From 50% to 80% call quality.

The opportunity

A small sample of calls, reviewed too late to make a difference.

Taurus BPO runs large-scale sales and service operations for their BFSI customers. Quality assurance on one line followed a familiar pattern: manual reviews of a small sample, delayed scoring, and coaching that was too broad, subjective, and hard to measure.

The operation had scale. It did not yet have a learning system.

At baseline:

• Call quality averaged 50%, based on 4,50,000 calls reviewed in the quarter
• New agents produced ₹0.36L in their first 30 days
• Experienced agents produced ₹3.95L with over a year’s tenure

Four patterns stood out:

• Backward-looking QA

  A small sample reviewed manually, with scores arriving too late to act on.

• No learning loop into action

  Insights from calls rarely translated into immediate agent improvement.

• Repeated sales behaviours

  Defensive openers, context-free profiling, and feature-led pitches.

• Uneven productivity across tenure

  The same coaching approach struggled to work for both new and experienced agents.

The impact

Every relevant conversation scored against the client's own checks, and every finding turned into a coaching action.

We helped design and build QA Intelligence: a platform that evaluates every relevant conversation against parameters the client defines, surfaces the precise moment, behaviour or risk that needs attention, and turns that evidence into targeted coaching, recovery and control actions. This replaces sampled QA with a continuous learning loop.

• 5,85,000

  Audits a quarter, scored the same way

  Evaluator subjectivity removed, so the quality signal is dependable and comparable across agents and months, at the latest reported quarter's volume.

• 100%

  Full-data quality scoring

  Sampled reviews replaced with scoring across the whole conversation set.

• Client-defined parameters and summary

  Every call is evaluated against the client’s standards, with one clear outcome.

• A complete feedback loop

  Findings move directly into agent actions and training inputs.

• Compliance and risk monitoring

  Issues surface early enough for teams to prioritise and act.

The operating loop

How each cycle runs.

The system connects conversation data, QA, and training. Each review feeds training. Each training action is measured in the next cycle. That rhythm made the improvement durable.

Ingest calls

Bring all relevant conversations into a single review workflow

Apply client standards

Score every call against consistent, defined parameters

Find root causes

Surface the exact behaviour, moment, or risk behind outcomes

Assign coaching

Turn findings into agent-level actions and training inputs

Re-score outcomes

Measure whether quality and productivity moved in the next cycle

The result

Quality improved significantly followed by productivity.

Call quality improved from 50% to 80.1% over three quarters, while audit coverage increased from 4,50,000 to 5,85,000 calls per quarter.

The system improved performance while scaling, not after.

Agent productivity moved with it:

• New agents: ₹0.36L to ₹0.62L (72% increase)
• Experienced agents: ₹3.95L to ₹5.90L (49% increase)

Scoring the calls also made them a source of product insight. Three kinds of signal became specific actions for the sales pipeline, and when a customer's interest pointed to a different product, the lead was reallocated to the right line of business instead of being discarded.

1. 1

   Competition mapping: which alternatives customers raised, and at what point in the call, so agents get timely counter-positioning

2. 2

   Product insights: recurring objections and drop-offs fed into pitch and product changes.

3. 3

   Consumer behaviour: what made leads progress, stall, or drop, used for follow-up and prioritisation.