Transcription Vendor Evaluation Scorecard (Accuracy, Security, SLA + Support)

A good transcription vendor is the one that hits your accuracy needs, protects your data, meets your turnaround SLA, and responds fast when something goes wrong. The easiest way to choose well is to score every vendor the same way, then run a small pilot test with clear pass/fail thresholds.

This guide gives you a ready-to-use transcription vendor evaluation scorecard template, a pilot method, and acceptance criteria you can copy into an RFP or internal checklist.

Primary keyword: transcription vendor evaluation scorecard

Key takeaways

• Separate “overall accuracy” from high-risk details like names, numbers, dates, and technical terms.
• Require speaker attribution rules (including how vendors handle crosstalk and unknown speakers).
• Score security controls with evidence (policies, certifications, DPA), not promises.
• Define the SLA in writing (turnaround, uptime if applicable, and rework timelines).
• Run a pilot test with a fixed dataset and measurable thresholds before signing a longer contract.

What to measure (and what to ignore)

Most vendor comparisons fail because teams use vague criteria like “high quality” or “fast delivery.” You will get better results when you measure outcomes that matter to your workflow and risk level.

Focus on four buckets: accuracy, security, SLA/operations, and support. Ignore “nice-to-haves” until after you confirm the vendor can meet your minimum bar.

Accuracy: measure what can hurt you

Word-perfect transcripts matter in some cases, but many teams care more about specific error types that create real cost. Common high-impact errors include wrong names, missed numbers, and incorrect speaker labels.

• Names and proper nouns: people, companies, products, places.
• Numbers and units: prices, dates, dosages, model numbers, phone numbers.
• Speaker attribution: who said what, especially in interviews and meetings.
• Formatting compliance: timestamps, verbatim/clean read, paragraphing, tags like [inaudible].

Security: treat transcription like sensitive data processing

Audio often includes personal data, confidential business info, or regulated content. You should evaluate security as you would for any vendor that touches internal files.

If you process personal data in the EU/UK, you may also need contract terms that support GDPR obligations, such as a data processing agreement.

SLA and operations: define the work, not just the deadline

“24-hour turnaround” can mean many things. Your SLA should define what counts as on-time delivery, what happens when the audio is poor, and what rework timelines look like if the output fails your acceptance criteria.

Support: quality is a process, not a one-time event

Even great vendors will face edge cases: heavy accents, multiple speakers, noisy calls, or rush requests. Support responsiveness determines whether those edge cases become small fixes or large delays.

Transcription vendor evaluation scorecard template (copy/paste)

Use this template to score vendors consistently. The weights below are a starting point; adjust them to fit your risk level and volume.

How to use the scorecard

• Score each line item from 1 (poor) to 5 (excellent).
• Multiply by the weight.
• Require evidence for security and SLA claims (documents, screenshots, policy excerpts, contract language).

Scorecard table (weights and criteria)

• Section A — Accuracy (40%)
  • A1. Names & proper nouns accuracy (12%): Correct spelling and consistency across the transcript; uses provided glossary.
  • A2. Numbers/dates/units accuracy (12%): Correct numeric transcription; correct formatting rules (e.g., 1,000 vs 1000) as specified.
  • A3. Speaker attribution accuracy (10%): Correct speaker labels; handles interruptions; clear “Unknown Speaker” handling.
  • A4. Formatting & style adherence (6%): Timestamps, verbatim/clean read, paragraphing, tags like [inaudible], consistent casing.
• Section B — Security & compliance (30%)
  • B1. Data handling & retention controls (8%): Configurable retention, deletion process, access controls for files and transcripts.
  • B2. Encryption & access management (8%): Encryption in transit and at rest; role-based access; MFA availability.
  • B3. Legal/contract readiness (7%): Will sign NDA; provides DPA if needed; clear subprocessors disclosure.
  • B4. Security program evidence (7%): Provides documentation (e.g., security policies, audit reports, or certification letters if applicable).
• Section C — SLA & delivery operations (20%)
  • C1. Turnaround SLA options (8%): Standard and rush tiers; clear definition of “delivered.”
  • C2. Rework/corrections SLA (6%): Time to fix issues found in QA; escalation path.
  • C3. Workflow fit (6%): File formats, timestamps, integrations/export options, naming conventions, batch handling.
• Section D — Support & communication (10%)
  • D1. Support responsiveness (5%): Response times by channel; coverage hours; clarity of next steps.
  • D2. Accountability & reporting (5%): Issue tracking, root-cause notes, QA feedback loop, periodic performance review.

Suggested scoring bands

• 90–100: Strong fit; proceed to contracting after a successful pilot.
• 75–89: Possible fit; proceed only if gaps are low-risk or can be fixed in writing.
• Below 75: High risk; do not proceed without major changes.

Pilot test method (with acceptance thresholds)

A pilot prevents surprises after you commit volume or migrate workflows. Keep it small, realistic, and measurable.

Step 1: Build a representative pilot set

Select audio that matches your real work, not best-case recordings. Include variety so you can see how the vendor performs under stress.

• Length: 60–120 minutes total audio (split into multiple files).
• Speaker types: 1:1 interview, 4–8 person meeting, and at least one file with overlapping speech.
• Difficulty: one clean recording, one noisy call, one with strong accents or technical terms.
• Content risk: include the kinds of names, numbers, and terms that matter to you.

Step 2: Provide the same inputs to every vendor

• Your required style guide (verbatim vs clean read, timestamps, speaker labels).
• A glossary list (names, acronyms, product terms) and any “must not change” spellings.
• Clear instructions for unintelligible audio (e.g., use [inaudible 00:12:03]).

Step 3: Define how you will grade

Pick one grading method and use it consistently. You can grade by sampling (faster) or full review (slower but clearer for small pilots).

• Sampling method: Review 10 minutes per file plus all sections containing names/numbers you flagged.
• Full review method: Review 100% of the transcripts for the pilot set.

Step 4: Track errors by category

Create a simple log with the timecode, error type, and severity. Severity helps you avoid overreacting to minor punctuation issues.

• Critical: wrong name, wrong number/date, wrong speaker attribution that changes meaning, missing sentence that changes the record.
• Major: repeated mishearing of a key term, missing speaker labels, confusing paragraphing that slows review.
• Minor: punctuation, filler words (if clean read), small style inconsistencies.

Step 5: Acceptance thresholds (example you can adapt)

Set thresholds that match your use case and risk. These are practical starting points; tighten them for legal, medical, or compliance-heavy work.

• Names & proper nouns: ≥ 98% correct on a pre-defined list of flagged names (e.g., no more than 2 errors per 100 occurrences).
• Numbers/dates/units: ≥ 99% correct on flagged numeric items (e.g., no more than 1 error per 100 items).
• Speaker attribution: ≥ 95% correct speaker labels in reviewed segments; 0 critical misattributions in decision-critical sections.
• Turnaround: ≥ 95% of pilot files delivered within the agreed turnaround window; document any exceptions and causes.
• Support responsiveness: First response within the window you require (example: within 4 business hours for pilot issues), plus a clear resolution plan.
• Security evidence: Required documents provided and accepted by your internal review before production data begins.

Step 6: Decide pass/fail and next steps

• Pass: Vendor meets all critical thresholds; finalize SLA, security terms, and escalation path.
• Conditional pass: Vendor meets critical thresholds but misses a minor one; require corrective actions and re-test one file type.
• Fail: Vendor misses any critical threshold; do not proceed until they can re-run the pilot successfully.

Security due diligence checklist (questions to ask)

Ask for specific answers and evidence. If a vendor cannot explain their controls in plain language, treat that as a risk.

Core security questions

• How do you restrict staff access to customer files (role-based access, least privilege)?
• Do you support MFA for customer accounts?
• Is data encrypted in transit and at rest?
• What is your default retention period for audio and transcripts, and can we change it?
• How do you handle deletion requests, and how long do they take?
• Do you use subprocessors, and can you list them?
• Can you sign an NDA and, if needed, a DPA?

If you handle personal data

If your audio includes personal data and you operate under GDPR, you typically need a contract that covers processor obligations and subprocessors. You can reference the basics in GDPR Article 28 requirements when you build your vendor checklist.

Common pitfalls when comparing transcription vendors

• Only measuring overall accuracy: A transcript can look “good” but still miss names and numbers that matter.
• Skipping speaker attribution tests: Meetings and panels break many workflows if labels are wrong.
• Not defining “turnaround”: Clarify time zone, business hours, and what happens when the vendor needs clarification.
• Over-trusting security summaries: Ask for documentation and confirm deletion and retention in writing.
• Failing to test your worst audio: Pilot with real-world noise, accents, and crosstalk.
• No rework path: Require a clear corrections SLA and escalation route.

Decision criteria: choosing between human, AI, or a hybrid workflow

Many teams evaluate vendors that offer different production methods. Your scorecard still works, but you should decide what “good enough” means for each use case.

When automated transcription can fit

• You need fast drafts for internal notes or search.
• The audio is clean and has few speakers.
• You have time for human review or proofreading.

If you plan to start with AI, you can compare options using automated transcription as one lane in your pilot and score it with the same acceptance thresholds.

When human transcription is a safer default

• You publish transcripts or use them as records.
• You need strong performance on names, numbers, and speaker attribution.
• Your audio is noisy, technical, or has many speakers.

When hybrid makes sense

• You want speed from AI plus a human QA layer for high-risk errors.
• You can tier work: draft-only vs publish-ready transcripts.

If your internal team reviews transcripts after delivery, consider building a formal QA step using transcription proofreading services as a comparable benchmark for “publish-ready” quality.

Common questions

How many vendors should we pilot?

Two to four vendors is usually enough to see meaningful differences without creating too much review work. Run the same pilot set and grading method for each vendor.

What is the best way to test speaker attribution?

Use a meeting-style file with at least four speakers and some interruptions. Grade speaker labels in timed segments and flag any critical misattributions in decision-heavy sections.

Should we require timestamps?

If you review audio, edit video, or need auditability, timestamps save time. Decide whether you need periodic timestamps (e.g., every 30–60 seconds) or change-of-speaker timestamps.

What should an SLA include besides turnaround time?

Include definitions (what “delivered” means), rework/corrections timelines, escalation steps, and how the vendor handles low-quality audio. Put the format requirements in writing so “on time” does not mean “delivered in the wrong format.”

How do we compare vendors if one uses AI and another uses humans?

Use the same acceptance thresholds for the output you need. If you can accept a draft, set draft thresholds; if you need publish-ready text, require publish-ready thresholds and include a proofreading step for AI output.

What evidence should we ask for in a security review?

Ask for written policies, retention/deletion details, access controls, and contract terms like an NDA or DPA when needed. For teams handling personal data, align your checklist with your internal privacy process and the vendor’s subprocessors list.

What do we do if a vendor misses thresholds but we still like them?

Offer one retest after corrective actions, using a new file of the same type that failed. Do not waive critical thresholds for names, numbers, or speaker attribution if those errors create downstream risk.

Putting the scorecard into action (simple workflow)

• 1) Define requirements: use cases, turnaround, formatting, and security needs.
• 2) Shortlist vendors: pick 2–4 that can meet your baseline.
• 3) Run the pilot: same dataset, same instructions, same grading.
• 4) Score results: fill the scorecard, attach evidence, log errors.
• 5) Decide and document: thresholds, SLA language, support path, and rework rules.

When you are ready to compare real options, GoTranscript can support different workflows—from AI drafts to human-reviewed deliverables—so you can match accuracy, security, and turnaround needs to the right process. You can learn more about our professional transcription services and use the scorecard above to evaluate fit in a transparent way.