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AI for Communication

Customers leave long before your queue reaches them.

We engineer AI for contact centres and CX teams: omnichannel routing with sentiment and intent, agent-assist with cited knowledge, grounded self-service that escalates when unsure, and QA that samples the whole volume instead of a shift.

Built on your help desk, your knowledge base, and the compliance posture your legal team already enforces.

Discuss your CX workflowSee how the system works

The operational reality

A problem starts on chat. Moves to email. Ends on voice.

Three agents. Two tiers. One frustrated customer.

Resolve it once. Or lose them once.

Felt by VPs of customer support, heads of CX, directors of support operations, and the QA leads who absorb what thin routing and thin agent-assist leave behind.

  1. 01

    Contact arrives across a channel

    The customer reaches out via chat, email, voice, or social. Identity, context, and prior-ticket history are pulled from the CRM; routing decides queue, priority, and skill.

    Friction · Routing is rules-only; the first message is read by a human whose shift just started.

  2. 02

    Self-service and deflection

    A bot or help centre offers an answer for the common requests. Simple cases close without an agent; complex cases queue.

    Friction · The bot answers from training, not from the current policy; wrong, confident answers damage trust faster than no bot at all.

  3. 03

    Agent handles the case

    An agent reads the customer's words, searches the knowledge base, consults policy, drafts a reply or takes an action, and resolves or escalates.

    Friction · The right knowledge-base article is two tabs away; the agent types the same reply they typed yesterday.

  4. 04

    Escalation and handoff

    Complex cases route to a specialist, a supervisor, or a back-office team. Context has to travel; the customer cannot be sent back to the start.

    Friction · Context does not travel cleanly; the specialist asks the customer to repeat everything.

  5. 05

    QA, coaching, and knowledge

    QA samples a slice of volume, scores against a rubric, and feeds coaching back to the team. Knowledge gaps surface from real conversations.

    Friction · QA reaches a sliver of volume; coaching lags; the knowledge base never catches up to what customers actually ask.

Swipe the workflow

Where the work breaks

The friction is in the places nobody budgets for.

  • 01

    The queue is longer than the attention span of the customer waiting in it.

    How we fix it · A system we build catches this before it costs you.

    Impact · Simple cases abandon in minutes; the customer calls back as a complaint, not an inquiry.

  • 02

    The self-service bot answers from training, not from the current policy.

    How we fix it · A system we build catches this before it costs you.

    Impact · Wrong, confident answers reach customers; trust in the channel takes months to rebuild.

  • 03

    Agents retype the same answer every day because the knowledge base is one tab too deep.

    How we fix it · A system we build catches this before it costs you.

    Impact · Handle time stays high; the strongest answers never compound across the team.

  • 04

    Context does not travel through an escalation.

    How we fix it · A system we build catches this before it costs you.

    Impact · The customer repeats the problem to each new agent; CSAT dies in the handoff.

  • 05

    QA sees two percent of volume; the ninety-eight percent is invisible.

    How we fix it · A system we build catches this before it costs you.

    Impact · Coaching lands on cases QA happened to see; systemic patterns never surface.

What we would build

Systems, not features. Each with its own job.

  • 01

    Agent-assist with cited knowledge

    Agents retype the same answer every day; the right article is one tab away.

    Who benefits · Front-line agents on chat, email, and voice; QA and training leads.

    Inputs
    The live conversation, the customer's prior tickets and account state, and the company's own knowledge base, policies, and macros.
    What the system does
    The assistant drafts a reply in the agent's voice, retrieves the relevant knowledge-base passage with the article and section, and offers two alternate phrasings tuned to tone and length. Confidence is shown; low-confidence turns escalate rather than draft.
    Output
    A draft reply with a cited source, two alternate phrasings, and a one-click action to send, edit, or escalate.
    Benefit
    Handle time drops; the strongest answers compound across the team; every reply is grounded in a passage an auditor can open.
    Human stays here
    The agent reads, edits, and sends every reply. Nothing leaves the queue without a human on the send button.
  • 02

    Omnichannel routing with intent and sentiment

    Rules-only routing lands the hardest cases on the newest agents and the easiest on the specialists.

    Who benefits · Support ops, workforce-management leads, and agents.

    Inputs
    The first message or utterance, prior-ticket signals, sentiment and urgency cues, agent skills, and shift state.
    What the system does
    A routing model infers intent and sentiment on arrival, matches to a skill and tier, and sequences the queue so an angry churn-risk customer never waits behind a routine password reset.
    Output
    Routed contacts with the intent, suggested priority, and sentiment attached, visible to the agent before they open the case.
    Benefit
    Short waits for the cases that need a short wait; specialists see the cases that need them.
    Human stays here
    Supervisors can override any routing decision. Routing never acts on protected attributes.
  • 03

    Grounded self-service that escalates honestly

    Self-service bots guess at exactly the wrong moments and cannot show where an answer came from.

    Who benefits · Customers on web, mobile, and messaging; the agents who inherit a failed deflection.

    Inputs
    The company's current help centre, policies, product documentation, and account-state APIs.
    What the system does
    A retrieval-grounded agent answers from the live knowledge base with citations the customer can open, and escalates to a human when retrieval does not support a confident answer. Account-state actions require explicit customer confirmation.
    Output
    Resolved cases with cited sources, or a warm handoff to an agent with the conversation and context attached.
    Benefit
    Deflection where it is honest; a clean handoff where it is not. The customer never gets sent back to the start.
    Human stays here
    Low-confidence turns escalate. The agent sees everything the bot said, with citations, before they reply.
  • 04

    First-contact resolution and QA across all volume

    QA samples a slice of volume; coaching lands on cases QA happened to see.

    Who benefits · QA leads, training managers, team leads, and agents.

    Inputs
    The full conversation log across channels, the resolution outcome, the knowledge-base state, and the team's own QA rubric.
    What the system does
    The system scores every conversation against the rubric, surfaces patterns of weak handling or missing knowledge, and feeds targeted coaching cards to agents and team leads.
    Output
    A QA score per conversation with evidence, a knowledge-gap list, and a weekly coaching card per agent.
    Benefit
    Coaching lands on representative patterns, not accidents of sampling; the knowledge base closes the gaps customers actually hit.
    Human stays here
    QA leads calibrate the rubric and review flagged cases. Agent scores are never final without a human review.
  • 05

    Knowledge management from the real conversation

    The knowledge base is a day behind the product and never catches up to what customers ask.

    Who benefits · Knowledge managers, product teams, and support leaders.

    Inputs
    The questions customers actually ask across channels, the resolutions that worked, and the current knowledge-base state.
    What the system does
    The system clusters recurring questions, drafts new articles grounded in approved resolutions, and flags out-of-date passages against product and policy changes.
    Output
    Draft knowledge articles, update suggestions, and a prioritised gap list, ready for a knowledge manager to review.
    Benefit
    The knowledge base compounds with the volume, instead of being rebuilt once a quarter.
    Human stays here
    A knowledge manager approves every article and update before it is published to agents or customers.

Show the system

What it could look like in your hands.

A design concept, not a shipped product. Interfaces like this get drawn out before any code lands, so the system reads back the way the people using it work.

contact-center / agent assist

Live queue

  • CX-10492 · chat

    A. Nguyen

    Return window on order #88314

    0:12
    frustratedreturns / tier-1

  • CX-10493 · voice

    M. Patel

    Charge appears twice on statement

    0:48
    angrybilling / tier-2

  • CX-10494 · email

    R. Osei

    Replacement shipped to wrong address

    7:21
    neutralshipping / tier-1

  • CX-10495 · chat

    L. Chen

    Promo code failed at checkout

    1:04
    neutralself-service, deflected

  • CX-10496 · voice

    S. Haddad

    Cancel upcoming subscription

    0:33
    neutralretention / tier-2

A. Nguyen

CX-10492 · chat

Return window on order #88314

Conversation

  • Hi, I bought a jacket on the 24th and need to return it. The site now says the return window is closed. Can you check?
  • I was travelling, I didn't realise it was 14 days.

Assistant draft reply

86%

Thanks for the note, A. I can see order #88314 placed on the 24th. Our standard return window is 30 days from delivery, not 14, and your parcel was delivered on the 26th, so you're still well within range. I'll send a prepaid return label to the email on file now, and once the parcel reaches our warehouse the refund posts back to the original card in 3-5 business days.

Alternate phrasings

  • Shorter: You're inside the 30-day window from delivery. Label coming to your email now; refund in 3-5 business days once it arrives.
  • Warmer: Totally hear you, travel makes dates slip. Good news, you have 30 days from delivery and your parcel arrived on the 26th. Sending a return label now.

Cited knowledge

  • Returns policy, consumer · Section 2.1 Return windowp.3
  • Order lookup · Order #88314, delivery eventlive
Synthetic customers and conversation. This is a design concept of how a live queue, a focused conversation, and a cited draft reply could land in an agent's hands.

End-to-end workflow

From the trigger to the outcome.

A representative contact path, from channel arrival to a resolved case. Automated stages run the moment the message lands; human stages are explicit and never skipped.

  1. 01

    Contact arrives

    A chat, email, voice call, or social message lands. Identity and prior-ticket context are pulled from the CRM; the first utterance is parsed for intent and sentiment.

    Integration
  2. 02

    Routed with intent and sentiment

    The routing model matches the case to a skill and tier, and sequences the queue. Supervisors see every routing decision and can override.

    Automated
  3. 03

    Self-service attempt, grounded

    For deflection-eligible cases, a retrieval-grounded agent answers from the live knowledge base with citations the customer can open.

    Automated
  4. 04

    Low-confidence escalates

    If retrieval does not support a confident answer, the agent escalates to a human and attaches the conversation and context.

    Guardrail
  5. 05

    Agent-assist on the live case

    During the agent's turn, the assistant drafts a reply with cited knowledge and two alternate phrasings. Confidence is shown; the agent reads, edits, and sends.

    Automated
  6. 06

    Agent reviews and sends

    The agent reads the draft, edits as needed, and sends. Account actions require explicit customer confirmation.

    Human in loop
  7. 07

    QA scores the conversation

    The full conversation is scored against the team's rubric. Weak handling, policy drift, and knowledge gaps surface as coaching and knowledge tasks.

    Automated

How exceptions are handled

  • If retrieval confidence is low, the agent escalates rather than drafting a confident wrong answer.
  • If the customer asks for an account action (refund, cancel, address change), the agent requires explicit confirmation and logs the action with the agent ID.
  • If a call is handling payment-card data, the PCI DSS flow redacts the card details from logs and recordings.
  • If a customer invokes a data-protection right (GDPR, state privacy laws), the request is routed to the privacy workflow, not answered by the agent.

How it is engineered

Built on your stack, not ours.

Design choices that make this run on your help desk, not in a vendor silo. We build to your platform, your identity provider, and your compliance posture, not to our stack.

01

Help-desk and CCaaS compatibility

We build against Zendesk, Intercom, Salesforce Service Cloud, Genesys, and Five9 as platforms you may already run. Compatibilities, not partnerships. Integrations use the platform's own APIs and app frameworks.

02

Identity and access

Single sign-on through your existing identity provider. Agent, supervisor, QA, and admin roles are separate; agents never act under a shared service account.

03

Data boundary

Conversation data stays in your tenant or ours under a signed data-processing agreement, encrypted at rest and in transit. Model inference runs with no retention and no training on your data.

04

Retrieval

A knowledge-base index that reflects the live help centre, with provenance on every passage. Answers refuse when retrieval confidence is low, and every claim carries its source.

05

Voice

Streaming ASR for the live transcript, low-latency inference for the assistant, and TTS where the channel is fully automated. PCI-regulated turns redact the card details from logs and recordings.

06

Observability and evaluation

Every draft, every retrieval, and every QA score is logged with the model version, the inputs, and the agent edits. An evaluation harness watches drift and surfaces regressions.

What the business will measure

Numbers the operator already watches.

We agree on metrics and baselines before building. These are the numbers a CX leader usually watches when deciding whether a system is working.

  • 01

    First-contact resolution rate

    Baseline today

    Share of cases closed in the first contact today, by channel, intent, and tier.

    How it moves

    Routing lands the case with the right skill the first time; agent-assist drafts answers grounded in the current policy.

  • 02

    Customer satisfaction (CSAT)

    Baseline today

    Post-contact survey scores today, by channel and agent.

    How it moves

    Short waits for the cases that need a short wait; replies that read like the brand; honest escalation when the agent cannot help.

  • 03

    Average handle time

    Baseline today

    Median handle time today across channels, by intent and tier.

    How it moves

    The right article is in the agent's cursor, not two tabs away; drafts save the retyping.

  • 04

    Deflection rate on self-service

    Baseline today

    Share of eligible contacts closed by self-service with positive CSAT today.

    How it moves

    Grounded self-service answers from the live knowledge base and escalates honestly when it cannot.

  • 05

    Agent-assist adoption

    Baseline today

    Share of eligible agent turns where the assist draft is used as sent or edited.

    How it moves

    Drafts land in the agent's voice; alternate phrasings cover tone and length; the agent trusts the suggestion.

Human in the loop

Oversight, safety, and the data boundary

Where the humans stay in control.

The system is designed around what agents, customers, QA, and legal already expect. Nothing is autonomous that should not be.

  • 01

    PCI DSS where cards are handled

    Payment-card data is redacted from transcripts and recordings at the turn level. The assistant never sees a cardholder number in plain text; account actions requiring card data route to the PCI-approved flow.

  • 02

    HIPAA where applicable

    For health-plan and provider CX, we operate under a signed Business Associate Agreement. PHI is encrypted at rest and in transit, with minimum-necessary access and audit.

  • 03

    GDPR and state privacy posture

    Data-protection rights (access, deletion, portability) are routed into your privacy workflow, not answered by the assistant. Retention is bounded and documented per channel.

  • 04

    Consumer-protection acts

    Outbound and automated flows respect TCPA, FDCPA, and equivalent state law. Opt-outs are honoured; disclosure of automation is explicit where required.

  • 05

    Agent on the send button

    No reply or account action is final without a human agent. Drafts are drafts until the agent sends.

  • 06

    Low-confidence refusal

    When retrieval or intent confidence is below threshold, the system escalates to a human rather than drafting a confident wrong answer.

Proof

Work we have shipped, and the shape of what we build.

Real engagements where we can speak to them, and representative use cases where we cannot.

How the build runs

Scoped tightly, shipped in phases.

Fixed price per phase, approved before the phase starts. You own the code, the models, the data, and the deployment.

  1. 01

    Discovery

    Two weeks. We sit with agents on shift, map the real contact flow against your help desk and knowledge base, and agree on the metrics we will move and how we will baseline them.

  2. 02

    Prototype on real contacts

    Four to six weeks. One channel, one queue, one proof of mechanism. Agents use it in parallel to real work; we measure against the baseline.

  3. 03

    Production rollout

    Eight to twelve weeks per channel. Help-desk integrations, SSO, DPA, PCI redaction, audit trails, evaluation harness, and the oversight workflow your legal and QA teams signed off on.

  4. 04

    Handover

    You get the full source, the runbooks, the evaluation suite, and a two-week walkthrough with your platform, support-ops, and QA teams.

  5. 05

    Optional ongoing support

    A fixed-term support agreement for model-drift monitoring, knowledge-base reindexing, and platform API changes. Never a retainer dependency.

Common questions

Answers before the first call.

01Which help desks and CCaaS platforms do you integrate with?
We build against Zendesk, Intercom, Salesforce Service Cloud, Genesys, and Five9 as the platforms customers most often already run, using each platform's own APIs and app frameworks. These are compatibilities, not partnerships. For other platforms we use their public APIs.
02Where does conversation data live?
In your tenant where your help desk already sits, or in a dedicated environment we operate for you under a signed data-processing agreement. Encrypted at rest and in transit, minimum-necessary access, no retention at inference time, and no training on your data.
03How do you handle payment-card data on calls?
PCI DSS flows redact the cardholder number from the transcript and recording at the turn level. The assistant never sees a card in plain text; account actions requiring card data route to your PCI-approved flow.
04Will the self-service agent make things worse before it makes them better?
The common failure mode is a bot that answers from training, not from the current policy. Our self-service agent answers from the live knowledge base with citations, and escalates when retrieval does not support a confident answer. We measure deflection-with-positive-CSAT, not deflection alone.
05What happens when the model is wrong?
Every reply is a draft until an agent sends it. Low-confidence turns escalate rather than draft, and every automated action logs the model version and inputs so an audit can reconstruct it. We treat errors as a product metric, not an afterthought.
See the other details on the FAQ page

Next step

A thirty-minute conversation. Bring one channel or one queue that costs agent time today, and we will walk through what the first phase would look like, where the measurements come from, and what has to be true for your legal and QA teams to approve it.

Discuss your CX workflow

Helpful to have on the call

Helpful to have on the call: the help desk and CCaaS you run, one channel or queue with a handle-time, deflection, or QA pain, and the person who owns the metric you would like to move.

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