ClickMasters builds production LLM applications for B2B companies across the USA, Europe, Canada, and Australia. Document Q&A systems that answer questions from your proprietary knowledge base with cited sources. AI writing assistants that generate on-brand content at scale. Contract analysis platforms that extract and compare terms across thousands of documents. Code review tools. Report generation systems. Every LLM application built with streaming, cost management, evaluation frameworks, and production observability not just a wrapper around an API call.
Years Experience
Projects Delivered
Client Satisfaction
Support Available
Production LLM applications require more than API calls. The gap between a demo that works in a Jupyter notebook and a product that reliably serves 10,000 users is the production architecture streaming, error handling, evaluation, cost management, and observability. ClickMasters builds every LLM application on this foundation from day one.
LangChain and LlamaIndex are both LLM orchestration frameworks, but they have different design philosophies and strengths. LangChain is a general-purpose LLM application framework it provides abstractions for chains (sequences of LLM calls), agents (LLMs that decide which tools to call), memory (conversation history management), and tool integration. LangChain is the better choice for complex multi-step LLM workflows, agent-based systems, and applications requiring broad tool integration. LlamaIndex is specialised for data-intensive LLM applications specifically RAG systems. It excels at document ingestion, chunking strategies, index construction, query pipeline configuration, and RAG evaluation (RAGAS integration). LlamaIndex is the better choice when the primary use case is Q&A or analysis over a document corpus. ClickMasters uses LangChain for orchestration-heavy applications and LlamaIndex for RAG-heavy applications often combining both in the same system.
LLM application evaluation uses automated and human evaluation methods. For RAG systems, RAGAS provides four automated metrics: Faithfulness (does the answer contain only information from the retrieved context no hallucinations?), Context Relevance (does the retrieved context contain information relevant to the question?), Answer Relevance (does the answer actually address the question asked?), and Context Recall (did the retrieval find all the relevant context?). For generation quality, DeepEval provides pytest-style unit tests for LLM outputs assert that a response contains specific information, does not contain specific words, is within a character length range, or matches a semantic pattern. LangSmith captures production traces real user queries and LLM responses can be reviewed, annotated, and used to build an evaluation dataset from production traffic. ClickMasters implements RAGAS or DeepEval evaluation as standard on all RAG and generation applications providing a quantitative quality baseline and a regression detection mechanism for future model or prompt changes.
What we deliver
05 capabilities
ClickMasters operates as a full-stack llm applications development partner — product strategy, UI/UX, engineering, cloud infrastructure, QA, and ongoing support in one delivery model.
LLM application answering questions from document corpus: ingestion pipeline (PDFs, Word docs, web pages via Unstructured.io, semantic chunking, embeddings in pgvector), query pipeline (question embedded → top-k retrieval → Cohere reranking → GPT-4o answer with citations), streaming response, source attribution UI, and admin interface for knowledge base management.
LLM-powered content generation for B2B: brand-voice writing assistant (system prompt encodes voice, few-shot examples demonstrate style), email and proposal generator (first-draft from template + CRM context), content repurposing tool (blog → social posts, summaries, newsletters), and multilingual content generation.
LLM-powered contract analysis: clause extraction (payment terms, liability caps, termination provisions structured JSON output), contract comparison (flag deviations from standard, severity rating), risk scoring, bulk analysiysis (hundreds of contracts), and contract Q&A with clause-leveltations.
Automated report generation from structured data: data-to-narrative (financial metrics, survey results → narrative interpretation), executive summary generation, personalised report generation (each user sees analysis of their specific data), and scheduled report generation (weekly/monthly automated reports).
LLM-powered developer tooling: automated code review (GitHub PR integration bugs, security vulnerabilities, style violations, test gaps), code explanation (plain language for onboarding), technical debt identification, and natural language to SQL (business questions → SQL queries against schema).
Why choose us
05 advantages
We combine architecture discipline, transparent delivery, and long-term partnership — so your investment translates into measurable business results, not just shipped code.
7 layers: LLM + orchestration + RAG + streaming + evaluation + observability + cost | Basic: API call wrapped in a UI
RAGAS metrics: faithfulness, context relevance, answer relevance, context recall | Basic: No evaluation (can't measure quality)
LangSmith tracing, token costs, latency metrics, replay production traces | Basic: No observability (black-box failures)
Token budgets, response caching, model tiering, per-user rate limits | Basic: No cost controls (unexpected bills)
SSE + ReadableStream API tokens displayed as generated | Basic: No streaming (blank screen, poor UX)
500+
Companies served
4.9/5
Client rating
15+
Years in delivery
Scroll to walk through each phase — lines connect as you move down.
Architecture design (RAG vs fine-tuning vs agents), model selection, RAG pipeline design, evaluation strategy, cost model, and success metrics. Deliverable: Architecture Specification.
Document ingestion pipeline (Unstructured.io), semantic chunking (meaning boundaries, not character count), embedding generation (text-embedding-3-small), vector store (pgvector), retrieval with reranking (Cohere Rerank). Deliverable: Production RAG Pipeline.
LangChain or LlamaIndex orchestration, chain definition, prompt engineering (system prompts, few-shot, chain-of-thought), structured output (JSON schema), response streaming (SSE). Deliverable: Core LLM Integration.
FastAPI backend with streaming endpoints, React frontend with ReadableStream API for token-by-token display, source attribution UI, admin interfaces. Deliverable: Full-stack Application.
RAGAS evaluation (faithfulness, context relevance, answer relevance), DeepEval unit tests, LangSmith tracing setup, cost monitoring dashboard, accuracy drift alerts. Deliverable: Evaluation Framework + Dashboard.
Deploy with feature flag, gradual rollout. Post-launch: prompt optimisation, evaluation monitoring, model updates, feature development. Deliverable: Production Application + Retainer Option.
Modern tools we use to build scalable, secure applications.
Languages & Frameworks
Data Processing
Infrastructure
Deep expertise across various sectors with tailored solutions
Pricing
Transparent pricing tailored to your business needs
* All prices are estimates and may vary based on requirements.
To build scalable, intelligent custom software development solutions that empower businesses to grow, automate, and transform in a digital-first world.
We are not building software. We are architecting the infrastructure of tomorrow—systems that think, adapt, and grow alongside the businesses they power.
Amjad Khan
Chief Executive Officer
12+
Years Exp
300+
Success
98%
Retention
Common Inquiries
Can't find the answer you're looking for? Please chat to our friendly team.
Discover how we can help transform your business through our comprehensive services, real-world case studies, or our full solutions portfolio.