What we deliver
Twara Technologies designs and engineers applications that put large language models (LLMs) to productive use inside real business processes. We treat generative AI as software engineering with extra uncertainty: the model is one component among many, surrounded by retrieval, permissions, evaluation, monitoring and human review. The result is an application your teams can trust to the right degree, measure over time and improve safely.
For conversational front ends specifically, see AI Chatbots & Virtual Assistants; for extracting data from documents, see Intelligent Document Processing.
This service suits organisations whose people spend much of their day reading, searching and writing, and those building products in which language-model features would genuinely help users.
Typical scope
- Knowledge assistants that answer questions from policies, manuals, contracts or tickets, with citations.
- Drafting and summarisation inside CRM, helpdesk, email or document tools.
- Classification and routing of free-text requests, complaints or emails.
- Structured extraction from unstructured text into your systems.
- Agent workflows that call internal APIs, such as looking up an order or creating a draft record, within defined permissions.
- Evaluation, monitoring and cost controls for all of the above.
Technologies we work with
- Hosted models: major commercial model families offered through their own APIs or through cloud platforms such as Amazon Bedrock, Microsoft Azure and Google Vertex AI. Cloud routes can simplify data residency, billing and access control.
- Open-weight models: widely used open model families, served with tools such as vLLM or Ollama on your own infrastructure when data control or unit cost matters most.
- Retrieval: vector search in PostgreSQL with pgvector, OpenSearch or Elasticsearch, or a dedicated vector database, combined with keyword search for exact terms.
- Orchestration: lightweight in-house code where possible; frameworks such as LangChain or LlamaIndex when they genuinely reduce effort.
- Evaluation and observability: test harnesses with automated and human-graded checks, plus tracing of prompts, retrieved context, outputs, latency and cost.
How we choose: quality on your own examples first, then data-handling terms and hosting location, then latency and running cost. We keep the model behind an interface so it can be swapped as the market moves.
How we approach it
- Frame the task. Who uses it, what a good output looks like, and what happens when it is wrong.
- Collect examples. Real inputs and expected outputs from your team become the first evaluation set.
- Prototype and compare. Try candidate models and retrieval designs, and measure them against the set.
- Build the application. Integrate with your systems, permissions and user interface; add guardrails and logging.
- Pilot with real users. Gather feedback and failure cases, and grow the evaluation set from them.
- Operate. Monitor quality, cost and safety signals, and re-run evaluations before every significant change.
Security, privacy and quality
- LLM-specific risks. The OWASP Top 10 for LLM Applications 2026, published by the OWASP GenAI Security Project in August 2026, lists risks including prompt injection, sensitive information disclosure, excessive agency, unbounded consumption, misinformation, hidden context exposure, vector and embedding weaknesses and improper output handling. We address each one in design: treating retrieved content and user input as untrusted, enforcing document-level permissions in retrieval, validating outputs before they reach other systems, and limiting what agents can do without approval.
- Risk management. We use the voluntary NIST AI Risk Management Framework and its four functions (Govern, Map, Measure, Manage) to structure risk discussions, together with NIST’s Generative AI Profile, NIST AI 600-1, which identifies risks such as confabulation, data privacy and information security.
- Personal data. India’s DPDP Rules, 2025 were notified on 14 November 2025 with an eighteen-month phased compliance period. We design for purpose limitation, data minimisation and deletion, and keep personal data out of prompts and logs unless it is genuinely needed.
- EU users. The EU AI Act, Regulation (EU) 2024/1689, includes transparency duties such as informing people when they are interacting with a chatbot and labelling deepfakes.
Engagement options
- Discovery and proof of value: a focused exercise to test a use case on your own data and examples.
- Application build: Twara Technologies delivers the production application end to end.
- Rescue and hardening: take a promising pilot and make it measurable, secure and affordable.
- Ongoing evaluation and improvement: monitoring, model updates and regression testing as providers release new versions.
Contact us to discuss the task you want to improve.