Production system · High-volume environment

Routing systemTelecom operator

AI Routing Engine

Reduced unnecessary human-handled calls by 180k/month by redesigning the decision point where routing fails.

This system replaced a legacy IVR handling 12M+ calls/year across 120+ intent categories.

Most of the wasted cost came from routing failures, not call volume.

25% misrouting → <7% with 180k fewer human calls/month

180k

fewer human calls/month

<7%

misrouting (was 25%)

~$3.2M

estimated annual savings

Measured across 12M+ calls/year with 120+ intent categories

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Before

✕~25% of calls misrouted
✕~250k calls/month transferred to humans
✕Compounding cost from repeat contacts

After

✓<7% misrouting rate
✓~70k human calls/month
✓~$3.2M annual savings

Context

Large telecom operator handling 12M+ calls/year across 120+ caller intents, with significant cost pressure on human-handled interactions.

Problem

Every misrouted call multiplied cost and degraded service quality.

At $1–5 per human interaction, 180k unnecessary transfers/month represented millions in avoidable cost.

▸The existing IVR wrongly routed ~25% of calls — creating unnecessary transfers and repeat contacts.
▸Many users requested a human agent immediately, bypassing classification entirely.
▸This generated unnecessary cost and unsustainable load on support teams.
▸Misrouting compounded: one wrong transfer often triggered a second.

Constraint

This was not solvable with a simple automation layer.

▸The system had to make better routing decisions without increasing interaction time.
▸High scale (12M+ calls/year) and low caller patience made conversational design critical.
▸120+ intent categories required precision, not just classification.

What we built

A real-time routing system that classifies intent accurately, engages callers conversationally before transfer, clarifies needs, and routes to the correct resolution path — all within 28 seconds average.

Key insight — what actually made this work

The failure was not in classification. It was in losing control at the exact moment callers requested a human. We redesigned that decision point conversationally and recovered the routing logic.

Results

Measured against the previous IVR routing system:

✓Misrouting reduced from ~25% to <7% across all intent categories
✓Human-bound calls reduced from ~250k/month to ~70k/month
✓~180k fewer unnecessary human interactions per month
✓Average interaction kept under 28 seconds — no increase in call duration
✓Estimated savings: ~$270k/month (~$3.2M/year) at ~$1.5 per human-handled call

System runs in production handling the full call volume across all intent categories.

Business impact

This removed the operational constraint driving support cost:

▸Reduced operational cost by ~$3.2M/year without reducing service coverage.
▸Removed unsustainable load from call center teams.
▸Increased routing precision at massive scale — fewer escalations, fewer repeat contacts.

What happens if this is not fixed

▸~$270k/month in avoidable human call cost.
▸Unsustainable load on support teams compounding with growth.
▸Customer experience degraded by repeat contacts and wrong transfers.

Why this matters

—Misrouting is not a UX problem — it is an operational cost multiplier.
—Every unnecessary human interaction costs $1–5 and compounds at scale.
—Legacy IVR systems were not designed for the complexity of modern intent classification.

Why this approach works where others fail

✓Works without replacing existing telephony infrastructure.
✓Handles 120+ intents with conversational disambiguation, not rigid menus.
✓Designed for the moment callers try to bypass the system.
✓Built for massive scale — 12M+ calls/year with sub-second decisions.

Where this pattern applies

If your operation handles high-volume inbound support, this pattern applies.

Relevant to telecom, banking, healthcare, insurance, logistics, and any operation with high-volume inbound support flows.

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