Deep Expertise Across Contact Center Technology, Analytics and AI
Six connected practice areas. Each one is described the same way: what it covers, the problems it typically solves, what you actually receive, and the business outcome it supports.
01 Contact Center Analytics Turning raw interaction and campaign data into measures that leaders can act on with confidence.
Defining and governing the KPI layer for a contact center: which measures exist, how each is calculated, which source of truth it comes from, and who owns it. Extraction and shaping of call, disposition, agent, queue and campaign data from Five9 or other CCaaS platforms into an analysis-ready form, and the recurring analysis that sits on top of it.
Typical problems
- Two reports show different numbers for the same day
- Performance moves and nobody can explain the driver
- KPIs are defined differently by operations and finance
- Campaign and agent analysis is rebuilt manually each week
- Service-level breaches are found after the fact
Typical deliverables
- KPI dictionary with formulas and owners
- Extraction and refresh design for platform data
- Campaign, agent and disposition analysis models
- SLA and service-level monitoring views
- Call-flow and routing analysis
Business outcome
One agreed set of numbers that operations, finance and leadership all work from — so discussions move from arguing about the data to deciding what to do about it.
02 Business Intelligence Power BI built as a product, not a pile of reports: modeled, governed, fast and maintainable.
Semantic model design (star schema, relationships, role-playing dates, row-level security), a disciplined DAX measure layer, refresh and incremental-refresh strategy, workspace and deployment-pipeline structure, and dashboard design aimed at a specific decision rather than a generic set of visuals.
Typical problems
- Reports take minutes to render or time out on refresh
- Measures are duplicated across dozens of reports
- Model is a flat table with calculated columns everywhere
- Executives cannot get an answer without asking an analyst
- No separation between development and production
Typical deliverables
- Documented star-schema semantic model
- Reusable DAX measure library with naming standards
- Executive, operational and diagnostic report pages
- Refresh, incremental-refresh and gateway design
- Performance optimization report with before/after timings
Business outcome
Dashboards people actually open, that load quickly, survive change, and answer the next question without a new development request.
03 Data & Analytics Engineering The plumbing beneath the dashboard: reliable extraction, transformation, history and data quality.
Designing how contact center and telephony data moves from source systems into an analytical store: API and export-based extraction, incremental loading, deduplication, timezone and call-leg normalization, slowly changing dimensions for agents and campaigns, and automated data-quality checks that fail loudly instead of quietly producing wrong numbers.
Typical problems
- Daily extracts fail silently and nobody notices for a week
- Duplicate call records inflate volume metrics
- Timezones are inconsistent between systems
- Historical agent or campaign attributes are overwritten
- Analysis is limited by how far back the platform retains data
Typical deliverables
- Extraction and pipeline architecture
- Dimensional data model with retained history
- Data-quality rule set and monitoring
- Reconciliation logic against platform reporting
- Runbook and failure-handling documentation
Business outcome
A data foundation you can trust, retain beyond platform limits, and build on — instead of rebuilding reporting every time a source system changes.
04 Telephony & Platform Engineering Hands-on configuration, optimization and integration across CCaaS and telephony infrastructure.
Five9 VCC administration and campaign configuration, IVR and ACD design, dialer pacing and list strategy, Twilio and SIP integration work, API and webhook development for two-way data flow, and structured diagnosis of call quality, routing and connectivity problems.
Typical problems
- Connect rates drop and the cause is unclear
- Dialer pacing creates abandonment or idle agents
- IVR paths are undocumented and no longer match the business
- CRM and dialer data drift out of sync
- Call quality complaints cannot be traced to a cause
Typical deliverables
- Platform configuration review and change plan
- IVR / ACD flow documentation and redesign
- Dialer pacing and list-strategy recommendations
- API and webhook integrations with error handling
- Diagnostic methodology for recurring telephony issues
Business outcome
A platform configured deliberately rather than historically — with the instrumentation to prove what changed and what it did to performance.
05 AI & Automation AI applied where it removes real operational work — not as a product layer on top of unsolved data problems.
Automating the recurring analytical and operational work a contact center does by hand: scheduled report generation and distribution, transcript and call analysis, AI-assisted disposition classification and QA sampling, alerting on operational thresholds, and orchestration of the data pipelines that feed all of it.
Typical problems
- Analysts spend most of the week producing recurring reports
- Only a small sample of calls is ever reviewed
- Agent-entered dispositions are inconsistent
- Operational issues are discovered a day late
- Manual handoffs between systems cause errors
Typical deliverables
- Automated reporting and distribution workflows
- Call and transcript analysis pipeline
- AI-assisted dispositioning with human review design
- Threshold-based operational alerting
- Documented automation with monitoring and fallbacks
Business outcome
Analyst time moves from producing reports to interpreting them, and coverage of calls and quality checks expands well beyond what manual review can reach.
06 Strategic Advisory Decision support for the choices that are expensive to reverse: architecture, tooling and roadmap.
Independent assessment of the current analytics and contact center technology estate, a sequenced roadmap tied to business priorities, vendor and tooling evaluation against your actual requirements, build-versus-buy analysis, and documentation and knowledge transfer so capability stays in the organization.
Typical problems
- Competing tooling proposals with no objective comparison
- Analytics investment with no clear sequencing
- Internal team lacks a specialist to pressure-test decisions
- Knowledge sits with one person and is undocumented
- Platform migration with unclear analytics impact
Typical deliverables
- Current-state assessment with prioritized gaps
- Sequenced analytics roadmap
- Vendor evaluation matrix and recommendation
- Architecture and decision documentation
- Knowledge-transfer sessions with the internal team
Business outcome
Technology and analytics decisions made on evidence, with a defensible rationale you can take to a budget conversation.
Which of these is your current problem?
Tell us what you’re trying to solve. We’ll help identify the data, technology and analytics required to move forward.