We build chatbots that resolve routine questions, capture leads, and hand off cleanly to your team, grounded in your approved content instead of guessing.
This is a static preview, not a live model. It shows the kind of answer your chatbot would give.
What is your return window?
You can return most items within 30 days of delivery for a full refund, as long as the item is unused and in its original packaging.
Source: Returns Policy page, approved content set, updated by your team.
I do not have an approved answer for that one, so I have opened a ticket and passed our team the full conversation.
Your customers do not hate chatbots. They hate chatbots that loop, guess, and trap them. We build the opposite kind.
A rigid menu bot only understands the exact phrases its builders predicted, so a slightly different question sends the customer in circles. An ungrounded chatbot is worse: it sounds confident while inventing policies, prices, and links that do not exist. Both kinds create tickets instead of closing them, and both push customers toward the phone, angry, after they already tried to help themselves.
We combine natural language understanding, which reads what the customer actually means, with retrieval from your approved content, so every answer can be traced to a source. Intent recognition and dialog management keep the conversation on track, even when the customer changes topic mid-chat. When confidence is low, the chatbot hands off to a person with the full context, instead of gambling on an answer.
The result is a measurable shift in support load. Routine questions close inside the chat, your team handles the cases that truly need a human, and every conversation is logged so you can see exactly where the chatbot helped and where it handed off. That visibility is what turns a chatbot from a gamble into a controlled business tool, and it is built into every chatbot development service we deliver.
Pick one starting point, or combine several. Every service below uses the same grounding, review, and handoff controls.
We build RAG chatbots that retrieve passages from your documents, help center, and product data before answering. A vector database such as Pinecone or Qdrant stores your content as searchable meaning, so the chatbot finds the right passage even when the customer phrases the question differently.
We build LLM chatbots on large language models, tuned with your tone, rules, and guardrails. LangChain based orchestration connects the model to your tools and content, while prompt controls and output checks keep replies on brand and inside approved facts.
We build voice bots that handle spoken questions on the phone and in voice apps. Speech recognition turns speech into text, the chatbot reasons over your content, and a natural voice reads the answer back, with a clean transfer to an agent when the caller needs one.
We build multilingual chatbots that serve customers in their own language from one content base. Answers stay consistent across languages because every language draws on the same approved sources, and your team reviews translations before they go live.
We build enterprise virtual assistants that work across departments, with role based access, audit logs, and approval workflows. One assistant can serve support, HR, and IT, while each team controls only its own content and sees only its own conversations.
We build support automation that deflects tickets, collects the details an agent needs, and opens a complete ticket when a human is required. The chatbot reads order status, account details, and ticket history through your systems, so customers get real answers, not generic ones.
A chatbot pays for itself where questions repeat. These are the workloads we see most often.
Order status, returns, passwords, and billing questions are answered inside the chat, with a source attached. Containment rate, the share of conversations resolved without an agent, is tracked openly, so you can see the deflection you are actually getting.
The chatbot asks the questions your sales team asks, qualifies the visitor, books a meeting, and passes a complete lead record to your CRM. Visitors who arrive after hours still get a helpful conversation instead of a dead form.
Shoppers get product guidance, stock and delivery answers, and order help without leaving the chat. Connected to Shopify, the chatbot can check real order and product data, so its answers match what the store actually shows.
Employees ask about leave, benefits, password resets, and software access, and get grounded answers from internal policy documents. Sensitive requests route to the right team with the conversation attached, and access stays controlled by role.
Patients get appointment help, preparation instructions, and answers to common questions, built with privacy controls and PII redaction from the start. Clinical questions route to qualified staff, and the chatbot never pretends to diagnose.
New users get guided setup, feature explanations, and troubleshooting drawn from your product documentation. The chatbot reduces onboarding tickets and helps users reach value faster, while product feedback flows back to your team.
Customers do not all arrive through your website. Our conversational AI development services cover web chat, WhatsApp, Slack, Microsoft Teams, SMS, and voice, so one chatbot brain can serve every channel your customers already use. Answers, rules, and review controls stay consistent across channels because every channel draws on the same content and the same guardrails.
Our AI chatbot integration services connect the chatbot to the tools your team already runs. Zendesk and Salesforce carry tickets and customer history, HubSpot carries leads and marketing context, and Shopify carries orders and products. Frameworks such as Rasa, Google Dialogflow CX, Microsoft Bot Framework, and Amazon Lex are options we evaluate against your channels and scale, not defaults we force on every project. We recommend the stack that fits your team, your budget, and your existing systems, and we show you why before you commit.
Starting small is a deliberate choice, not a limitation. A chatbot that works well on one channel teaches you which questions customers really ask, which answers need better content, and where handoffs should happen. That evidence makes the second channel cheaper and faster to launch, because the content, the conversation flows, and the evaluation results already exist. Your team also learns how to review conversations and update content, so by the time the chatbot expands, running it feels routine rather than risky.
Two ways to teach a chatbot your business. Most projects need the first, some need both, and the choice should follow your content, not fashion.
| What to compare | RAG chatbot development | Fine-tuning |
|---|---|---|
| How it learns | Retrieves passages from your content at answer time, then writes a reply grounded in those passages. | Trains the model itself on your examples, so knowledge lives inside the model weights. |
| Best when | Your content changes often, answers must cite a source, or accuracy matters more than style. | You need a consistent tone, format, or style across thousands of similar replies. |
| Updating answers | Update the document and the next answer reflects it. No retraining needed. | Requires new training data and a retraining run before answers change. |
| Risk of invented facts | Lower, because answers are checked against retrieved passages and can show their source. | Higher for facts, because the model recalls knowledge without showing where it came from. |
| Our recommendation | Start here for support, product, and policy questions. Add fine-tuning only for tone and format where it clearly helps. | Use it as a complement to retrieval, not a replacement for grounded sources. |
In practice, most production chatbots we build use retrieval as the backbone and fine-tuning only where it earns its place. That combination keeps answers accurate, auditable, and easy to update, which is exactly what a support, sales, or operations team needs when the chatbot speaks for the business.
A chatbot touches customer data on every conversation, so security cannot be an afterthought. We redact personal details such as names, emails, and account numbers before they reach a language model, and we limit what the chatbot can retrieve to the content and systems each user is allowed to see. Access controls, audit logs, and retention limits are set during the build, not added after a problem appears.
Our work is built for compliance readiness, not claimed as certification. That means we design around GDPR and HIPAA requirements where they apply, follow SOC 2 and ISO 27001 aligned practices for access and logging, and apply OWASP Top 10 for LLM Applications guidance to prompt injection, data leakage, and unsafe output risks. You receive documentation that explains what data flows where, what is retained, and how a human reviews and overrides the chatbot, so your own compliance team can verify the setup.
Five phases, with an approval gate before launch. You see real answers on real questions before you commit to a full rollout.
We map your top questions, audit your content, and agree on what success looks like. You receive a fixed scope and price for the first phase before build starts.
We connect your content, build the conversation flows, and wire up the first channel and integrations. The pilot covers your most common questions first.
We test the chatbot against real customer questions, score accuracy, grounding, and handoff quality, and fix the weak spots. You see the evaluation results, not just a demo.
You approve the answers, tone, and handoff rules. Only then do we launch, starting with the channel and audience you choose, with monitoring from day one.
We review conversations, add content for questions the chatbot missed, and refine flows. Your chatbot gets better every month because its content and evaluation keep pace with your business.
No vague solution. A working chatbot, documentation, and a team that knows how to run it.
The chatbot itself, deployed on the channels you chose, connected to your content and your core systems.
A record of the content sources, the test questions, and the scores the chatbot earned before launch.
Tools for your team to edit content, review conversations, set handoff rules, and override answers when needed.
You own the chatbot, its configuration, and its content. We document everything so your team, or any team, can maintain it.
Start with a pilot on one channel and your most common questions. If the evaluation results earn your approval, we expand to more channels, more content, and deeper integrations. After launch, our support and maintenance keeps the chatbot monitored, its content current, and its answers improving, with regular reports on containment, handoffs, and the questions customers ask most. Pricing is always a fixed scope and price agreed after discovery, so you know exactly what each phase includes before it starts.
We also plan for the day your content changes. New products, new policies, and new pricing all need to reach the chatbot quickly, or customers start getting stale answers. Your team gets a simple content workflow: update the source document, review the chatbot's new answers on test questions, and publish. That workflow is part of the build, not an extra, and it is what keeps a chatbot trustworthy months after launch.
After a free discovery call, you receive a fixed scope and price for the first phase. Cost depends on the channels you need, the content the chatbot must cover, and the systems it connects to, so we never quote a number before we understand the work.
No. Our chatbots answer from content you approve, and they show the source behind each answer. When a question falls outside that content, the chatbot says so and offers a human handoff instead of guessing.
We build chatbots for your website, WhatsApp, Slack, Microsoft Teams, SMS, and voice. You can start on one channel and add more later, using the same answers, rules, and review controls.
No. It takes over the routine questions that repeat every day, so your team spends its time on the hard cases. Every handoff carries the full conversation, so customers never have to repeat themselves.
We redact personal details before they reach a model, limit data retention to what the project needs, and build access controls around every integration. Our work is built for GDPR, HIPAA, SOC 2, and ISO 27001 readiness, and we follow OWASP Top 10 for LLM Applications guidance for prompt and data safety.
Discovery sets the timeline for your project. We start with a small pilot on real customer questions, measure how well it answers, and only move to a full launch after you approve the results.
Bring one support problem. Leave with a clear scope for your first chatbot pilot.
Prefer email? Write to [email protected] and tell us your top support questions and the channels you use today.