u/Khade_G

One thing that keeps standing out in production voice/agent systems:

Users almost never speak the way demos assume they will.

They say things like:
- “Can you book me at that place my wife liked last month?”
- “Yeah the blue thing, not the other one”
- “Wait actually before that…”
- “The guy I talked to yesterday said something different”
- “I need the same appointment as last time but later”
- “Hold on my kid is talking to me”
- “No no not that account”

Technically, none of these are difficult, but operationally they break a huge percentage of agents because they combine:
- vague references
- implicit memory
- interruptions
- topic switching
- partial information
- emotional context
- and conversational repair behavior

A lot of public or client conversational datasets still skew toward:
- clean turns
- explicit intent
- cooperative users
- short interactions
- and benchmark-style phrasing

but real conversations are much messier than that.

Over the past few months, we’ve actually been sourcing real, consented conversational datasets on demand focused specifically around:
- indirect references
- interruption-heavy calls
- long-form conversations
- mixed intent
- off-script requests
- emotionally escalated interactions
- multilingual/code-switching behavior
- and conversational recovery scenarios

How it works: You simply put in a request for a specific dataset (e.g., 2,500 real-world customer support conversations with interruptions, vague references, topic switching, and mid-call intent changes) and we source/deliver it to you.

Out clients have been using these datasets both for:
- evaluation/stress testing
- and improving conversational robustness during training/fine-tuning.

These are often the exact interactions that determine whether an agent survives production traffic or collapses outside the demo.

Biggest takeaway so far:

The hardest conversational problems usually aren’t intelligence problems.

They’re context-management and interaction-reliability problems under messy real-world behavior.

If you’re actively running into these kinds of conversational gaps, feel free to DM me. Happy to help scope or source datasets around specific production failure modes.

Alternatively, if you already know your specific dataset needs, put a request in through the link on my profile page.

Cheers!

Full disclosure, my team built this platform and it’s been getting a lot of requests so wanted to share here in case it’s helpful.
Over the past few months, we’ve been helping teams source highly specific voice/telephony datasets that public benchmarks consistently miss.

Some examples:
- Off-script conversations (interruptions, mixed intent, layered objections)
- Noisy/real-world audio (background noise, bad mics, overlapping speech)
- Accents, dialects, and code-switching scenarios
- Call center edge cases (angry customers, retention, escalation flows)
- Long-call drift (context loss, topic switching, memory issues)
- Telephony degradation (latency, jitter, packet loss, one-way audio conditions)
- Multi-turn workflow scenarios (routing, fallback logic, tool failures)
- Domain-specific conversations (healthcare, finance, support, sales)

Biggest takeaway:

For most production voice AI systems, the bottleneck usually isn’t the model.

It’s dataset coverage around messy real-world deployment conditions.

Public or client datasets are usually enough for demos.

Custom datasets are what close the gap to production reliability.

The more complex the conversation and deployment environment becomes, the more valuable targeted data infrastructure becomes.

Some concrete requests we’ve been receiving recently:
- 1,000–3,000 customer support calls with interruptions, escalations, and retention scenarios
→ testing interruption handling + objection recovery

- 2,000+ multilingual / code-switching conversations across real-world accents
→ improving ASR + intent robustness

- 1,500 degraded VoIP/mobile calls with noise, overlap, and poor network conditions
→ testing performance under real telephony conditions

- 500–1,000 long-form conversations (5–10+ minutes)
→ evaluating context drift + memory

- structured call flows with failure cases (fallbacks, retries, routing errors)
→ validating workflow reliability + edge cases

If you’re actively running into voice AI dataset gaps that public benchmarks aren’t solving, feel free to DM me with what you need. Happy to help scope solutions.

Link in my profile if you already know your exact datasets needs.

Over the past few months, we’ve been helping teams source highly specific computer vision datasets that public benchmarks consistently miss.

Some examples:
- Industrial inspection edge cases (rare defects, anomaly classes, production variability)

- Difficult OCR scenarios (reflective packaging, embossed text, degraded print)

- Long-tail vision failures (low-light, oblique angles, motion blur, occlusion)

- Rear/partial vehicle datasets (specific viewpoints, regional variation, roadway deployment)

- Security/surveillance edge cases (poor camera quality, weather, unusual environments)

- Agricultural/drone imagery (crop health, NDVI, multispectral field conditions)

- Domain-specific operational scenarios where generic datasets fail to match deployment reality

Biggest takeaway:

For most production computer vision systems, the bottleneck usually isn’t the model.

It’s dataset coverage around messy real-world deployment conditions.

Public datasets are usually enough for demos.

Custom datasets are what close the gap to production reliability.

The more specialized the deployment environment becomes, the more valuable targeted data infrastructure becomes.

If you’re actively running into computer vision dataset gaps that public benchmarks aren’t solving, feel free to DM me with what you need, happy to help scope solutions.

Over the past few months, we’ve been helping teams source highly specific datasets that public benchmarks consistently miss.

Some examples:

- Off-script voice agent conversations (interruptions, objections, mixed intent)

- Real human SaaS workflow screen recordings

- Industrial OCR edge cases (reflective packaging, degraded print)

- Computer vision long-tail failures (low-light, oblique angles, occlusion)

- Agent workflow regression scenarios (schema drift, retries, stale state)

Biggest takeaway:

For most production AI systems, the bottleneck usually isn’t the model.

It’s dataset coverage around messy real-world deployment conditions.

Public datasets are usually enough for demos.

Custom datasets are what close the gap to production reliability.

The more specialized the deployment environment becomes, the more valuable targeted data infrastructure becomes.

If you’re actively running into dataset gaps that public benchmarks aren’t solving, feel free to DM me with what you need, always happy to compare notes or help scope solutions.