What percentage of tickets can actually be deflected?

It depends heavily on how repetitive your ticket volume is — we've seen deflection land anywhere from 25% to over 40% on genuinely FAQ-heavy support queues. We measure your ticket distribution during use-case mapping before promising a number.

What happens when the bot doesn't know the answer?

It says so and routes to a human, rather than guessing. That handoff logic is designed in from the start — a bot that occasionally says 'I don't know' is more trustworthy than one that never does.

Can it take real actions, or just answer questions?

Both, depending on scope — we build tool-calling so the assistant can look up an order, update a record, or trigger a workflow, with permission boundaries on what it's allowed to touch.

How do you keep it from leaking sensitive internal data?

Retrieval is scoped to what a given user or role is allowed to see, and we review what's in the knowledge base before it's indexed — access control matters as much here as it does in the rest of your systems.

AI Chatbots & Assistants

We design and deploy AI chatbots and copilots — customer support, internal knowledge assistants, and product-embedded agents — grounded in your own data and connected to your real systems.

Start a project See our work

42%

Inbound tickets deflected (Fernwood Retail)

98%

Client retention

24hr

Average response time

Why Avenva

What sets working with us apart

You own the code

No proprietary lock-in — the repository, infrastructure, and IP are yours from day one.

No vendor lock-in

Standard, well-documented stacks, so your next team (or ours) can extend it without a rewrite.

One team, start to finish

The same engineers scope, build, and support your project — no hand-offs between sales and delivery.

Direct access to engineers

You talk to the people writing the code, not an account manager relaying messages.

Fixed-scope, transparent pricing

Clear estimates before work starts, and no surprise change orders for work that was always in scope.

Support after launch

Launch day isn't the finish line — we stay on for monitoring, fixes, and iteration.

What's included

What we deliver

Custom knowledge assistants

Retrieval-augmented chatbots trained on your docs, tickets, and internal knowledge base — answering from what's actually true in your systems, not from general web knowledge.

Customer support automation

Deflect the repetitive tickets that eat a support team's week, while routing anything ambiguous or high-stakes to a human instead of letting the bot guess.

In-product AI copilots

Embedded assistants that take real actions inside your app through tool-calling — searching, filling forms, triggering workflows — not just answering questions in a sidebar.

Model & vendor flexibility

We architect around the provider that fits your data and budget, and structure the integration so switching models later is a config change, not a rebuild.

Is this you?

Where this actually pays off

Your support team answers the same 20 questions all day

Order status, return policy, account setup — if a handful of questions make up most of your ticket volume, that's the clearest deflection opportunity there is.

New hires spend weeks just learning where things are documented

An internal assistant grounded in your actual docs, wikis, and tickets turns 'ask around until someone knows' into an instant, sourced answer.

You want an AI feature inside your product, not a support widget

A copilot that can actually look things up or take action in your app — not a chat bubble that just talks about your product from the outside.

A generic chatbot tool gave wrong answers and lost trust

Off-the-shelf bots that aren't properly grounded tend to hallucinate policy details. Rebuilding on real retrieval against your data usually fixes exactly that failure mode.

Our approach

How we run this engagement

Use-case mapping

Identify the highest-leverage workflows to automate first.

Data & retrieval design

Structure your knowledge base for accurate, grounded answers.

Build & evaluate

Prompt/agent design with systematic evaluation before rollout.

Deploy & monitor

Ship with guardrails, logging, and continuous quality tracking.

Tech we use

Built on a proven, modern stack

Claude API

Careful, instruction-following responses for assistants that represent your brand.

OpenAI

An alternate model option evaluated case by case for cost and quality.

LangChain

Manages conversation state and multi-step tool use instead of ad hoc prompt chains.

Vector DBs

Answers grounded in your docs and data, not the model's general training.

Python

The glue layer connecting the model to your tools, data, and business logic.

Node.js

A fast, realtime-friendly backend for chat interfaces that feel instant.

Proof

Work we've shipped like this

AI Chatbot

Fernwood Retail

A retrieval-augmented support assistant that deflected 42% of inbound tickets.

AI Chatbot

Driftwood Bank

A compliance-aware banking assistant that handles balance and transaction questions without a human handoff.

“The AI assistant they built now handles nearly half our support volume, and customers genuinely prefer it to waiting on email.”

Priya Nadar

Head of CX, Fernwood Retail

Related insights

AI & Automation

AI Chatbot vs. Traditional Support Ticketing: The Real Case for RAG-Based Deflection

A generic chatbot widget hallucinating a policy detail is the failure mode that gives AI support a bad name. Grounded retrieval is the actual fix — here's the honest version.

Questions

Things people ask before starting

Can't find what you're looking for? Reach out and we'll answer directly.

Ask a question

Off-the-shelf widgets are easy to install and often answer from the model's general knowledge, not your actual policies or data — which is exactly how they end up confidently wrong. We ground every answer in retrieval against your real documents and evaluate accuracy before launch.