app.coval.dev

If you're new to Ahrefs and want to run your first SEO audit, the fastest path is: open Site Explorer, enter your target URL, review the Overview for a health snapshot, then dig into Organic Keywords, Top Pages, and Site Audit to find specific problems. From there, build a short prioritized to-do list instead of trying to fix everything at once.

This tutorial walks through that workflow using a realistic starting scenario, explains what the numbers mean, and shows how to turn findings into actions.

Before You Start: Pick a Narrow Scope

A common beginner mistake is auditing an entire large website on day one. The reports become overwhelming, and you can't tell which issues matter.

Instead, choose one of these starting points:

• A single important page (your homepage or a key product/service page)
• A small site (under ~50 pages, e.g., a personal blog or small business site)
• One section of a bigger site (e.g., /blog/)

For this tutorial, assume you're auditing a small business site with about 30 pages. The same steps scale up later.

You'll need an Ahrefs account to follow along. Ahrefs offers paid plans, and pricing and feature limits change over time, so check the current Pricing page for what's included in each tier before committing.

Step 1: Enter Your Target in Site Explorer

Site Explorer is Ahrefs' core tool for analyzing any website or URL.

1. Open Site Explorer from the top navigation.
2. In the search box, paste your domain (e.g., example.com).
3. Choose the Exact URL or Domain mode depending on scope. For a full-site view, use Domain or Prefix; for a single page, use Exact URL.
4. Press Enter.

You'll land on the Overview report. Don't try to absorb everything — focus on four numbers first.

Reading the Overview Snapshot

Important caveat: Ahrefs' traffic and keyword numbers are estimates based on its own data. They won't match Google Search Console or your analytics exactly. Treat them as directional, not absolute.

Step 2: See What You Already Rank For

Go to Organic Keywords in the left sidebar. This shows queries where your site appears in search results.

Sort by Traffic (descending) to see which pages bring the most estimated visitors. Then look for:

• Keywords ranking in positions 4–15 — these are often the easiest wins. A small content or on-page improvement can push them onto page one.
• Keywords with high volume but low position — potential opportunities if the topic is relevant.
• Irrelevant keywords — if you rank for something off-topic, it may signal thin or mismatched content.

Write down 5–10 of the position 4–15 keywords. These become your first optimization targets.

Step 3: Find Your Best and Weakest Pages

Open Top Pages. This ranks your URLs by estimated organic traffic.

Look for two things:

1. Your top performers — understand what topics and formats work. Can you create more content like this?
2. Pages with traffic but poor rankings — these may need on-page fixes (title, headings, internal links).

If a page gets zero traffic and targets a topic you care about, it's a candidate for a rewrite or consolidation.

Step 4: Run a Technical Site Audit

Now move to Site Audit. This crawls your site and flags technical and on-page issues.

1. Click Site Audit → New project.
2. Enter your domain and set crawl settings (default is usually fine for a small site).
3. Start the crawl and wait for it to finish.

Once complete, you'll see a Health Score and a list of issues grouped by category.

Which Issues to Fix First

Not all issues are equal. Prioritize in this order:

Ignore low-impact warnings (like minor meta description length) until the big items are handled.

Step 5: Turn Findings Into a To-Do List

You now have raw data. Convert it into a short, actionable list. Example:

1. Fix 3 broken links found in Site Audit.
2. Rewrite title tags on 5 pages with duplicate titles.
3. Improve 4 pages ranking in positions 6–12 by adding missing subtopics and internal links.
4. Remove or update 2 thin pages with no traffic.

Keep the list to 5–10 items max for your first audit. Finishing a short list beats starting a long one.

Common Beginner Mistakes

• Chasing every red flag. Site Audit flags many minor issues. Fix what affects rankings and users first.
• Trusting estimates as exact numbers. Ahrefs data is modeled, not measured from your analytics.
• Auditing a huge site too early. Start small to learn the interface.
• Ignoring search intent. A page can be technically perfect but still fail if it doesn't match what searchers want.
• Forgetting to re-crawl. After fixes, run Site Audit again to confirm improvements.

Where to Go Next

Once your first audit is done:

• Compare with competitors using Site Explorer's Competing Domains and Content Gap reports.
• Track keyword rankings over time with Rank Tracker.
• Explore backlink opportunities in the Backlinks and Link Intersect reports.
• Set up recurring Site Audit crawls so new issues surface automatically.

Your first audit isn't about perfection — it's about building a repeatable habit: enter a target, read the key reports, pick the highest-impact fixes, and act. Do that once a month and your site's health compounds.

Scaling a voice AI system means removing the bottlenecks that appear when concurrent sessions, audio volume, or model complexity grow. The work usually splits into four areas: latency budgets, concurrent session handling, model inference capacity, and cost control. You can start by measuring where time and money actually go, then apply the architecture changes below in order of impact.

Where Voice AI Systems Break Under Load

A voice pipeline is a chain of stages, and the slowest stage sets your ceiling. Typical stages:

1. Audio capture and transport — client microphone to server, often over WebSocket or WebRTC.
2. Speech recognition (ASR) — streaming or chunked transcription.
3. Language understanding and dialogue management — intent, context, turn-taking.
4. Response generation — LLM or rule-based.
5. Speech synthesis (TTS) — text back to audio.
6. Emotion and prosody analysis — optional but expensive, often run in parallel.

Each stage adds latency, and each has a different scaling profile. ASR and TTS are usually the first to saturate because they process continuous audio streams rather than discrete requests.

The Three Bottlenecks That Matter Most

Emotion and prosody models compound this: they often run alongside ASR on the same audio, so they double the compute per stream unless batched or run selectively.

Architecture-Level Scaling Strategies

Separate Stateless and Stateful Layers

Keep session state (conversation history, partial transcripts, turn context) in a fast store like Redis, and keep the model-serving layer stateless. This lets you add or remove inference workers without migrating live sessions. When a worker fails, the session can resume on another worker because the state lives elsewhere.

Stream Instead of Batch

Chunked or streaming ASR and TTS reduce time-to-first-token and let you start playback before the full response is generated. The trade-off is more complex error handling: partial results can be wrong, so you need a strategy for correction or re-query.

Route by Complexity

Not every turn needs the largest model. A practical pattern:

• Short, predictable turns (confirmations, yes/no) → small model or cached response.
• Open-ended or emotional turns → full pipeline with emotion analysis.
• Offline or low-priority → batch queue at lower cost.

This routing is one of the highest-leverage cost levers, because it avoids paying premium inference prices for trivial exchanges.

Autoscale on Queue Depth, Not Just CPU

GPU utilization is a lagging indicator for voice workloads. Scale on queue depth or time-to-first-audio instead. If requests wait more than your latency budget before a worker picks them up, add capacity even if GPU utilization looks moderate.

Cost and Performance Trade-offs

Every scaling decision trades money for responsiveness. The main levers:

• Model size vs. latency: smaller models are faster and cheaper but less accurate, especially on noisy audio or emotional speech.
• Streaming vs. batch: streaming improves perceived latency but keeps connections open longer, increasing concurrent-session cost.
• Emotion analysis on/off: running it on every turn multiplies compute; running it only when confidence in intent is low keeps cost down.
• Caching: repeated phrases (greetings, confirmations) can be pre-synthesized and cached, cutting TTS calls.
• Regional deployment: placing inference near users reduces transport latency but may increase infrastructure cost if traffic is uneven.

A useful rule: optimize for p95 time-to-first-audio first, then reduce cost per minute without pushing p95 past your budget. Users forgive a slightly slower average more than they forgive occasional long stalls.

Metrics to Monitor While Scaling

Track these continuously, not just during incidents:

• Time to first audio (TTFA) — the single best proxy for perceived responsiveness.
• End-to-end turn latency — from user stops speaking to system starts responding.
• Concurrent active sessions — and the ratio of sessions to available model slots.
• Queue depth per stage — ASR, LLM, TTS separately; a single deep queue tells you which stage to scale.
• Error and retry rate — streaming failures often show up as retries before they show up as errors.
• Cost per conversation minute — broken down by stage, so you can see which model dominates spend.
• Interruption and barge-in success rate — a scaling-sensitive behavior; it degrades when latency rises.

A Practical Scaling Sequence

If you are starting from a working prototype, apply changes in this order:

1. Instrument every stage with timing and queue metrics before changing anything.
2. Move session state out of the model workers so you can scale horizontally.
3. Add streaming to ASR and TTS to cut perceived latency.
4. Introduce routing so simple turns skip the heaviest models.
5. Autoscale on queue depth with a hard cap on time-to-first-audio.
6. Cache deterministic responses (greetings, confirmations, error prompts).
7. Re-evaluate emotion analysis — run it selectively rather than on every turn.

Each step is independently useful, so you can stop when your latency and cost targets are met. The order matters because state separation and instrumentation make the later optimizations measurable rather than guesswork.

COVAL (app.coval.dev) is a learning resource focused on the architecture of voice AI. According to its own description, it covers the full stack "from speech recognition to emotional intelligence" and is aimed at people who want to build, scale, and evaluate voice AI systems. If you are looking for conceptual and architectural guidance on voice AI rather than a hosted product or API, this site is worth a visit. If you need a ready-to-use voice AI service or SDK, this is not that.

What the site covers

Based on the site's stated scope, the content spans the pipeline of a voice AI system:

• Speech recognition — the input layer that turns audio into text.
• Emotional intelligence — the layer that interprets tone, affect, and intent beyond literal words.
• Architecture — how these components fit together as a system.
• Build, scale, and evaluate — practical concerns for taking a voice AI system from prototype to production and measuring whether it works.

The framing is architectural and educational: it explains how voice AI systems are structured and how to reason about them, rather than selling a specific product.

Who it is for

What to expect from the content

The site positions itself around three verbs: build, scale, evaluate. That suggests the material is organized around the lifecycle of a voice AI system rather than around a single model or vendor. Topics like speech recognition and emotional intelligence are treated as parts of one architecture, which is useful if you are trying to understand how the pieces interact — for example, how recognition errors propagate into downstream emotion or intent detection.

Because the description emphasizes architecture, expect conceptual explanations and system-level reasoning rather than step-by-step tutorials for a specific tool.

How to decide whether to visit

• Visit if you are designing or evaluating a voice AI system and want a structured view of how the components (recognition, emotion, and the layers between) fit together, and how to think about scaling and evaluation.
• Skip if you need a hosted voice API, pricing, or a specific implementation guide for a named framework — the site's own description does not indicate it offers those.

The site's stated purpose is the clearest signal available: it is a place to learn the architecture of voice AI and how to build, scale, and evaluate such systems.

Voice AI architecture is a layered pipeline: audio is captured and preprocessed, converted to text by automatic speech recognition (ASR), enriched with acoustic and linguistic features, interpreted for meaning and intent, and finally mapped to a response that may include emotional or prosodic cues. Speech recognition is the entry layer, not the whole system. Everything above it — diarization, intent detection, sentiment and emotion classification, and speech synthesis — depends on the quality and timing of the layers below. COVAL (app.coval.dev) frames this same span, describing its subject as the architecture of voice AI "from speech recognition to emotional intelligence," including how to build, scale, and evaluate such systems.

The layer stack at a glance

Speech recognition: the foundation layer

ASR converts a waveform into a sequence of words. A conventional architecture has three learned or specified parts:

• Acoustic model — maps short frames of audio features (e.g., mel-spectrogram or MFCC frames) to phonemes or subword units.
• Lexicon/pronunciation model — maps those units to words (often implicit in end-to-end models).
• Language model — scores word sequences so the decoder picks the most probable transcript.

Modern end-to-end systems (CTC, RNN-T, attention-based encoder-decoder) collapse these into one network, but the functional split still explains where errors come from: noisy audio hurts the acoustic model, domain jargon hurts the language model, and overlapping speakers hurt both.

Why it constrains everything above: intent classifiers, sentiment models, and TTS prosody controllers all consume ASR output. A word error rate that looks acceptable in isolation can flip an intent ("I can't cancel" vs. "I can cancel") and invert the emotional read of a sentence.

From recognition to understanding

Once you have a transcript, the pipeline adds structure:

1. Diarization and speaker attribution — "who spoke when," so multi-party audio becomes per-speaker turns.
2. Segmentation — splitting a stream into utterances or turns suitable for downstream models.
3. Intent and slot extraction — classifying what the user wants and pulling out parameters (dates, names, amounts).
4. Dialogue state tracking — maintaining what has been established across turns.

Each step consumes the previous step's output, so latency and errors compound. This is the layer where most "the assistant misunderstood me" failures actually originate — not in ASR, but in segmentation or intent resolution.

Where emotional intelligence enters

Emotional intelligence in voice AI is not a single model; it is a fusion of signals:

• Acoustic/prosodic features — pitch, energy, speaking rate, pauses, voice quality. These carry arousal and much of the emotional signal independent of words.
• Lexical/semantic features — word choice and sentiment from the transcript.
• Contextual features — conversation history, speaker identity, and the task at hand.

A system may classify emotion into discrete categories (happy, sad, angry, neutral) or continuous dimensions (valence, arousal, dominance). The key architectural point: emotion models sit above ASR and understanding, so they inherit ASR errors, and they need alignment between audio frames and words to fuse acoustic and lexical evidence correctly.

Emotional intelligence also flows downward into generation: a TTS layer with prosody control can adjust pitch, rate, and emphasis to sound empathetic, calm, or urgent. That closes the loop from perception to expression.

Data flow and dependencies

audio → [preprocess/VAD] → [ASR] → transcript + timestamps
                                   ├→ [diarization] → speaker turns
                                   ├→ [NLU: intent/slots] → structured meaning
                                   └→ [acoustic features] ─┐
                                                           ├→ [emotion fusion] → affect state
                        [NLU output] ──────────────────────┘
affect state + meaning → [dialogue policy] → [response generation] → [TTS + prosody] → audio out

Two properties matter when you build this:

• Streaming vs. batch. Real-time voice requires incremental ASR and partial-result handling; batch pipelines can wait for full utterances and are simpler but unusable for live conversation.
• Latency budget. Every layer adds delay. A conversational target usually forces you to trade accuracy for speed at the ASR and emotion layers, or to run layers in parallel rather than strictly in sequence.

Core components to master when building

• Voice activity detection and endpointing — deciding when a user has finished speaking.
• ASR with domain adaptation — custom vocabulary, fine-tuning, or biasing for your use case.
• Diarization — essential for meetings, call centers, and any multi-speaker setting.
• NLU and dialogue management — intent, slots, and state.
• Emotion/affect models — acoustic, lexical, or fused.
• TTS with prosody control — for expressive output.
• Evaluation harness — WER for ASR, intent accuracy/F1 for NLU, and separate metrics for emotion (accuracy, F1, or correlation for dimensional models). COVAL's stated scope includes how to evaluate these systems, which is the layer most teams under-invest in.

How to use this framing

If you are designing or auditing a voice AI system, walk the stack top-down to find where a failure originates, then bottom-up to fix it:

1. Start at the symptom (wrong response, flat tone, misread emotion).
2. Trace it down: was the transcript wrong (ASR), the turn split wrong (segmentation), the intent wrong (NLU), or the affect signal missing (emotion layer)?
3. Fix at the lowest layer that explains the error, because higher layers cannot recover information the lower layers lost.

For a structured treatment of this full span — recognition through emotional intelligence, plus building, scaling, and evaluation — COVAL at app.coval.dev is the reference point this overview follows.

Evaluate a voice AI system by testing it at each stage of its pipeline — speech recognition, language understanding, response generation, speech synthesis, and emotional/social intelligence — rather than judging the whole system on one "quality" score. COVAL (app.coval.dev) frames voice AI as an architecture that runs from speech recognition to emotional intelligence, and that framing is useful for evaluation: each layer has its own failure modes, so each layer needs its own measurement. Use the approach below when you are choosing a vendor, running a regression test before a release, or diagnosing why a system that "sounds fine" still frustrates users.

Why you evaluate layer by layer

A single end-to-end score hides where the problem is. A system can transcribe perfectly and still fail because it interrupts the user, or understand intent correctly and still answer in a flat, robotic tone. Splitting evaluation by layer tells you what to fix and what to leave alone.

Metrics and methods per layer

Speech recognition

• Word Error Rate (WER) — the standard accuracy metric. Lower is better, but WER alone is misleading: a system can have low WER and still fail on the specific words that matter (names, numbers, product terms).
• Domain-specific term accuracy — build a small test set of the words your users actually say and score those separately.
• Test conditions matter: run the same audio through clean and noisy variants, and across accents and speaking speeds present in your user base.

Language understanding

• Intent accuracy and entity F1 on a labeled set of real utterances.
• Confusion analysis — which intents get mistaken for which. This is usually more actionable than a single accuracy number.

Response generation

• Task success rate — did the user get what they came for? This is the metric that matters most to the business.
• Relevance and correctness — scored by human raters on a sample, since automated scoring of open-ended answers is unreliable on its own.
• Turn count and latency — how many exchanges and how long before the answer. Both drive perceived quality.

Speech synthesis

• Intelligibility — can listeners repeat back what was said? Test with names, numbers, and abbreviations.
• Naturalness — human raters score prosody and pacing, typically on a 1–5 scale.

Emotional and social intelligence

• Emotion recognition accuracy on labeled audio where the speaker's state is known.
• Appropriateness of response — did the system acknowledge frustration, or plow ahead? This almost always requires human judgment.

Automated vs. human evaluation

Use both, and match the method to the question.

A practical pattern: run automated metrics on every change as a gate, then run a small human review on a fixed sample before major releases. Keep the human sample stable so scores are comparable over time.

Turning results into iteration

1. Build a fixed evaluation set from real user audio and transcripts, including the hard cases — noise, accents, interruptions, emotional calls.
2. Score each layer separately so you know which component regressed.
3. Prioritize by user impact, not by the biggest metric gap. A small WER improvement on rare words may matter more than a large gain on common ones.
4. Re-run the same set after each change to confirm the fix didn't break another layer.
5. Watch for metric–experience drift: if scores improve but complaints don't drop, your evaluation set is missing the failure users actually hit.

Common pitfalls

• Judging the whole system on one number. End-to-end scores tell you that something is wrong, not what.
• Testing only on clean, scripted audio. Real calls include background noise, interruptions, and mid-sentence changes of mind.
• Ignoring latency. A correct answer delivered too slowly reads as a failure to the user.
• Skipping the emotional layer. For voice, tone and timing are part of correctness, not decoration.
• Letting the test set go stale. As your product and users change, so should the evaluation set.

If you are new to the field, COVAL's framing — architecture from speech recognition through emotional intelligence, plus how to build, scale, and evaluate these systems — is a reasonable map for organizing your evaluation around the same layers you build.