The product, screen by screen
Built from scratch for high-volume BPO floors handling Taglish and English calls. Everything below is the live dashboard running on demo data.
Mission control for call quality
Calls scored, average QA score, pass rate, and open compliance alerts — with week-over-week movement, a six-week trend line, and campaign-vs-campaign comparison. Filter by campaign or date range and every number follows.
The 'needs attention' panel puts open alerts and your three lowest-scoring agents one click from the evidence.
Leaderboard and criteria heatmap
Every agent ranked by average score with trend arrows, call counts, and auto-fails. Below it, the heatmap: agents × scorecard criteria, pass rates coloured so weaknesses jump out before they become client complaints.
Team leads coach where it's red instead of guessing from a 2% sample.
Coaching pages that quote the call
One page per agent: score history, their three weakest criteria, and — this is the part QA managers love — the exact transcript lines that earned each fail, linked to the full call.
A 1-on-1 stops being 'I feel like your empathy slipped' and becomes 'here's the call from Tuesday, here's the line.'
Compliance alerts with a paper trail
Auto-fail calls — missed identity verification, missing disclosures — raise critical alerts the moment they're scored. Resolve each one with a note; the trail stays for audits.
The alert fires the day it happens, not three weeks later when sampling stumbles across it.
Every score carries its receipts
Drill into any call: the full transcript, every criterion's pass/fail, and the evidence quote behind each judgment. Your analysts stay in charge — they review flagged calls and can override any score.
Evidence, not vibes. That's the whole product philosophy.
Test the intelligence in action
Switch scenarios below to see how exact evidence quotes are matched.
See how the AI evaluates Taglish calls with quoted evidence
Customer frustrated over delayed internet repair. Agent responds with authentic Taglish reassurance.
Agent recognized customer agitation, validated the frustration using natural Taglish empathy ('Naiintindihan ko po ma'am, pasensya na sa abala'), expedited the technician dispatch, and gave a specific ticket reference.
Scorecard Criteria & Receipts
Click a criterion to spot the transcript quote“"Naiintindihan ko po ma'am, pasensya na sa abala... i-prioritize ko po ito ngayon." (Natural Taglish empathy acknowledged)”
“"May I confirm the account name and registered billing address po?" (Full 2-point DPA check)”
“"Na-escalate ko na po sa dispatch team with ticket #PH-88421. Darating ang tech before 2 PM bukas."”
“"Salamat po sa pagtawag sa SkyFiber, have a great afternoon!"”
Verified Transcript (Taglish / English)
Audio Stayed On-PremisesThank you for calling SkyFiber Support, my name is Mark. How may I assist you today?
Hello Mark, pangatlong araw na walang internet sa bahay namin! Kailangan ko mag-work from home!
Before we check the line status po, may I confirm the account name and registered billing address?
Maria Santos po, 142 Acacia St., San Lorenzo, Sta. Rosa Laguna.
Naiintindihan ko po ma'am Maria, pasensya na talaga sa abala lalo na't working from home kayo. I-prioritize ko po ito ngayon para maayos agad.
Sige please, kasi sobrang hassle na talaga.
Checked the node, may isolated LOS on your segment. Na-escalate ko na po sa dispatch team with ticket #PH-88421. Darating ang tech before 2 PM bukas.
Ayun, salamat Mark. At least may exact time kayo na binigay.
Walang anuman po ma'am Maria. Salamat po sa pagtawag sa SkyFiber, have a great afternoon!
How BPO AI-QA compares
Why mid-market Philippine BPOs choose on-premises AI QA over US enterprise tools.
| Capability | Manual QA (1-2% Sampling) | US Cloud QA (Observe.AI / CallMiner) | BPO AI-QA |
|---|---|---|---|
| Call Coverage | 1–2% random sample | 100% in cloud | 100% on-premises |
| Taglish Code-Switching | Native (Human) | Chokes / Low Accuracy | Natively tuned for Taglish |
| Audio Storage & Privacy | Local SAN / PBX | Uploaded to US cloud servers | Zero audio leaves premises |
| Price per Seat / Month | Analyst labor costs | $80 – $150 / seat | $15 / seat (~1/5 cost) |
| Evidence Validation | Subjective analyst notes | Keyword spotting & scores | Quoted transcript receipts for every score |
Fifteen minutes, your own eyes
We'll walk you through the dashboard on live demo data — then talk about auditing 1,000 of your own calls, free.