AI Chatbot Development Company

Your customers and employees already know what they want to ask. We build the AI chatbots that answer them, from your content, your data and your systems, in seconds.

Enterprise conversational AI for support deflection, internal knowledge access, lead qualification and guided onboarding. Built to answer accurately, integrate with what you already run, and hand over to a person the moment a question needs human judgment.

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Trusted By Global Clients, Including Fortune 500 Companies

AI conversations delivered in production

120,000+

AI conversations delivered in production

Languages reached through a single messaging channel

50+

Languages reached through a single messaging channel

70+

AI and ML specialists

300+

Engineering specialists

2700+

Projects delivered

1800+

Clients served

15+

Years in business

ISO 42001: 2023

ISO 42001: 2023

ISO 27001: 2022

ISO 27001: 2022

ISO 9001: 2015

ISO 9001: 2015

SOC2 Type II

SOC2 Type II

HIPAA

HIPAA

GDPR

GDPR

WHAT AN AI CHATBOT CHANGES IN YOUR BUSINESS

Your support team stops answering the same forty questions

Routine questions about orders, policies and account settings are handled instantly. Anything unusual reaches an agent with the full conversation attached.

Employees stop searching three systems for one answer

Policies, runbooks and product documentation become something people can simply ask, filtered by what each person is allowed to see.

Customers get an answer at 2am instead of a ticket

Your existing help content becomes a service that works overnight and across time zones, booking genuine exceptions into the morning queue.

Assistance reaches people where they already are

Web, WhatsApp, Teams, Slack or SMS, in the languages your users actually speak, without asking anyone to install anything.

Sales opens qualified conversations, not names

Conversational qualification captures intent in the buyer’s own words and writes a structured record into your CRM before a rep is involved.

AI CHATBOTS WE HAVE PUT INTO PRODUCTION

Three production systems. Different industries, different output types, and one shared architecture: the model generates into a system of record, and a named accountable person releases it. The numbers are from after launch.

View all AI case studies

Shoorah - Conversational AI at scale in a regulated wellbeing context

A mental wellness platform needed round-the-clock support for individual consumers and corporate HR programs from one product, handling deeply sensitive data, with certainty that conversations needing a clinician reached one. We built Shuru, an AI companion available 24 hours a day in multiple languages, on a backend also serving an enterprise portal and a white-label API layer, with a connected expert portal routing users to licensed therapists, doctors and life coaches. Escalation was part of the original design, not a later fix.

Outcomes:

120,000+AI therapy conversations delivered
1,000+five-star reviews across app stores
Read the Shoorah case study

Arrow - Knowledge assistance designed to hold no patient data

Medical billing teams needed fast answers inside a platform handling protected health information at scale. We scoped OpenRCM as a zero-PHI knowledge assistant: rather than securing a chatbot that touches PHI, it was designed never to receive any. Deciding what the conversational surface is not allowed to see is usually cheaper than securing it afterward.

Outcomes:

85%Claim denials reduced by
18 DaysAccounts receivable days cut from 45
1.5B+Claims processed through the platform
Read the Arrow case study

WHY TEAMS CHOOSE US FOR CHATBOT BUILDS

Most chatbot projects do not fail on the model. They fail because an answer was wrong once in public, the knowledge went stale, escalation dropped the conversation, or nobody could say whether the thing was working. Four commitments prevent that, and they are why our engagements start with your content rather than your model choice.

Every answer traces to a source you control

The chatbot answers from your content rather than a model’s general training, and returns the supporting passage where the answer must be checkable.

Users see only what they are entitled to see

Retrieval is filtered by the asking user’s permissions before the model reads anything, so a conversation cannot become a route to a document.

Quality is measured, not debated

An evaluation set built from your real questions runs on every change, and transfers to you at handover with the prompts, pipelines and conversation logs.

Handover to a person is designed, not improvised

Three conditions route to a human: nothing adequate was retrieved, the question crosses a permission boundary, or the subject touches money, health, legal standing, employment or safety.

A proof of concept on your data before any production build, no exceptions. You own everything from day one, and your data never trains models for anyone else.

AI CHATBOT DEVELOPMENT SERVICES

01

Chatbot strategy and feasibility

Your real questions, taken from ticket logs and search queries, mapped to the sources that should answer them. Ends in a written recommendation and a fixed estimate.
02

Conversational AI development

The chatbot itself, designed around your users, content and tone, with the questions it must refuse specified alongside those it must answer.
03

Knowledge and retrieval integration

Connection to the content that holds your authoritative answers. Where retrieval quality over a large or messy corpus is the deciding factor, that is specialist retrieval-augmented generation work RAG development services.
04

Business system integration

Read access into helpdesk, CRM, order and account systems, scoped to what the person asking is entitled to see.
05

Human escalation design

The handoff path, carrying transcript, sources and escalation reason into your support tooling so the customer never repeats themselves.
06

Evaluation and optimization

A test set of real questions run on every change, plus post-launch monitoring of answer quality, escalation rate and cost per resolved conversation.

AI CHATBOT DEVELOPMENT COST AND ENGAGEMENT

A grounded production chatbot connected to your content and one or two business systems is typically a focused AI solution at $25,000 to $60,000 over 8 to 14 weeks. Larger multilingual or regulated programs sit in higher bands documented on our AI development services page. Ongoing operation runs at 15 to 20% of build cost annually. 

Engagements run in four stages: a feasibility assessment producing a written recommendation and fixed estimate, a proof of concept on your own data, the build and integration, then launch with monitoring in place. 

Model, retrieval architecture and infrastructure are selected against your latency, sensitivity and cost requirements, working across frontier and open-weight model families, managed and self-hosted vector stores, and your existing helpdesk, CRM and messaging platforms.

What drives the investment
Why it matters
Knowledge readiness
Whether the answers exist in writing, are current, and are resolvable when versions conflict. The largest variable and the most underestimated
Number of integrations
Each system adds authentication and entitlement filtering. Systems without a usable API are where budgets move
Security and regulatory scope
Permission filtering, logging, retention and data residency add design and review time
Languages and channels
Each language adds evaluation work, not just translation. Each channel adds delivery and testing

IS A CHATBOT THE RIGHT BUILD

Most organizations need a chatbot alongside deterministic flows and human agents. Drawing the boundaries deliberately is what keeps the automation from costing more than the work it replaced.
Modality
Use when
Business case
Cost of change
Keep it for
Scripted flow
The path is fixed and the outcome must be exact
Removes error from transactions
Every new question is a branch built by hand
Payments, cancellations, consent capture
Retrieval chatbot
Questions vary in wording and answers exist in writing
Removes waiting from the long tail of questions
Update the content and the chatbot follows
Policy, product, account and how-to questions
Human support
The answer needs judgment or accountability
Protects the relationships that matter most
Training and headcount
Complaints, exceptions, anything with a consequence
WE WILL TELL YOU WHEN IT IS NOT A CHATBOT.

WE WILL TELL YOU WHEN IT IS NOT A CHATBOT.

If the point is to update the record or run a multi-step task rather than explain it, you want AI Agent Development. If the user is already inside your application and assistance should appear in context, that is AI Copilot Development. If the answers do not exist in writing yet, we recommend fixing the content first, because a chatbot surfaces knowledge and does not create it. And if nobody has named the number the project should move, it has no way to succeed or fail.

We are also not the right partner for the cheapest possible chatbot on a three-week timeline. The grounding, evaluation and escalation work in section 4 makes us slower and more expensive than a vendor shipping a widget, and it is why these systems are still running a year later.

Mesh Gradient Background

FIND OUT WHETHER A CHATBOT IS THE RIGHT BUILD

The AI Feasibility Assessment reviews one use case against your content and systems, returning a written recommendation, architecture options and a fixed estimate in two weeks, at no cost. Sometimes the recommendation is not to build.

Get A Feasibility Assessment

ALREADY HAVE A CHATBOT THAT UNDERPERFORMS?

Already have a chatbot that underperforms? The Stalled Pilot Review is a five-day audit of grounding, evaluation coverage and integration surface.

Review A Stalled Chatbot

AI CHATBOT DEVELOPMENT FAQS

Explore answers to common questions about AI Chatbot Development capabilities.

Yes. Read access into helpdesk, CRM, order and account systems is standard scope, with the authentication and entitlement filtering each one needs. Systems without a usable API are the exception, and we surface that cost during feasibility rather than in month four.

Yes. It retrieves from your documents and content and answers from what it retrieved, returning the source where the answer needs checking. Where nothing relevant comes back, it says so rather than improvising.

Yes. The chatbot inherits the asking user’s entitlements and retrieval is filtered before the model sees anything, so nobody can reach content through conversation that they could not reach directly.

Yes, carrying the transcript, the sources retrieved and the escalation reason into your helpdesk as part of the ticket. Handoffs that drop context are the most common reason users rate an otherwise capable chatbot poorly.

Usually. Flows, integrations and channel setup normally survive. What rarely survives is an intent-matching layer, because a grounded architecture answers from content rather than predefined intents. In practice this is a re-architecture that reuses the integration work.

Against an evaluation set built during feasibility, then in production through the share of answers traceable to a source, escalation rate, confirmed resolutions and cost per resolved conversation. We agree which of those the project is accountable for before development starts.

When the value is in completing work rather than answering questions. If the system needs to update records, process claims or run a multi-step task across several systems, that is a different engagement.
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LLM Development & Fine-Tuning
LLM Development & Fine-Tuning

When the base model is not accurate enough on your domain and prompting has stopped helping.

RAG Development
RAG Development

When answers must be grounded in your own documents and cite them.

AI Agent Development
AI Agent Development

Systems that plan and act rather than answer

Generative AI Development
Generative AI Development

the wider application category

AI INSIGHTS FROM OUR ENGINEERING TEAM
Written by the people doing the work, for the questions that come up before a project starts.