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Solution

AI Call Center Automation for Operations-Heavy Businesses

Routine calls handled by AI agents on your current phone system, complex calls handed to people with the context already gathered. Built, integrated, and operated by DPI, with containment, transfer, and outcome numbers you can inspect.

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Who This Is For

Built for teams drowning in repeat calls

  • Support and service desks where most calls are status checks, scheduling, password and account questions, or simple changes.
  • Businesses running an in-house call center or an outsourced answering service and paying for volume that follows a script.
  • Operations teams with peaks that wreck service levels: storm season for utilities, month-end for finance, campaign days for retail and logistics.
  • Companies with a PBX or contact center platform they intend to keep, who want AI added to it rather than a rip-and-replace.

What the Solution Includes

From first ring to closed ticket

Intent recognition and routing

The agent identifies why the customer is calling in the first sentences and either handles it or routes to the right queue with a summary.

Self-service resolution

Order status, appointment changes, balance and payment questions, address updates, document requests, and other scripted interactions completed end to end.

Authentication

Caller verification against your records before any account data is read or changed, with step-up to a human for sensitive actions.

Warm transfer with context

When a person is needed, the call transfers with the transcript summary, the customer record, and the attempted actions, so nobody repeats themselves.

After-hours and overflow

The agent takes calls when queues are full or the office is closed, resolves what it can, and schedules callbacks for the rest.

Quality and reporting

Containment rate, transfer reasons, handle time, outcomes, and transcript reviews in one dashboard, plus alerts when quality drifts.

What changes when AI takes the routine calls

Most inbound call volume follows a handful of scripts: where is my order, can I move my appointment, what is my balance, I need to update my address. Human agents spend the day on those while complex, revenue-relevant calls wait in the queue. AI call center automation flips that: the agent resolves the scripted calls in full, and people get the calls that need judgment, with the context already collected.

The result is measured in service levels and staff time: fewer abandoned calls in peaks, coverage after hours without a night shift, shorter handle times for the calls that still reach people, and a cost per handled call that does not scale linearly with volume.

Designed around your telephony, not a vendor's

DPI runs voice on an Asterisk-based core that connects to your PBX or contact center platform over SIP. Calls you route to the agent are handled there; anything outside scope transfers back into your queues with a summary. Because we control the telephony layer, hold, transfer, voicemail, recording, and reporting behave the way your operations team already expects.

Speech-to-text and text-to-speech can run on open-source models inside your environment, and language models are chosen per workload: OpenAI, Anthropic Claude, AWS Bedrock, or self-hosted models such as Qwen when audio and transcripts must stay under your control. For healthcare call centers we sign a BAA and design to HIPAA requirements from the first day.

Rolling out without risking service levels

We never switch a whole call center to AI on day one. The pilot takes one intent or one time window on live traffic while your team reviews transcripts and we tune. Each additional intent goes live only when it meets the quality threshold you set. Human fallback is preserved on every path, so the worst case for a caller is a transfer, never a dead end. Which intent to start with, and how to scale from it, is covered in our guide to call center automation and what to automate first.

What you get every month

A report with containment, transfer reasons, handle times, outcomes, and the transcript reviews behind them, plus the list of improvements shipped and the next intents queued. Call center automation is not a project with an end date; it is an operation, and we run it that way.

How It Works

How a call center deployment runs

  1. Call mix analysis

    We classify a sample of real calls by intent, volume, and resolution path, and pick the intents worth automating first.

  2. Integration and escalation design

    System lookups and actions, authentication rules, transfer targets, hours, languages, and the exact conditions for hand-off.

  3. Pilot on a slice of traffic

    The agent takes one intent or one time window on your live lines while your team reviews transcripts and we tune.

  4. Scale by intent

    Additional call types are added as each meets its quality threshold, with human fallback preserved at every step.

  5. Managed operation

    Monthly reviews, model and prompt updates, new intents, and reporting against the service-level and cost targets set at the start.

Stack & Integrations

Works with your contact center stack

SIP trunks and IP PBX systems Asterisk telephony core Existing IVR and queues CRM and ticketing systems Order and billing systems Google Calendar WhatsApp Business API Mailgun email OpenAI, Anthropic Claude, AWS Bedrock Self-hosted open-source speech and language models PostgreSQL, Redis

Industries

Call centers we automate

Financial Services

Balance and payment questions, document reminders, verified account changes.

Energy & Utilities

Outage information, billing questions, service appointments, and peak-season overflow.

Real Estate

Tenant maintenance requests, leasing inquiries, and vendor coordination.

Engagement Models

Engagement models

Call center assessment

Two to three weeks: call mix, automation candidates, integration map, projected containment, and a rollout plan by intent.

Pilot and rollout

Fixed-scope delivery of the first intents on your live lines, then staged expansion as quality thresholds are met.

Managed AI call center

Ongoing operation with quality reviews, tuning, new intents, and monthly reporting against service levels and cost per call.

FAQ

Questions about AI call center automation

What share of calls can the AI handle?

It depends on the call mix. Status, scheduling, and simple account changes are highly automatable; complaints and complex cases are routed to people. The assessment quantifies the achievable containment for your specific calls before we commit to a number.

Will customers know they are talking to an AI?

Yes. The agent identifies itself, and callers can ask for a person at any time. Transparency keeps trust and keeps you on the right side of disclosure rules.

Do we need to replace our contact center platform?

No. The agent sits alongside your PBX or contact center platform over SIP, takes the calls you route to it, and transfers back into your queues. Nothing about your carriers or numbers changes.

How do transfers work?

Warm transfer with context: the receiving agent gets the summary, the customer record, and what was already attempted. Transfer targets, hours, and overflow rules are configured per intent.

Can it handle several languages?

Yes. Speech and language models are configured per language, and the agent can detect the caller's language or offer a choice at the start of the call.

How is quality monitored after launch?

Every call is logged with intent, outcome, and transcript. Dashboards show containment, transfers, handle times, and error patterns, and alerts fire when metrics drift. We review transcripts with your team on a set cadence.

What does it cost compared with staffing or an answering service?

Cost depends on volume, hosting choice, and integrations. Self-hosted speech and language models remove per-minute vendor fees, which is often what makes the economics work at scale. We model your numbers during the assessment.

Related

Next Step

Bring one workflow. Leave with a production plan.

Tell us where calls, tickets, documents, or approvals pile up. We map the workflow, size the impact, and propose a deployment you can measure.

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