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Dialed

Connected

100%

Qualified

Meetings

Avg duration

42s

Total talk time

🧪 Prompt A/B test· primary config vs alt_config

Variant A

primary

Calls1

Qualified rate0%

Avg judge score4.0/10

Variant B

candidate

No calls routed yet.

⚙ A/B test setup· 20% to variant B

Variant B persona (everything else inherits from primary)

Riya ek warm aur professional sales representative h DSA Sathi se. Loan industry ka workflow samajhti h — DSAs ki day-to-day reality, commission tracking ki dikkat, bank coordination, aur lead leakage. Hinglish me natural baat karti h — technical CRM terms ko simple bhasha me explain karti h. Pushy bilkul nahi — pehle DSA ki actual problem samajhti h, fir solution dikhati h. Agar koi DSA bahut chhota h (1-2 loans / month), to honestly free trial suggest karti h. Trust-first approach — DSA ka business badhe, hum baad me fayda lenge.

Product

DSA Sathi CRM

DSA Sathi

Languages

hi-IN, en-IN

Qual fields

2 guardrails

Max call

5min

Recent calls

All calls →

When	Campaign	To	Duration	Score	Stage	Disposition
6/16/2026, 11:12:35 AM	—	+918503966857	42s	0	connected	Not interested

Speculative LLM (latency boost)

Race a fast draft model against the primary. ~1s faster TTFA on slow models. Shadow mode collects metrics without changing caller audio.

Enable speculative draft+verifyShadow mode — collect metrics only, do NOT emit draft to caller

Draft providerDraft model (optional)

Match threshold: 0.85Cosine ≥ this value keeps the draft. Lower = more aggressive (more latency wins, more pivots).Hard pivot on mismatch (interrupt TTS mid-word — accurate but can sound jarring)Skip speculation in OBJECTION state (recommended — preserves precision)

Quality features (per agent)

Override the org-wide env flags for this agent. "Default" inherits from env; "On" / "Off" force the behaviour regardless of the env value.

Multi-agent debate

Closer-vs-skeptic parallel personas fire on sticky objection / low confidence / OBJECTION state. ~1.5s extra LLM budget per triggered turn.

Self-reflection (inner monologue)

Per-turn hidden JSON thought fed into the next turn. Runs async during TTS playback so zero added latency, but costs 1 extra LLM call per turn.

LLM-as-judge post-call scoring

After each call, score the transcript on the active rubric and write to call_scores. One extra LLM call per completed call.

Voice biometric fraud check

Embeds caller voice + scores against org blacklist + deepfake detector at call start. Skip on low-value retention agents to save ~80ms per call.

Advanced sub-knobs (override env defaults)

Debate low-confidence threshold (0–1)Debate hard budget (ms)Reflection every N turns (≥1)LLM hard timeout (sec)

Whisper STT (only if STT provider = whisper)

⚠ Bot loads ONE model per process — first agent to dial wins. Restart bot to swap.

ModelDeviceCompute type

Call quality & turn-taking (advanced — blank = bot default)

How the bot listens and how patient it is with silence. Lower VAD stop/start = snappier but more likely to cut the caller off; higher = more patient. Idle timeout + reprompt count control how long it waits on a quiet line before giving up.

Idle reprompt timeout (sec)dead air before the bot re-checks · default 15Silence reprompts before hangupdefault 3 · higher = more patient with a quiet callerVAD stop (sec)silence before a turn ends · default 0.45VAD start (sec)speech needed to trigger · default 0.3VAD confidence (0–1)speech-detection strictness · default 0.7VAD min volume (0–1)background-noise floor · default 0.6Echo gate cooldown (sec)mute mic after bot speaks · default 0.4

Knowledge base

The product facts this agent may state on a call. Each entry is embedded and surfaced to the agent by the RAG retriever when it's relevant.

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Title

Smart specialists

Subagent specialists handle specific moments of the call alongside the main agent. Each is opt-in. The main agent always has a working fallback — if a specialist times out, the call continues normally.

Objection handler

When the bot detects a prospect objection (price, timing, trust, fit), a specialist crafts a tailored response using your objection bank + recent transcript. Soft fallback to classic logic on timeout — zero risk to live calls.

Qualifier (Wave 3)

A parallel watcher that runs every 3rd turn — predicts qualification score, flags missing fields, suggests next question. Async / soft hint; never blocks the bot.

Closer (Wave 4)

When the prospect signals buying intent OR just crossed the qualification threshold, a specialist crafts a single-turn close pitch with a specific next-step ask. Fires once per call. Soft fallback to classic logic on timeout.

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