You need to know whether an AI voice agent can genuinely replace an on-call staff member for your evenings, weekends and holidays. This article walks you through what to test, what to measure, and the questions to put to any vendor in writing before you commit budget. It is written for business owners and operations leads who are evaluating AI after hours answering with their own money, and who can tell the difference between a vendor who understands the problem and one assembling plausible sentences.
This is an independent buyer's guide from Sysevo. We are not affiliated with Deepgram. Feature sets and pricing change often, so treat anything you read here as a prompt to check the vendor's own documentation directly rather than a guaranteed fact.
What You Are Actually Testing
Out of hours call handling works only if three things happen in sequence, and each one can fail independently. First, the system must recognise what the caller is saying accurately enough to understand their intent. A speech recognition engine that mishears "I have a burst pipe" as "I have a first prize" does not hand your morning team useful information. Second, the system must write what it understood into your CRM or ticketing system cleanly enough that a human can act on it without hunting for context. Third, it must route urgent calls correctly, escalating a genuine emergency while filtering out routine enquiries that can wait until morning. If any of those three chains breaks, the whole system fails operationally, and you end up with either missed emergencies or wasted callback time chasing false alarms.
Most AI voice platforms, including Deepgram as a speech recognition component within a wider system, sit in the first layer: converting audio to text. But a speech recognition engine alone is not a complete after hours solution. You need to evaluate how well it integrates with the routing and CRM capture steps that follow. This is where most evaluations go wrong. Vendors show you how well the speech engine performs in isolation, not how well it performs when combined with your specific CRM, your call routing rules, and your actual caller patterns on a Tuesday at 2 a.m.
Before you run a trial, understand what you are testing separately. You are testing speech accuracy. You are testing integration quality. You are testing whether the system reliably escalates what matters and suppresses what does not. You are testing whether your team can actually use the data it captures. These are four different tests, and a vendor demo that passes one does not mean it passes the others.
How to Evaluate Deepgram for AI After Hours Answering: Week One
The first week of any trial should establish a baseline. You need to know how well the speech recognition performs on your actual callers, in your actual environment, using real-world call patterns. Do not use vendor-supplied test calls. Do not use a quiet office. Use production calls during actual off-hours, even if that means waiting a week to accumulate enough volume.
Configure the system to log every call to a folder you can access. Record both the audio and the transcription the system generated. You need at least 50 calls to establish a real pattern. That takes a week for most businesses. Smaller operations may need two or three weeks. Do not draw conclusions from fewer than 40 calls. For each call, measure three things: whether the transcription captured the essential intent (not perfection, but enough to action), whether it went to the right team member or queue, and whether any missed emergency calls were present in the batch.
Create a spreadsheet with columns for call timestamp, what the caller actually wanted (you can listen back and write this), what the system transcribed, whether the intent was captured correctly, and whether the routing was right. This is tedious. It is also the only way to know whether the system works for your business. A vendor who resists giving you access to raw transcriptions and audio logs is hiding something. That answer alone should end a sales process.
Measuring Speech Recognition Accuracy for Your Scenario
Speech recognition vendors often quote accuracy as a percentage, but the number means less than it sounds. A 95% word accuracy rate can still miss the core intent of a call. A caller saying "I need to cancel my appointment on Thursday" might be transcribed as "I need to cancel my apartment on Thursday." The word accuracy is high. The actionable accuracy is zero.
For after hours answering, measure intent accuracy instead. Did the system correctly identify why the person called? Create a simple taxonomy: appointment cancellation, emergency, billing enquiry, callback request, complaint, or unclear. Listen to each call, mark what it actually was, and compare it to what the transcription indicates. Aim for at least 90% intent accuracy on your own call mix. Below that, you will spend more time correcting transcriptions than you would have spent taking the calls yourself.
Pay special attention to calls with background noise: traffic, music, other conversations. After hours callers ring from cars, from homes with kids and pets, and from locations where your environment assumptions do not hold. A speech engine trained on quiet office calls will stumble here. Test it explicitly. If more than 15% of your actual calls happen in high-noise environments and the system misses intent on more than 10% of those, flag this in writing to the vendor and ask for specific mitigation. Accept only concrete answers: retraining on noisy audio, noise-cancellation preprocessing, or fallback to human escalation at a defined threshold.
Testing CRM Integration and Data Capture
Speech recognition is only useful if the transcription reaches your team in a form they can actually use. Many AI voice systems perform well on the speech side but lose information or format data poorly when writing to your CRM. Test this explicitly by setting up a full integration with your actual CRM or ticketing system during the trial, not a demo version or a test environment.
Configure the system to capture at least these fields for every call: caller phone number, transcription of the call, system-detected intent or category, timestamp, and any specific details the caller mentioned (names, account numbers, appointment dates). Run 20 calls through to your CRM and check them manually. Are all the fields populated? Are the transcriptions readable and actionable? Is the caller's phone number captured cleanly enough to match against existing customer records? Many systems capture everything but format it as a single long text blob, which forces your team to re-read the entire call to find one fact.
Ask the vendor these questions in writing: Can you map custom fields from the transcription to my CRM? What happens if the transcription is incomplete or the caller hangs up before finishing their sentence? Does the system attempt to fill in missing fields with a follow-up question, or does it pass a partial record to your team? If the CRM integration fails (your API changes, credentials expire), what is the fallback? Does the system continue to record calls and attempt retry, or does it drop data?
Call Routing and Escalation Rules
After hours answering only works if the system routes correctly: urgent calls go to whoever is on call, routine calls get logged for morning follow-up, and false emergencies do not wake your team at 3 a.m. every night. This is where speech recognition meets business logic, and most trials do not test it properly.
During your week-one trial, set up three call buckets: definite emergencies (your business defines these: burst pipes, security alarms, active incidents), definite routine (general information, pricing enquiries, non-urgent callbacks), and uncertain (calls that could go either way). Route definite emergencies to a test number. Log the others. Over 50 calls, measure how many fell into the wrong bucket. Aim for zero false emergencies. A system that flags one routine call per 100 as an emergency will wake your team roughly every other night and destroy your on-call arrangements.
Ask the vendor in writing: How do I define emergency keywords or phrases? Can I test rule changes without pushing them to production? If a caller says "this is not urgent" but mentions a keyword like "flooding", which takes precedence? Some systems use keyword matching alone, which is fragile. Others use the full transcription to contextualise keywords. Some use multiple detection methods and rank them. Ask how they work and request that they explain their approach in writing. If they cannot articulate it clearly, they may not understand it themselves.
Testing Human Handoff and Escalation Quality
No AI system handles every call perfectly. You need to know what happens when the system cannot resolve a call or needs to escalate. Ideally, your after hours agent or on-call staff member receives a short summary and the option to listen to a clip of the call, not a wall of transcribed text.
During your trial, simulate escalation by setting the system to flag 5% of calls for human review, even if they seem routine. Check how those flagged calls arrive: is there a context summary, or just a transcript? Can you listen to the original audio without digging into logs? Is the caller's identity confirmed or flagged as unverified? If a human has to re-ask all the questions the AI already asked, the escalation failed. You are not improving the customer experience. You are doubling the work.
Measure escalation overhead by timing how long it takes your on-call person to understand a flagged call from the summary you are given. If it takes more than 90 seconds to get context and call back the customer, your AI is adding friction instead of removing it. Ask the vendor whether the system can generate summaries of longer calls (calls over 3 minutes) automatically and whether those summaries can be customised to your business. A generic summary is less useful than one that knows your products, your common issues, and your customer segments.
Availability, Reliability and Fallback
An after hours answering service that works 99% of the time is useless. The 1% downtime will happen at the worst possible moment: a holiday, a weekend during peak demand, or a night when your on-call person is genuinely unavailable. You need to understand what happens when the system fails and whether there is a fallback.
Ask the vendor these questions in writing: What is your uptime SLA for speech recognition and call routing? What does it cover? (Some SLAs exclude force majeure or DDoS attacks, which means they cover nothing.) If the service is down, do calls fail silently or are they routed to a fallback number? Can you configure a fallback phone number, email queue, or SMS alert so urgent calls do not vanish? How quickly would you know if the service is down, and would you alert us automatically? If you find out 12 hours later because customers complained, the SLA is worthless.
Check the vendor's status page. Does it exist, and is it updated in real time? Can you subscribe to updates? During your trial, check the status page weekly to see whether incidents are logged and communicated. If there are no logged incidents during your trial, ask whether they are filtering them out. Every service has incidents. A vendor claiming zero downtime is not being honest.
Security, Data Retention and Compliance
After hours calls often contain sensitive information: account numbers, dates of birth, payment card details, emergency contact information. The system handling these calls must be secure, and you must own the records.
Ask the vendor these questions in writing: Where are call recordings and transcriptions stored? In which countries? For how long? Can I delete specific calls or entire batches? What encryption is used in transit and at rest? Who has access to my data? (Include staff, contractors, security researchers, and anyone else.) Can I audit access logs? Do you delete data after a specified period, or do I have to request deletion manually? If they cannot give you written answers to all of these, do not proceed. Assume your data will be used for training, sold for analytics, and kept forever.
Check whether the vendor is certified for compliance with standards that matter to you: HIPAA (if you handle health data), PCI DSS (if you handle card details), GDPR (if you have EU customers or staff). Certification is not a guarantee, but its absence is a red flag. Ask for the vendor's security audit reports or certifications. If they will not share them under NDA, that is a signal they do not have them.
When AI After Hours Answering Is the Wrong Choice
AI voice agents are excellent at filtering routine calls and capturing basic information, but they struggle in specific scenarios and you should know before you buy whether any of these apply to you. If you run a medical practice and a significant share of after hours calls involve clinical judgement, diagnosis, or triage beyond "book an appointment", AI will miss critical information. Humans are better at detecting urgency from tone and context. If your business operates in a niche industry with specialised terminology that the system cannot be trained on, transcription accuracy will suffer. If you have a small number of after hours calls (fewer than five per night), the cost of a properly integrated AI system often exceeds the cost of a single on-call staff member.
AI struggles with strong accents that differ significantly from the training data. If a large share of your callers are non-native English speakers or speak with regional accents underrepresented in the vendor's training set, test this explicitly before committing. Accents are not a blocker, but they must be tested on your actual caller population, not on a vendor's demo. If your business requires the system to understand callers' emotional state and respond with empathy or de-escalation, current AI is not mature enough. The system can detect that someone is angry. It cannot reliably respond in a way that reduces that anger.
Finally, if your main goal is to reduce payroll costs, reconsider your expectations. A fully integrated AI after hours answering system costs time and money to implement, train and maintain. The savings come from handling predictable, routine calls. If your after hours call mix is 60% emergency and 40% routine, you will still need on-call staff. The AI filters the routine 40%. The payoff is operational: faster callback on legitimate issues, better hand-off information, fewer missed calls. If you cannot measure those benefits, you cannot justify the cost.
Creating Your Trial Plan and Success Criteria
Before you sign anything, document exactly what success looks like for your business. A trial without defined success criteria is a sales conversation, not an evaluation. Your success criteria should be specific, measurable, and based on the three steps outlined at the start: speech recognition accuracy, CRM capture quality, and routing correctness.
Create a one-page document with these sections: baseline metrics (how many calls do we get per week, what percentage are emergencies, what is our current on-call cost), trial duration (recommend 2-4 weeks), measured outcomes (intent accuracy target, escalation overhead target, false emergency rate target), red flags that end the trial (missing answers to security questions, uptime below 99.5%, transcription accuracy below 85% for your call mix), and decision date. Share this document with the vendor before the trial starts. If they push back on any of it, ask why. If they cannot commit to measurable outcomes, they are not confident in their product for your use case.
During the trial, do not wait until the end to surface problems. If speech recognition is failing on 20% of calls by day three, raise it immediately. Ask what the vendor will do to improve it. If the answer is "it should improve with more data", ask how much more data and how you will know when it has improved. If the vendor cannot give you a concrete path to success, end the trial early.
Cost Structure and Hidden Factors
Evaluate the full cost of ownership, not just the vendor's per-minute or per-call charge. Most AI voice platforms price speech recognition separately from the system that routes calls and writes to your CRM. Check the vendor's pricing page carefully. Ask these questions in writing: What is the cost per inbound call? Per minute of conversation? Are there monthly minimums? Do you charge differently for peak hours and off-peak hours? What about calls that route to human escalation? Do you charge per escalation, or is it included? What happens if calls fail mid-conversation due to your infrastructure or my network? Do I still pay?
Calculate your expected monthly cost by using your actual after hours call volume from the past three months. Multiply by the vendor's per-call rate. Add the cost of integration work (many vendors quote this separately or expect you to hire developers). Add the cost of any custom training the vendor needs to improve accuracy on your domain-specific language. Most deployments cost 30-50% more than the quoted per-call rate once you include integration and tuning. Get this figure in writing before you commit.
Compare this cost to your current after hours arrangement. If you employ an on-call person at £15 per hour and they receive 20 after hours calls per week, that costs roughly £780 per month. An AI system that reduces that load by 50%, freeing up your on-call person for only the genuine emergencies, might cost £400 per month and pay for itself. But if you are paying £1,500 per month for the AI and it only handles 30% of calls, you have not saved money. You have added cost. Ensure the math works before you start.
Integration Complexity and Implementation Time
A speech recognition engine that performs well in isolation can be slow and painful to integrate into your actual system. Most AI voice platforms require you to connect their API to your CRM, your call routing system, and your alerting system. This is rarely a checkbox. It usually requires custom development work and testing.
Ask the vendor: Do you have pre-built integrations for my CRM? If yes, have you tested them recently? If no, what is the estimated implementation time? Who pays for integration work? Is it included in the contract or a separate service? What SLA applies to integration work? If the vendor breaks your CRM integration with an API update, who fixes it? Many vendors will say "we will support you", which means nothing without an SLA in writing. Get a timeline for integration, a named point of contact, and a success criteria. If the implementation takes longer than four weeks without clear progress, escalate. Your business cannot wait indefinitely.
Questions to Put to Any Vendor in Writing
Before you commit budget, send the vendor a list of written questions and require answers before proceeding. Verbal assurances are worthless. Here are the questions you must ask: (1) Can I access raw transcriptions and audio logs during and after the trial? (2) What uptime SLA do you offer, and what does it cover? (3) If the service fails, what is the fallback? (4) Where is my data stored and for how long? (5) Can I delete my data on request? (6) What is your security certification status? (7) Who owns the transcriptions and recordings? (8) Can I use them for internal training? (9) Can I export them at any time? (10) How do you handle updates that affect my integration? Do I have to test them before they go live?
Require the vendor to answer all ten in writing within three business days. If they avoid any of them, ask why. If they give vague answers ("we take security very seriously"), ask for specificity. Do not accept "this is proprietary" as an answer to data ownership questions. If the vendor will not tell you who owns your data, assume they do and act accordingly. For any vendor you are seriously considering, ask for references from three customers with a similar call volume and use case. Contact at least one of them and ask what surprised them during implementation, what they wish they had known, and whether they would choose the same vendor again.
Frequently Asked Questions
How much does AI after hours answering typically cost?
Most vendors charge between £0.05 and £0.30 per inbound call, plus integration fees. For a business receiving 20 after hours calls per week, expect £50-£200 per month before integration. Get pricing in writing that specifies per-call rates, minimum monthly fees, and whether escalations are included or charged separately.
What is the difference between speech recognition accuracy and intent accuracy?
Speech recognition accuracy measures whether the system hears words correctly (95% word accuracy is common). Intent accuracy measures whether the system understands why the caller called (90%+ is required for after hours use). A call transcribed perfectly but routed to the wrong team is a failure. Test intent accuracy on your actual calls.
Can I test an AI voice system before buying?
Yes. Any vendor should offer a trial period of 2-4 weeks using your actual after hours calls. Insist on access to raw data: audio logs, transcriptions, and CRM captures. Do not accept a vendor demo as a trial. Measure the three key steps: speech accuracy, CRM integration, and routing correctness.
What happens if the AI voice system goes down?
This depends on the vendor's infrastructure and your configuration. Ask in writing: Does the service have a fallback to a human queue? Can you configure a backup number that rings when the AI is down? What is the recovery time for a complete service outage? Do not proceed without a clear answer.
Who owns the call recordings and transcriptions?
This varies by vendor and contract. Ask in writing: Do I own the recordings? Can I delete them? Can I use them for internal staff training? If the vendor owns them, they may use them for training their AI, which could expose your customer data. Require written clarity on data ownership before signing.
How long should a trial last before we make a decision?
For after hours calls, collect at least 40-50 calls before you draw conclusions. For most businesses, this takes 2-4 weeks. Do not decide based on fewer calls. If the vendor pushes you to commit earlier, they are prioritising sales speed over your confidence. Insist on a complete trial period.
Independent buyer's guide published by Sysevo. Sysevo is not affiliated with, endorsed by, or partnered with Deepgram, and Deepgram is the trademark of its owner. Product details change often, so confirm anything that matters to your decision with the vendor directly before you buy.
