Cloud Contact Center
on Amazon Connect

What we deliver

• Channel-by-channel volume profile — voice, chat, email, social, with peak / off-peak shape
• Top intents and resolution paths — what customers contact about, where calls drop or transfer
• AHT / FCR / CSAT / NPS baseline — the numbers you'll measure migration success against
• IVR / flow library audit — which trees deflect, which frustrate, which can become AI
• Integration inventory — CRM, telephony providers, knowledge base, identity, ticketing
• Target Connect design — flows, queues, routing, channels, AI scope, agent UI
• Pay-per-use forecast — monthly Connect bill modelled against your actual volume

How we do it

• Read-only access to existing reporting — no production touch
• Interviews with 4–6 stakeholders — ops, supervisors, IT, CX leadership
• Output in Confluence so the audit lives where your team already works
• Single named lead, weekly checkpoint, fixed deliverables

Outputs go straight into

• Confluence — current-state CX audit and target Connect design, living documents
• Jira — backlog of build epics with sized estimates
• Executive summary deck — for the steering committee, not the engineers

What gets built

• Connect instance(s) — single or multi-region depending on RTO and geography
• Telephony — direct DIDs, toll-free numbers, BYOC where existing contracts must stay
• Queues, routing profiles, hours of operation, holiday calendars
• IAM — agents, supervisors, admins, with least-privilege roles and SSO via Identity Center
• KMS-encrypted call recording, S3 archive with lifecycle policy
• CloudWatch baseline dashboards and alerts — wired before traffic, not after the first outage
• Encryption, redaction, retention policies aligned with your compliance posture

How it differs from "just spin up Connect"

• IaC from day one — CloudFormation / Terraform, so the next instance is faster
• Naming conventions and tag standard so the bill is attributable per business unit
• Disaster recovery scenarios documented and tested before go-live, not after
• Audit trail wired from CloudTrail to your SIEM — not a follow-up project

What we deliver

• Number porting — direct DIDs, toll-free, vanity numbers, with carrier coordination
• Contact flows rebuilt as Connect blocks — versioned, testable, peer-reviewed
• Amazon Lex bots for natural-language menus — "what do you need today?" instead of "press 1"
• Whisper flows, agent screen pop, after-call work hooks
• Pilot queue plan — small business unit runs Connect in parallel, real customers, real measurements
• Cutover ceremony with named owner, rollback path, and explicit go / no-go criteria

What we explicitly will not do

• Big-bang cutover on a Friday — pilot or no migration
• Lift-and-shift 87 IVR levels — most of them deflect nothing; we redesign while we move
• Replace human agents with bots — that's a self-service decision (Capability 05), not a migration decision

Channels we light up

• Web chat — embedded in your site or app, persistent across sessions
• In-app chat for mobile (iOS / Android)
• Email — full thread context, attachments, signatures, auto-reply discipline
• SMS — two-way, MMS-capable, with opt-in / opt-out compliance
• WhatsApp Business and Apple Messages for Business — where customers actually live
• Voice — same routing rules, persistent customer history across channels

What "unified" actually means

• One queue model — chat and voice in the same queue if your ops want them that way
• One agent UI — no tab switching, no missed handovers
• One reporting surface — SLA, AHT, CSAT consistent across channels
• Customer history persists across channels — start in chat, finish on voice, no repeat

What we build

• Amazon Q in Connect — knowledge-grounded answers for agents and self-service flows
• Lex bots — slot filling, intent disambiguation, multi-turn conversation
• Knowledge sources — Confluence, SharePoint, S3 docs, FAQ, product catalog
• CRM and order-system actions — check status, reschedule, cancel, refund (with policy guardrails)
• Voice and chat — same backend, same intents, channel-appropriate responses
• Warm transfer to a human — bot summarizes intent + history, agent gets the screen-pop
• Evaluation loop — every conversation scored, weak intents flagged for tuning

What "agentic" actually means here

• The bot can take action, not just retrieve a knowledge article
• It knows when it doesn't know — and escalates instead of guessing
• It carries context across turns — no "as you said earlier" hallucinations

What gets analyzed automatically

• Full transcripts — voice (multi-language) and chat, with speaker separation
• Sentiment per turn and overall trajectory — does the call get better or worse?
• Theme detection — what topics drive call volume this week vs last
• Compliance markers — script adherence, mini-Miranda, disclosure phrases
• PII detection and redaction — automatic, before recordings hit storage
• Auto quality scoring against your rubric — supervisor reviews exceptions, not random samples

Real-time signals (during the live call)

• Sentiment plunge → supervisor barge-in suggestion
• Compliance phrase missed → live nudge to the agent screen
• Repeat-caller detected → agent sees prior contact history

What we integrate

• Salesforce, Zendesk, Dynamics 365, HubSpot, ServiceNow — bi-directional sync
• Order systems — last 5 orders, status, returns, eligibility
• Billing — current balance, payment method, last statement, dispute state
• Loyalty / membership tier — automatic VIP routing where applicable
• Identity / authentication — Cognito or your IdP, with secure verification flows
• Past-contact history across channels — voice, chat, email, SMS — single timeline

What the agent actually sees

• Customer name, tier, lifetime value — top of the screen, no clicking
• Reason the system thinks they're calling (recent order issue, billing event, churn risk)
• Last 3 interactions across all channels — what was discussed, by whom, what was promised
• One-click actions — refund up to limit, escalate, schedule callback

What lives in one screen

• Customer Profile — name, tier, history, sentiment trajectory
• Active contact — channel, queue, wait time, call recording state
• Step-by-Step Guide — branching workflow for refund, claim, escalation, address change
• Knowledge from Amazon Q — surfaces answers grounded in your own docs
• Real-time agent assist — suggestions, compliance reminders, sentiment alerts
• Case / ticket form — fields pre-filled from contact context
• One-click after-call work — outcome, disposition, follow-up scheduled

Where Step-by-Step Guides earn their keep

• Refund eligibility flows with policy enforcement built in — no more "let me check with my supervisor"
• Complaint intake with required disclosures and routing decisions
• Claims / disputes with branching by scenario, evidence requirements baked in
• New-agent training shortcut — the guide is the training material

What Day-2 actually involves

• Contact flow change management — versioning, peer review, staged rollout, rollback
• Telephony health — SIP quality, ASR/MOS scores, carrier issues escalated proactively
• Fraud monitoring — toll fraud, robocall floods, suspicious geo patterns
• Queue and routing tuning as volume and channel mix evolve
• AI / Q knowledge refresh — content updates flow into the bot, not into a forgotten doc
• FinOps governance — monthly bill review, anomaly flag, action list, owner per item
• Named SDM, monthly review, quarterly business review with real numbers

Self-managed or MSP — your choice

• Self-managed — we hand over runbooks + training, you operate, we stay on standby
• MSP — Triangu Partner-Led Support takes over 24/7 NOC, you keep oversight via dashboard
• Switchable mid-flight — start MSP, move in-house as your team grows; or vice versa

Live KPIs (Tuesday 14:42 — current shift)

Activity timeline (last 30 min)

• 14:42 — Spike on billing queue (+18 chats in 4 min) — AI deflection rate held at 41%, no overflow
• 14:35 — Sentiment plunge alert on voice call #C-8821 — supervisor barged in, recovered
• 14:28 — WhatsApp campaign batch 3 of 5 sent — 1,200 outbound, opt-out rate 0.4%
• 14:14 — Q in Connect deflected 87 contacts on "where is my order" — handoff rate 11%
• 14:02 — Agent #A-117 flagged for low FCR last 4 contacts — coaching task auto-created
• 13:51 — Toll-free DID +1-800-XXX-XXXX dropped ASR briefly (carrier issue) — auto-resolved

What you don't see today (and will)

• Sentiment heatmap across queues — where customer frustration is rising right now
• AI deflection rate per intent — which bot flows resolve, which collapse to human
• FinOps live — minute-by-minute Connect spend vs daily forecast
• Compliance flags — script adherence, missed disclosures, surfaced in real time
• Coaching backlog — supervisors see who needs the next 1:1, not random sampling

Outbound Campaigns — what we set up

• Multi-channel — voice, SMS, email, WhatsApp — one campaign, blended cadence
• Preview / progressive / predictive dialing modes per campaign and per list segment
• Compliance guardrails — DNC list, time-of-day rules, opt-out handling, TCPA / consent tracking
• List management — CRM sync, dedupe, suppression, retry policy
• Live KPIs — connect rate, conversion, agent utilization, cost per outcome

Workforce — Forecasting / Capacity / Scheduling

• AI forecasting from your own historical volume — short-term and long-term
• Capacity planning — FTE targets per service-level scenario, budget modeling
• AI-generated schedules aligned with demand peaks, agent skills, channel mix
• Real-time schedule adjustments when volume drifts from forecast
• Agent flexibility — shift swaps, time-off requests, self-service inside the same workspace