ENTERPRISE CONVERSATIONAL AI SOLUTIONS & CONSULTING
Triazine Software engineers enterprise conversational AI: customer service chatbots, voice bots and knowledge assistants that understand natural language across languages, answer from your systems of record and verified data, and transfer to the right person with full context when a case requires it. Every engagement starts with the conversations that matter most to your customers and workforce, and every assistant remains fully owned by your organisation.
Customers expect an answer at 11 at night, in their own language, on the channel they already use. Employees expect to query enterprise systems instead of navigating dashboards. Most chatbots fail both: they follow rigid scripts, misread anything unexpected, generate unsupported answers or trap users in loops with no route to a person. Enterprise conversational AI understands intent, answers from verified data, completes routine actions such as status checks and service requests, and escalates to the right team with the full conversation history when a case needs human judgment. We engineer these solutions for enterprises across the US, UK, UAE, Australia, South Africa and India.
Trusted by the Tony Elumelu Foundation, IGL, Aavantika Gas, Godfrey Phillips, Domino's Pizza, Ministry of Defence, Schindler, University of Cambridge and other enterprises worldwide
Conversational AI is technology that lets people talk to software in natural language, by text or voice, and get accurate answers or actions back, using natural language understanding, large language models, speech recognition and integration with business systems.
A search bar returns documents. A dashboard returns whatever was pre-built into it. Neither one answers a question they both hand the person a pile of data and leave them to find the answer themselves. A conversational AI assistant does the finding: it understands what's actually being asked, retrieves the specific data behind it, and returns a direct, structured answer "What are the top 3 risk areas from last month?" gets a ranked list, rather than a dashboard link.
That's what separates enterprise conversational AI platforms from a generic chatbot. A scripted FAQ bot answers from a fixed set of pre-written responses. An enterprise knowledge assistant reasons over your live operational data incident records, compliance summaries, sales figures, field reports so the answer is accurate to what's actually happening in your systems today, rather than a canned reply written months ago. Where an answer should trigger the next action automatically, that connects to our agentic AI orchestration layer: the assistant surfaces the answer, the agent acts on it.
Business Outcomes
Users get a direct answer to a plain-language question incident history, compliance status, performance rankings bypassing the need to learn a platform, apply a filter, or wait on someone else to pull the data.
Routine questions about bills, orders, applications and status are answered instantly, in the customer's language, on web, WhatsApp or voice, with no queue.
Recurring operational reports are generated automatically from live data in a defined format. So every report stays complete, on schedule and consistent, independent of who wrote it and how much time they had.
Because the interface is a plain-language question rather than a trained skill, adoption extends to the people closest to the work field force, supervisors, operators reaching well beyond the analysts who already knew how to run a report.
"Conversational AI" covers very different technologies. Here is how the four main types compare, and where each one fits.
| Rule-based chatbot | NLU intent bot | LLM + RAG assistant | AI agent | |
|---|---|---|---|---|
| How it understands | Buttons and keywords | Trained intents and entities | Language models reading your content | Language models plus planning |
| Unexpected questions | Fails | Handles variations of known intents | Handles open questions | Handles open questions and multi-step goals |
| Answers from your data | Fixed scripts | Scripted replies plus API lookups | Retrieves from documents and systems, with sources | Retrieves and acts across systems |
| Takes actions | Simple form steps | Defined transactions | Simple lookups and requests | Multi-step work with approvals |
| Main risk | Frustrated users | Rigid flows | Made-up answers without guardrails | Wrong actions without guardrails |
| Best for | Menus and simple FAQs | High-volume, predictable requests | Knowledge questions and support | End-to-end processes (agentic AI) |
Most of our solutions combine the last three: NLU or structured flows for predictable transactions, an LLM grounded in your data for open questions, and agents where the conversation should complete a task.
Use-case discovery from your call, chat and ticket data, prioritised by volume and value; channel and platform choice; and conversation design: the assistant's persona and tone, dialogue flows, fallback paths and handoff rules, written and tested before a line of code. We also train your team to own and extend the assistant.
Multilingual virtual agents on your website, mobile app and WhatsApp that answer questions from your verified content, check bills, orders and application status in your systems, raise requests and complaints, and hand over to a live agent with the full conversation when needed.
Voice assistants that understand natural speech on your phone lines, replace menu-driven IVR with "tell me what you need", resolve routine calls, and route the rest to the right agent with a summary, plus agent-assist that suggests answers and drafts call notes in real time.
The core of what we build. Enterprise users type or ask plain-language questions and get a structured answer drawn from live operational data records, reports and insights in seconds, through conversational AI solutions built for real operational complexity, not manual report generation.
Built for the people who aren't at a desk. Field force and operational teams query outlet performance, stock levels, incident status and rankings from a mobile device, in natural language, through conversational AI platforms designed around field use, rather than a dashboard built for analysts.
Generic chatbots rarely understand operational language. As a conversational AI development partner, we build the assistant's retrieval logic, terminology and scope around your actual systems and data so it understands how your business actually talks about its own operations.
Deep integration into the ERP, CRM, EHS and operations platforms you already run the assistant queries and retrieves from live systems of record directly, keeping every answer current rather than pulling from a parallel data extract that goes stale.
Go-live is the starting point, with the real work continuing well beyond it. We track answer accuracy, unanswered queries and usage patterns on every deployed assistant, refining retrieval and scope as your systems and questions evolve.
Most clients start with one channel and the top questions by volume, prove the value, then expand.
Best as the first step. A short, fixed-scope engagement that analyses your conversation data and ends with ranked use cases, a channel and platform recommendation and a pilot plan.
Best for proving value. One channel and the top use cases, live with real users, measured against agreed resolution, handoff and satisfaction targets.
Best when the pilot has proven value. More channels, languages and use cases on a shared knowledge, integration and analytics layer.
Best after launch. Conversation designers and engineers review transcripts, close knowledge gaps and add use cases every month, or work as an extension of your team. See hire data and AI specialists.
Most first assistants go live on one channel in 6 to 10 weeks.
Define the questions users actually need answered, and the data behind each one.
Typical duration: 1-2 weeks, using real call, chat and ticket data
Connect the ERP, CRM, EHS and operational systems the assistant will retrieve from.
Typical duration: 1-2 weeks
Define how the assistant recognises what's being asked and which data it maps to.
Typical duration: 1-2 weeks, including persona and conversation design
Build how the assistant finds the right data and structures it into a direct, usable answer.
Typical duration: 2-3 weeks
Test the assistant against real questions and real data before it reaches live users.
Typical duration: 1-2 weeks against real questions in every launch language
Define what the assistant answers directly, and what it escalates or declines to answer.
Typical duration: inside validation
Release into production with a limited group of real users, on real queries, contained scope.
Typical duration: 4-6 weeks with real users on one channel
Open the assistant to every user and every question it was mapped to handle - full channel coverage, no more contained scope, no more limited group.
Typical duration: phased by channel and language
Track answer accuracy, unanswered queries and usage patterns on an ongoing basis, feeding what's learned back into query mapping.
Typical duration: ongoing, with monthly transcript reviews
We use AI to answer customers and employees instantly, not to trap them in a bot or replace the people they sometimes need. Our engineers and conversation designers stay responsible for what the assistant knows, what it is allowed to say and do, and when it steps aside, and your teams stay in control of every case that needs judgment. Every answer is grounded in your verified data, every handoff passes the full context to a person, every conversation is reviewed and improved, and the assistant and its data stay fully owned by you.
The assistant answers from your approved content and live systems, shows where an answer came from, and says "I don't know" and offers a person instead of inventing one.
When a case needs a person, the conversation, the customer's details and a short summary go to the live agent or helpdesk, so nobody is asked the same question twice. On TEFConnect, the assistant escalates to people when needed.
Topics, actions and tone are defined up front: no legal, medical or financial advice beyond your approved wording, no actions above set limits, and sensitive requests always routed to people.
Unanswered questions, handoffs and low ratings are reviewed regularly, and the knowledge base and flows are updated, so the assistant answers more each month.
| Channel | Best for | Example |
|---|---|---|
| Website and mobile app chat | Customer support and lead capture | Bill, order or application status with live-agent handoff |
| WhatsApp Business | Customers who will not download another app | New connection requests, payment reminders, complaint status |
| Voice and IVR | Phone-first customers and contact centres | Natural-speech IVR that resolves routine calls and routes the rest |
| Microsoft Teams and Slack | Employee helpdesks and knowledge assistants | HR, IT and policy questions answered from your own documents |
| SMS | Notifications and short replies | Appointment and payment confirmations with two-way replies |
| In-app field assistant | Field teams on mobile | "What is the stock at this outlet?" or "Is this permit approved?" |
| Conversational AI platform | Custom LLM + RAG build | Hybrid | |
|---|---|---|---|
| Examples | Dialogflow CX, Amazon Lex, Azure Bot Service and Copilot Studio, Rasa | Claude, OpenAI or Azure OpenAI with your own retrieval and orchestration | Platform for channels and flows, custom LLM for open questions |
| Speed to first launch | Fastest | Moderate | Fast |
| Control over behaviour | Within the platform's limits | Full | High |
| Data residency and private deployment | Depends on the platform | Fully in your control | Configurable |
| Lock-in | Tied to the platform | None; you own it | Low |
| Best for | Standard support flows in one ecosystem | Knowledge-heavy or regulated use cases | Most enterprise deployments |
We are platform-neutral and recommend per use case.
We build conversational AI software on the same retrieval, language-model and orchestration frameworks powering enterprise-grade knowledge assistants today backed by infrastructure proven across 11+ years of mission-critical delivery.
In production for our clients: an NLU assistant that answers entrepreneurs in English, French, Arabic and Portuguese and escalates to people, running on Azure as part of TEFConnect.
Conversations carry personal data and speak for your brand. These controls are designed in from the first sprint. We build assistants that support your regulatory obligations; certification of your own organisation stays with you and your auditor.
Customers verify with OTP or your existing login before the assistant shows bills, orders or applications, and staff assistants respect each user's role-based permissions.
Card numbers, IDs and health details are masked in transcripts and logs, and only the data a conversation needs is sent to a language model.
The assistant is tested against attempts to make it ignore its rules, reveal internal data or produce harmful content, before launch and after every change.
Models and transcripts stay in the regions you specify, or run in your own cloud account. Your conversations are not used to train public models.
Every conversation, answer source, action and handoff is logged with a timestamp. On TEFConnect, every AI decision is logged with reviewer override.
CMMI Level 3 delivery governance and ISO 9001:2015 quality management standards. Architectures support GDPR, Nigeria's NDPR (TEFConnect), India's DPDP Act and Saudi PDPL, depending on geography. Your data is protected by our Non-Disclosure Agreement, and every assistant, flow and knowledge base belongs to you.
Assistants work best where we already know the customers, the systems and the rules.
WhatsApp and voice assistants for bill and payment queries, meter-reading submission, complaint registration and status, new connection requests and CNG station finder.
Distributor order and claim status, scheme information, and a field sales assistant that answers outlet performance and stock questions in plain language.
Voice-first field assistants for permit status, safety procedure questions and quick incident reporting from the shop floor.
Multilingual citizen helpdesks for applications, eligibility and status, as on TEFConnect.
Appointment booking and reminders, scheme eligibility questions and patient FAQs, with clinical questions always routed to clinicians.
HR policy, leave, IT support and onboarding questions answered from your own documents in Teams or Slack.
Order tracking, returns and refunds, product discovery and abandoned-cart follow-up.
Vendor invoice and payment status, expense policy questions and approval reminders.
Learner support, course guidance and assessment questions, in the learner's language.
Shipment tracking, delivery rescheduling and proof-of-delivery queries.
Multilingual assistants, retrieval-augmented chatbots and natural language search in production, each architected, developed, implemented and supported by Triazine.
AI capabilities: Natural language understanding, multilingual conversational AI | Industry: Government and public programmes | Services: Architecture, development, implementation, support and maintenance
For the Tony Elumelu Foundation, an NLU assistant inside TEFConnect answers entrepreneurs in English, French, Arabic and Portuguese about applications, training and funding, and escalates to people when needed, with every AI decision logged for audit. Platform results: 265,000+ applications processed, 90% fewer customer complaints after go-live and 99.9% uptime, for a programme that has disbursed $100M+ across 54 African countries.
AI capabilities: Retrieval-augmented generation (RAG), large language models | Industry: Customer support and knowledge management | Services: Architecture, development, implementation, support and maintenance
Support teams were answering the same questions repeatedly, user messages were vague or misspelt, and answers were scattered across FAQs and long documents. A LangGraph pipeline rewrites each message into a clear query, labels its intent and key entities, searches curated FAQs first and the knowledge base only when no FAQ matches, and scores confidence by combining retrieval similarity with an LLM check of context sufficiency. Low-confidence queries automatically create a support ticket and share support contact details, so users reach a person instead of a wrong answer. Built with React, FastAPI, LangGraph, Groq-hosted LLMs, Chroma and PostgreSQL.
AI capabilities: Natural language processing, large language models, natural language search | Industry: Multiple industries | Services: Architecture, development, implementation, support and maintenance
Users struggled to find information across multiple dashboards and filters, and non-technical staff avoided traditional reporting tools. The assistant understands the intent behind a plain-language question, converts it into system searches and returns reports, records and summaries instantly, reducing time spent searching and speeding up decisions.
AI capabilities: Natural language processing, sentiment analysis, social media analytics | Industry: Government | Services: Architecture, development, implementation, support and maintenance
Natural language processing analyses social media conversations to measure public sentiment towards digital campaigns and programmes in real time, giving communication teams a continuous view of how their messages are received.
"Working with Triazine Software was an excellent experience. Their flexibility, responsiveness, and problem-solving expertise delivered every requirement, improved our app store ratings, enhanced customer experience... and significantly reduced the cost of resolving customer complaints."
Chief General Manager, Indraprastha Gas Limited (India)
"We weren't looking for a traditional vendor. We wanted a true technology partner who could integrate with our team, understand our creators, and share our vision. Feels like one unified team collaborating across continents."
Fractional CTO, Sound Royalties (United States)
"Triazine Software was recommended by IBM for our Pizza Online Ordering mobile app and the team stood up to our expectation... I highly recommend Triazine Software as dependable development partner for organization like us."
Technical Program Manager, Domino's Pizza (India)
"Triazine delivered high-quality work on time, transforming our vision into a successful hybrid app. Their patience, responsiveness, flexibility across time zones, and commitment to implementing every feedback ensured outstanding results."
CEO, TrueLook (United States)
"Triazine Software transformed Ticketkore with a powerful smart ticketing and reservation platform."
CEO, Ticketkore (South Africa)
Off-the-shelf chatbots answer from a fixed script. As a conversational AI consulting partner, we build retrieval logic trained on your actual terminology and systems, so the assistant understands what your teams are really asking.
Before launch, every assistant is tested on real questions from your call, chat and ticket history in every launch language, and after launch we report resolution, handoff and satisfaction against agreed targets every month.
The assistant queries your ERP, CRM, EHS and operations platforms directly, so every answer reflects what's happening in your systems today, staying in sync rather than pulling from a parallel dataset.
Field supervisors, distributors and operators get the same plain-language access as analysts, from a mobile device, using an interface built for their role just as much as anyone else's.
We track answer accuracy, unanswered queries and usage patterns on every deployed assistant, refining scope and retrieval as your systems and questions evolve.
Every answer traces back to a verifiable data source, with access scoped and logged built to meet the governance standards of regulated, high-stakes enterprise environments.
We build assistants that work in the languages your customers actually use. TEFConnect's assistant answers in English, French, Arabic and Portuguese, and we design for Hindi and regional languages for Indian customers and field teams.
Triazine Software was founded in 2015 by Abhinav Kumar and Vikash Srivastava to help enterprises replace fragmented systems and manual workarounds with platforms that run with clarity, governance and accountability. Abhinav, Co-Founder and Director, brings over two decades of enterprise software engineering experience and leads technology direction and delivery. Vikash, Co-Founder and Director, leads operations, client success and strategic partnerships.
Conversational AI consultants who analyse your conversation data and prioritise use cases
Conversation designers who write the persona, flows, fallbacks and handoff rules
AI and integration engineers who build the assistant and connect it to your systems and channels
QA engineers who test answers, languages and guardrails before and after launch
USA: Triazine Software LLC, 8 The Green, Ste A, Dover, DE 19901. +1 (347) 941-1655
India: Triazine Software Pvt. Ltd., B-28 & 29, Sector 58, Noida 201301. +91-120 4275378
Email: sales@triazinesoft.com
Serving enterprises across North America, Europe, the Middle East, Africa and Asia Pacific.
Conversational AI is technology that lets people talk to software in natural language, by text or voice, and get accurate answers or actions back. It combines natural language understanding, large language models, speech recognition and integration with business systems, and powers chatbots, voice bots and knowledge assistants.
A typical solution includes use-case discovery, conversation design, the assistant itself on one or more channels, connections to your knowledge base and business systems, live-agent handoff, security controls, analytics, and ongoing optimisation after launch.
It covers analysing your call, chat and ticket data to find the highest-value use cases, choosing channels and platforms, designing the persona and conversations, defining handoff and guardrail rules, planning integrations and success measures, and training your team to own the assistant.
A basic chatbot follows buttons, keywords or a fixed script. Conversational AI understands what people mean in their own words, keeps context across a conversation, answers from your data and can take actions in your systems. Every conversational AI assistant is a chatbot, but most chatbots are not conversational AI.
Conversational AI talks: it answers questions and handles simple requests in a conversation. Agentic AI acts: it plans and completes multi-step work across systems, often without a conversation at all. Many enterprise solutions combine both, with the assistant as the front door and agents doing the work behind it.
We are platform-neutral. We build on Dialogflow CX, Amazon Lex, Azure Bot Service and Copilot Studio and Rasa, and build custom assistants on Claude, OpenAI, Azure OpenAI and Gemini with our own retrieval and orchestration, choosing per use case on control, data residency and lock-in.
Website and mobile app chat, WhatsApp Business, voice and IVR, Microsoft Teams, Slack, SMS and in-app assistants for field teams, sharing one knowledge base so answers stay consistent across channels.
Yes. TEFConnect's assistant answers in English, French, Arabic and Portuguese, and we design for Hindi and regional languages for Indian users. Each language is tested on real questions before launch.
The assistant answers only from your approved content and live systems, shows its sources, is tested against real questions before launch, and is designed to say it does not know and offer a person rather than guess.
When it is not confident, when the customer asks for a person, when the request is sensitive or above a set limit, or when the customer is frustrated. The conversation, customer details and a short summary go to the live agent or helpdesk.
Yes. The assistant reads and writes through APIs, so it can check a bill, an order or an application, raise a ticket or complaint and update the customer record, inside the systems you already run.
We track resolution or containment rate (conversations completed without a person), handoff rate, unanswered questions, response time and customer satisfaction, report them monthly against agreed targets and use them to plan the next improvements.
A use-case workshop takes one to two weeks. A first assistant usually goes live on one channel in 6 to 10 weeks, then expands by channel, language and use case in phases.
Customers verify their identity before seeing account data, personal data is masked in transcripts and logs, access is role-based, data is encrypted in transit and at rest and stays in the regions you specify, and your conversations are not used to train public models.
Yes. Triazine Software LLC is registered in Dover, Delaware, and you can reach the US team on +1 (347) 941-1655 or sales@triazinesoft.com. Delivery is run from our Noida, India centre with US clients such as Sound Royalties and TrueLook.
When a conversation should hand off to an agent that completes the work.
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