AI Agent & Chatbot Development
We handle AI agent development end to end: customer support agents, internal knowledge assistants, and copilots inside your product. Each one is grounded in your own data and connected to your real systems, so it can take action instead of only answering questions.
- You own the code
- Fixed price before work starts
- Reply within one business day
42%
Inbound tickets deflected (Fernwood Retail)
98%
Client retention
24hr
Average response time
AI chatbot development, from knowledge base to deployment
Not sure what you need yet?
Tell us the problem on a free call. We will suggest what to build first and what can wait.
Book a free callCustom 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, like searching, filling forms, and triggering workflows, instead of only 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.
When an AI chatbot development company is worth it
If one of these sounds familiar, that is usually the moment we get the call.
Talk it throughYour 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.
How we run this engagement
Use-case mapping
Identify the highest-impact 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.
How pricing works
Step 1
Free call
Tell us what you need. We ask questions and suggest what to build first.
Step 2
Written scope
You get every feature of the first version listed in writing.
Step 3
Fixed price
One price for that scope, agreed before any work begins.
Step 4
Build and launch
You see progress as we build, then we launch and stay on for support.
An AI chatbot or agent starts from
$700
The final price depends on the features you need. The calculator gives you a realistic range in a couple of minutes.
Included in every project
- Code and accounts in your name
- Testing before launch
- Support after launch
How working with us compares
Who you talk to
Avenva Solutions: The engineers building it
- Typical agency:
- Usually an account manager
- Freelancer:
- One developer
How you pay
Avenva Solutions: A fixed price, in writing, before work starts
- Typical agency:
- Often hourly or open ended
- Freelancer:
- Usually hourly
Who owns the code
Avenva Solutions: You, from day one
- Typical agency:
- Depends on the contract
- Freelancer:
- Depends on the person
Skills on the project
Avenva Solutions: Design, build, testing, launch, and support
- Typical agency:
- A full team
- Freelancer:
- One person's skills
After launch
Avenva Solutions: We stay on for fixes and updates
- Typical agency:
- Often a separate support contract
- Freelancer:
- Depends on their availability
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.
Work we've shipped like this
“The AI assistant they built now handles nearly half our support volume, and customers prefer it to waiting on email.”
Priya Nadar
Head of CX, Fernwood Retail
Related insights
Things people ask before starting
Can't find what you're looking for? Reach out and we'll answer directly.
How is custom AI chatbot development different from a widget I could add myself?
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.
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 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, because a bot that occasionally says 'I don't know' is more trustworthy than one that never does.
Can an AI agent 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.
What should you expect from an AI chatbot development company?
A clear list of what the bot will and won't handle, accuracy testing against your real questions before launch, a handoff path to a human, and logging so you can see every conversation. If a company can't show you how they measure wrong answers, keep looking.
