We design and deploy AI chatbots trained on your business data — so support teams answer faster, sales captures more qualified leads, and website traffic turns into conversations that convert.
75+ AI systems built · Upwork Top Rated Plus · 2–4 weeks typical launch
30 minutes · Senior engineer · No commitment
The goal is not just a chatbot widget. The goal is faster answers, cleaner lead capture, and fewer repetitive conversations for your team.
Bots designed around your funnel, pages, and customer questions rather than generic prompts.
Resolve repetitive support queries, route edge cases to humans, and reduce first-response delay.
Knowledge bases, PDFs, SOPs, product docs, and internal notes structured into accurate answer systems.
Capture budget, timeline, use case, and contact details before a sales rep ever steps in.
Fallbacks, sensitive-topic handling, confidence thresholds, and human handoff paths built in.
Pass chat transcripts and lead data into your CRM, email, Slack, or ticketing stack automatically.
These are the chatbot use cases that usually create measurable ROI first.
Answer FAQs, onboarding questions, account issues, and product guidance before tickets hit your team.
Turn anonymous traffic into qualified leads by asking the right pre-sales questions and routing hot prospects instantly.
Help staff search SOPs, onboarding docs, and process knowledge without digging through scattered files.
Guide users through onboarding, setup, pricing, and feature discovery directly inside your app or site.
Buyers searching for an AI chatbot development company usually hit these walls before finding a team that ships.
Off-the-shelf widgets answer with confident nonsense because they were never trained on your policies, SKUs, or support history.
Ticket queues expand faster than headcount. First-response SLAs slip even when the product is strong.
Paid ads and SEO bring visitors, but forms feel heavy and live chat staffing is expensive.
The bot lives outside your CRM, helpdesk, or WhatsApp workflow — so follow-up still breaks down.
150+ products shipped · Top-rated on Upwork & Fiverr · Production chatbots, not demo scripts.
We structure FAQs, PDFs, SOPs, and product docs into retrieval layers so answers cite your business — not generic model guesses.
Confidence thresholds, sensitive-topic rules, and human handoff to email, CRM, Slack, or WhatsApp with full transcript context.
Beyond the widget: CRM routing, analytics, workflow triggers via n8n, and post-launch tuning from engineers who've shipped 75+ AI systems built.
No junior hand-off. You work directly with builders who've deployed chatbots for SaaS, nonprofits, and ecommerce teams in the US, UK, and UAE.
Typical custom chatbot engagements from discovery to production.
Most production chatbots use a retrieval-augmented generation (RAG) layer over your documents, a routing layer for tools and CRM writes, and guardrails for PII and low-confidence fallback — not a single prompt in a iframe.
We scope what content is indexed, where embeddings live, and whether queries can be logged — aligned to your privacy policy and customer contracts.
Role-based bot behavior for internal vs external users; secrets in environment vaults, not client-side code.
Sensitive intents (billing disputes, medical, legal) route to humans instead of autonomous replies.
“What a 70-engineer team could not deliver, a small senior team at GreeLogix shipped. The app went from stuck to live across mobile, web, and backend.”
At-a-glance scope, pricing tiers, delivery timelines, engineering tradeoffs, and decision criteria for technical founders and product leaders.
AI automation combines workflow tools (n8n), large language models (GPT-4, Claude), and optional voice or phone agents to handle repetitive business tasks — support, lead qualification, scheduling, and data sync between systems.
Pricing depends on scope, integrations, and timeline. GreeLogix provides transparent fixed-milestone quotes after discovery, with flexible models for one-off audits, sprint retainers, or dedicated engineers.
Simple workflows: 1–2 weeks. Chatbots with RAG: 3–4 weeks. Voice or phone agents with CRM: 3–6 weeks. Phases: Discovery & use-case mapping (Week 1); Knowledge & conversation design (Week 1–2); Build, integrate & test (Week 2–3); Launch & optimize (Week 3–4+).
Compare delivery models, cost profiles, and engineering tradeoffs before committing resources.
| Option / Approach | Best When | Primary Tradeoff / Risk | Recommended Fit |
|---|---|---|---|
| Zapier / Make DIY | Simple 2-step automations with no custom logic or AI | Scalability bottlenecks, vendor lock-in, architectural rigidity, and substantial technical debt before handling production traffic. | Early validation & UI experiments |
| Off-the-shelf chatbot SaaS | Generic FAQ widget when you don't need CRM writes or custom flows | Recurring per-seat software fees, inflexible workflow constraints, and zero proprietary software or IP ownership. | Standard non-differentiating processes |
| In-house AI team | You already have ML engineers and want full control of model training | 3–6 month hiring cycle, high recruitment fees, and fixed long-term payroll overhead regardless of development volume. | Companies with dedicated HR pods |
| GreeLogix Pod | You need production AI workflows and voice agents hooked directly into your real CRM and database, not just isolated demos. | Requires active product owner for weekly sprint reviews and scope alignment. | Production & Scaling (Recommended) |
Talk to a senior engineer — scope, timeline, and cost range in one 30-minute call. No sales script.
Most companies looking for an AI chatbot development company are not actually shopping for a chatbot in isolation. They are trying to solve a business problem. Usually that problem is one of three things: support teams are overwhelmed, website traffic is not converting into enough qualified conversations, or internal teams are wasting time answering the same questions over and over again. A chatbot becomes valuable when it directly reduces one of those costs or creates more pipeline from the traffic you already have.
That is why our approach starts with intent, not just implementation. We look at the pages users land on, the questions prospects ask before buying, the repetitive support patterns, and the exact moments where response speed changes outcomes. From there, we design a chatbot experience that fits your actual business rather than dropping a generic AI assistant into the corner of your site and hoping people use it.
For some companies, that means a sales-focused bot that qualifies leads and books calls. For others, it means a support assistant trained on help docs and past conversations. In many cases, the best result is a hybrid model where the chatbot handles frequent questions, captures lead details when intent is high, and routes complex requests to the right human with full conversation context attached.
A lead-generation chatbot should never feel like a gimmick. It should move visitors forward. That means understanding search intent, matching the language of the page they landed on, and asking questions that improve sales quality instead of adding friction. On a service page, for example, the bot might ask what the visitor wants to build, what stage they are at, and whether they already have existing software or data. Those answers can be enough to qualify urgency and fit before your team joins the conversation.
This matters because not every lead deserves the same response path. A founder looking for an MVP this quarter should not enter the same inbox process as someone casually exploring ideas. With a properly designed chatbot, you can segment conversations automatically, route hot leads to WhatsApp or a meeting link, and pass lower-intent inquiries into email nurture or a lighter follow-up queue. That keeps your sales attention where it has the highest chance of conversion.
We also think carefully about how the bot looks and behaves. Tone, prompt suggestions, fallback copy, and escalation rules all shape whether people trust the experience. The best chatbot systems feel helpful, fast, and specific to the business. They do not sound like a generic model pasted into a template.
The strongest chatbot projects connect into the systems you already use. We can integrate chatbot conversations with CRMs, support inboxes, internal notifications, and lead routing logic so the chatbot becomes part of the operating workflow rather than an isolated frontend feature. That is especially important if you want reliable follow-up and measurable ROI.
For support teams, we map the most common queries, create a response knowledge layer, define when the chatbot can answer directly, and decide when it should escalate. For sales teams, we design the qualification sequence, decide what fields should be collected, and make sure important context reaches the right team member quickly. If there is a need for multilingual interaction, AI search over documents, or secure handling of sensitive data, we account for that in the architecture from the beginning.
Because GreeLogix also works on AI integrations, automations, and custom product development, we can extend beyond the widget itself. If the chatbot needs to trigger workflows, look up product data, summarize inquiries, or push records into internal systems, we can build that end-to-end instead of stopping at surface-level implementation.
Deploying a conversational AI system is only valuable if it drives tangible business outcomes—reducing tier-1 support tickets, speeding up lead qualification, and automating complex inquiries. That is why our onboarding is focused on measurable impact: tell us where your support queue or sales pipeline is encountering friction, and we will architect the optimal conversational workflow.
If you already know you want a chatbot, we can scope the right build quickly. If you are still deciding whether your use case is better suited to an AI chatbot, voice agent, or broader AI integration, we can guide that too. The point is to help you deploy the right conversational system, not just sell the trendiest label.
If your team wants fewer repetitive tickets, faster replies, and stronger lead capture from the traffic you already pay for, this is a good place to start. Reach out and we will map the use case, data source, and rollout model that makes the most commercial sense.
Answers to the buyer questions we hear most before a project starts.
Most chatbot projects launch in 2 to 4 weeks depending on how much training data, routing logic, and integration work is needed. Sales qualification bots are usually faster than deeply integrated support assistants.
Yes. We can structure your knowledge base, FAQs, PDFs, SOPs, and product content into a retrieval layer so the chatbot answers using your business context instead of generic model responses.
Yes. We design escalation paths so low-confidence, sensitive, or high-value conversations can be handed to your team through email, CRM, support inboxes, Slack, or WhatsApp with context intact.
Most chatbot projects range from $5,000–$25,000 depending on integrations, languages, and knowledge-base complexity. We provide a fixed quote after a free strategy call.
We typically deploy OpenAI GPT-4 or Anthropic Claude with retrieval over your documents. Model choice depends on latency, cost, and data residency requirements.
Yes. We integrate chatbots with major CRMs, helpdesks, and custom APIs so conversations and leads flow into systems your team already uses.
We identify where a chatbot can reduce workload or increase lead conversion fastest.
We structure prompts, documents, qualification steps, fallback rules, and handoffs.
The bot is connected to your site, CRM, inboxes, and relevant workflows.
We refine answers, monitor conversations, and optimize qualification quality after launch.
Tell us your support or sales use case and we’ll recommend the right chatbot rollout plan.
From chatbots and voice agents to workflow automation — pick the capability that matches your roadmap.
Use our free calculators and planners — then book a strategy call when you are ready for a senior engineer review.
Case studies, guides, and free tools from the same engineering team.
Scope, timeline, and cost range — no sales deck. Or start with the free readiness quiz if you are still evaluating your stack.
You'll talk to Abuzer — the senior engineer who'll scope your build
Founder & Senior Engineer · Response within 4 business hours
Book a Free 30-Min CallResponse within 4 business hours · No commitment on first call