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Solution · A brand-native AI that actually helps

A concierge that knows your product, your policies, and your customer.

Grounded on your content, personalised per user, and always escalating to a human when it should.

3s
median answer
Grounded + cited
38%
resolution rate
Without human handoff
Personal
context
Per user & account
Escalation
with memory
Human picks up seamlessly
01 · The problem

Where teams get stuck without this.

These are the recurring patterns we hear on discovery calls — and the ones this solution is built to end.

01
Generic chatbots

Feel bolted-on, sound corporate.

02
No context

Doesn't know who's asking or what plan they're on.

03
Bad handoffs

Users repeat themselves to the human.

04
No memory

Every session starts from zero.

02 · What we ship

Every part built to production standard.

RAG pipeline

Grounded on your docs, product data, and CRM.

Personalisation

Aware of plan, role, region, history.

Multi-channel

Web, WhatsApp, in-app, email.

Escalation with memory

Human gets the transcript + context.

Guardrails

Scope, PII, prompt-injection defence.

Analytics

What it answered, what it deflected, what it missed.

Built onOpenAI / AnthropicSupabase pgvectorNext.jsWhatsApp APIIntercom / Zendesk
03 · The engagement

How we build it with you.

  1. Step 1
    Content & data audit

    What can it ground on today?

  2. Step 2
    Persona

    Voice, scope, and escalation rules.

  3. Step 3
    Build

    RAG, personalisation, channels, handoff.

  4. Step 4
    Silent launch

    Behind a flag, measured quietly.

  5. Step 5
    Roll out

    By segment with weekly reviews.

04 · Proof
38% deflection with 94% CSAT
Fintech, 200k users
"It feels like a great support rep, not a chatbot."
— VP CX
05 · FAQ

Questions we hear.

Not seeing yours? Ask us directly →

Only answers from grounded content with citations; anything else escalates.

06 · Deep dive

What to know before you buy.

RAG evaluation framework

Measure if your AI actually knows what it says.

Retrieval quality determines answer quality. Evaluate both separately.

Retrieval accuracy

For a sample of real questions, did the top retrieved chunks contain the answer?

Answer faithfulness

Does the generated answer stay grounded in the retrieved chunks without adding invented facts?

Citation quality

Can a human verify the answer by following the cited source links?

Escalation precision

Does the assistant admit uncertainty and escalate when the answer is not in the knowledge base?

LLM guardrails

Keep the assistant helpful, safe, and on-brand.

Without guardrails, an AI concierge can leak data, give wrong advice, or go off-topic.

Scope boundaries

Define what the assistant can and cannot answer. Politely decline out-of-scope requests.

PII redaction

Detect and remove personal data before it reaches the model or logs.

Prompt injection defence

Filter instructions hidden inside user messages that try to override system behaviour.

Human escalation

Route sensitive, emotional, or high-stakes topics to a person with full context.

Let's scope your ai concierge / assistant.

30-minute discovery call. No decks, no pitch — just a working plan for what to build and how it pays back.