Applied AI · SaaS

Scaling support with AI automation

How Inteople built and operates ReplyFlow — an AI reply and workflow engine that resolves the routine, elevates human agents, and keeps people in control of every automated answer.

OmnichannelUnified inbox
RAG-groundedAccurate replies
Human-in-loopConfidence-gated
Model-agnosticBest model per task
DomainApplied AI
RoleProduct owner & engineering
DisciplinesAI · Software · Cloud
StatusLive & operated
The problem

Conversation volume outpaces teams

Support and sales teams face more messages, across more channels, at all hours — and most are the same handful of questions. Hiring linearly to keep up is unsustainable, yet generic chatbots erode trust with vague, off-brand answers customers don't believe.

  • Repetitive questions consume skilled agents' time
  • Fragmented channels mean conversations get lost
  • Off-the-shelf bots give inaccurate, unbranded replies
  • After-hours coverage is costly or simply absent
Our solution

Automate the routine, escalate the rest

ReplyFlow reads each conversation in context, drafts a reply grounded in the organization's own knowledge, and either sends it or routes it to a human — governed by a confidence threshold the team controls. Agents get AI assist; customers get fast, accurate answers.

  • RAG grounding keeps answers accurate and on-brand
  • Confidence gating decides auto-send vs. human review
  • One omnichannel inbox unifies every conversation
  • Analytics expose resolution rate, time and automation share
Technology used

The stack behind ReplyFlow

AI core

LLMs (multi)RAGmodel routingguardrails

Retrieval

pgvectorembeddingsreranking

Backend

Node.jsPostgreSQLRedisqueues

Frontend

ReactTypeScriptrealtime

Integrations

emailchatsocialwebhooks / API

Cloud & eval

multi-tenantautoscalingeval harnessobservability
Implementation

How we built it

PHASE 01 · Grounding

Knowledge ingestion & retrieval

We built the retrieval layer first — ingesting docs, FAQs and past conversations so every generated reply has a verifiable source.

PHASE 02 · Generation

Model-agnostic reply engine

A routing layer selects the right model per task, balancing quality, latency and cost, with structured outputs and citations.

PHASE 03 · Control

Confidence gating & workflows

We added confidence scoring and rules so teams decide what auto-sends and what a human reviews — automation never runs unchecked.

PHASE 04 · Channels

Omnichannel unification

Connectors brought email, chat, social and messaging into one inbox and workflow.

PHASE 05 · Evaluate & operate

Continuous evaluation

An eval harness with golden datasets gates quality on every change; the platform runs multi-tenant in production under our operation.

Visuals

The product in use

aireply.com/workspace
ReplyFlow AI workspace
Agent workspace — AI-drafted replies with source grounding and confidence.
Inbox
Auto-resolved
68% today
AI
You
AI

Mobile view shown as an interface mock-up.

Impact

What it changed

ReplyFlow shifts the economics of customer communication: AI carries the high-volume, low-complexity load while humans focus on the conversations that need judgment. Service stays fast and on-brand, around the clock, without scaling headcount in lockstep with volume.

Because every reply is grounded and every automation is gated by confidence, quality is measurable and controllable — teams can dial automation up as trust in the system grows.

60%+Routine load automatable
SecondsFirst-response time
24/7Coverage
100%Replies grounded & logged

Metrics are illustrative of typical automation potential; figures vary by deployment and will be replaced with published results.

The goal was never to remove people from support — it was to stop wasting them on the questions a machine should answer, and let them own the ones that matter.

SM Mohammad Ali · Founder & CTO, Inteople
ReplyFlow

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