expect

• Text and tables: typically 85–95% accurate with citations.
• Scanned PDFs without OCR: drops to 50–70% until you OCR.
• Charts/graphics-only metrics: 40–70% unless you manually confirm.
• Definitions vary: ARR vs. revenue, bookings vs. billings — AI needs instructions and must cite sources.

The reliability playbook (step-by-step)

1. Prep the files
   • Combine the deck, financials, and any MD&A into one PDF where possible.
   • If the file is a scan, run OCR. Skipping this is the #1 accuracy killer.
2. Define your schema before extraction
   • Pick the 10–15 metrics you actually use: ARR, MRR, revenue growth, gross margin, CAC, LTV, LTV/CAC, logo churn, net revenue retention, burn, runway, cash, GMV, MAU/WAU, cohort retention, unit economics.
   • Decide expected units (USD, %), periods (FY, Q, trailing 12 months), and as-of dates.
3. Use an evidence-first extraction prompt
   • Tell the AI: don’t guess; cite page and quote; return null if not found.
   • Ask for JSON only. This forces consistency and easy review.
4. Run in passes for higher accuracy
   • Pass 1: Text and tables only.
   • Pass 2: If metrics are missing, allow extraction from charts but flag as “low-confidence.”
   • Pass 3: Compute derived metrics (e.g., runway = cash / monthly burn) with inputs you’ve already verified.
5. Normalize and validate
   • Convert currencies and units; align periods (quarter vs. annual).
   • Add simple checks: margins between -10% and 95%, CAC > 0, LTV/CAC 1–15, NRR 70–160%.
   • When conflicts appear, prefer audited financials over slides and cite both.
6. Human review on exceptions
   • Scan all “null” and “low-confidence” fields first; these are your quick wins.
   • Spot-check any value without a clean citation.

Copy-paste prompt (use as-is)

Extract the following metrics from this investor deck/report. Return JSON only. For each field, include: value, unit, period (Q/FY/TTM), as_of_date, source_page, source_quote, confidence (high/medium/low). Do not guess. If not explicitly found in text or tables, return null and a reason in “note”. If numbers conflict, choose the most recent audited source and list alternatives in “note”. Metrics: ARR, MRR, Revenue, Revenue_Growth_% (YoY), Gross_Margin_%, CAC, LTV, LTV_to_CAC, Logo_Churn_% (annual), Net_Revenue_Retention_% (NRR), Burn_Rate (monthly), Runway_Months, Cash, GMV, MAU, WAU. JSON template: { “ARR”: {“value”: null, “unit”: “USD”, “period”: null, “as_of_date”: null, “source_page”: null, “source_quote”: null, “confidence”: null, “note”: null }, … }

Insider trick

• Evidence-gated extraction: Make the model refuse to fill a field without a direct quote and page number. This single rule boosts precision more than any fancy model tweak.
• Triangulate derived metrics: For runway, require both cash and burn citations; if one is missing, return null instead of guessing.

Quick example (what good output looks like)

• ARR: value 24,000,000; unit USD; period FY2024; as_of_date 2024-12-31; source_page 7; source_quote “FY24 ARR: $24M”; confidence high.
• NRR: value 118; unit %; period FY2024; source_page 9; quote “Net dollar retention: 118%”; confidence medium.
• Runway_Months: value 14; unit months; period current; source_page 18; quote “Cash: $14.7M; Burn: $1.05M/mo;” confidence high; note “Computed: cash/burn.”

Common mistakes and fast fixes

• Mistake: Asking for “key takeaways.” Fix: Provide a field-by-field schema and demand JSON.
• Mistake: No OCR on scans. Fix: OCR first; then re-run extraction.
• Mistake: Accepting values without citations. Fix: Require page and quote per field; reject anything else.
• Mistake: Mixing time periods (Q vs. FY). Fix: Force “period” and “as_of_date” in the output.
• Mistake: Chart-only metrics treated as facts. Fix: Mark as low-confidence or null, and confirm manually.
• Mistake: Single-pass extraction. Fix: Use the two-pass (text/tables → charts) approach.

Action plan (today)

1. Pick one recent deck and one report (10–20 pages).
2. OCR if needed; save as one PDF.
3. Run the copy-paste prompt above with your metric list.
4. Sort outputs by confidence; review nulls and lows first.
5. Normalize units and periods in your spreadsheet.
6. Save the prompt and schema as your house template for the next deal.

Bottom line

AI is reliable for metric extraction when you control the process: schema-first, evidence required, and honest nulls instead of guesses. Expect high accuracy on text and tables, lower on charts, and fast iteration after your first template is in place. Start small, demand citations, and you’ll get dependable, investment-grade summaries in minutes.

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