What we deliver
Twara Technologies builds conversational assistants that serve customers, partners and employees through the channels they already use. A useful assistant does three things well: it answers accurately from content you control, it completes routine tasks securely, and it knows when to hand over to a person. We engineer all three, along with the analytics and content workflows that keep the assistant improving after launch.
The underlying language-model engineering is shared with our Generative AI & LLM Applications service.
This service suits organisations with high volumes of repetitive questions, such as retailers, service providers, education institutions, healthcare administrators and internal shared-service teams. The best results come when there is already reasonable written content to draw on and a support team ready to take the conversations the assistant should not handle.
Typical scope
- Customer service assistants for orders, bookings, account questions, returns and product information.
- Internal helpdesks for HR, IT and policy questions inside workplace tools.
- Sales and pre-sales assistants that qualify enquiries and book meetings.
- Voice-enabled assistants where speech-to-text and text-to-speech add value.
- Integration with CRM, order management, booking and ticketing systems.
- Authentication flows so personal information is shown only to verified users.
- Analytics, content gap reporting and conversation review tools.
Technologies we work with
- Language models: hosted models through provider or cloud APIs, or open models on your infrastructure, chosen on quality in your languages, data terms and cost.
- Conversation frameworks: custom orchestration for LLM-first assistants; established platforms such as Rasa, Microsoft Copilot Studio, Amazon Lex or Google Dialogflow where structured flows and existing investments make them the better fit.
- Retrieval: search indexes and vector stores over help centres, documents and product catalogues.
- Channels: web chat widgets, mobile SDK integration, official messaging business APIs and workplace platforms such as Microsoft Teams and Slack.
- Helpdesk integration: handover to tools such as Zendesk, Freshdesk, Salesforce Service Cloud or your own contact centre software.
How we choose: if most conversations follow predictable steps, a structured flow with LLM assistance can be cheaper and more predictable; if questions are varied and content-heavy, an LLM-first design with retrieval usually serves users better. Many assistants combine both.
How we approach it
- Study real conversations. Review existing chat logs, emails and call reasons to find the questions and tasks worth automating.
- Design the scope and voice. Agree what the assistant handles, what it never handles, how it sounds and when it escalates.
- Prepare content. Fix gaps and contradictions in the knowledge base, since an assistant is only as good as its sources.
- Build and integrate. Retrieval, task integrations, authentication and handover.
- Test with real phrasing. Evaluate against collected questions in every supported language, including awkward and adversarial ones.
- Launch gradually. Start with a subset of users or topics, review conversations and widen scope as quality is proven.
Security, privacy and quality
- Attack resistance. Public chatbots attract misuse. We design against the risks in the OWASP Top 10 for LLM Applications 2026, including prompt injection, sensitive information disclosure, hidden context exposure and unbounded consumption, with rate limits, input and output filtering and strict scoping of what integrations can do.
- Personal data. Conversations often contain personal data. Following the principles described with India’s DPDP Rules, 2025, such as consent and transparency, data minimisation and storage limitation, we show clear notices, mask sensitive fields in logs and set retention periods for transcripts.
- Transparency. The EU AI Act treats chatbots as carrying transparency obligations, such as informing people they are interacting with one. We make the assistant’s nature clear to users wherever it is deployed.
- Accessibility. Chat widgets are built and tested against WCAG 2.2, the W3C Recommendation for web accessibility, including keyboard operation and screen-reader support.
- Governance. Risk discussions follow the four functions of the NIST AI Risk Management Framework: Govern, Map, Measure and Manage.
Engagement options
- Assistant discovery: analyse conversations and content, define scope and estimate effort.
- Assistant build and launch: design, build, integrate and roll out on agreed channels.
- Upgrade an existing bot: move a rule-based chatbot to an LLM-assisted design without losing what already works.
- Ongoing optimisation: conversation reviews, content updates and model changes after launch.
Contact us to discuss the conversations you want to automate.