Custom AI agents, document automation, intelligent workflows, and data extraction tools that reduce manual work and help your team make better decisions faster.
Sijzi builds AI solutions that are designed to solve specific, real problems — not showcase technology for its own sake. We focus on the parts of your operation where AI can reduce hours of manual work, surface insights your team would otherwise miss, or handle tasks that don't need a human in the loop every time.
Our work covers a broad range of applied AI: conversational agents and internal assistants built on LLMs, document processing pipelines that extract and structure data from PDFs, emails, and forms, retrieval-augmented generation (RAG) systems that give your team reliable answers from their own knowledge bases, and workflow automation that connects AI outputs to the tools your team already uses.
Every solution is scoped practically — we start with the problem, identify where AI adds real value, build something that works with your existing data and systems, and make sure your team can actually use and trust it. We don't start with a model and look for problems to apply it to.
We build LLM-powered agents and internal chatbots that handle support queries, answer questions from your knowledge base, assist staff with internal processes, and take actions via tool calls and integrations.
We build pipelines that process PDFs, emails, contracts, invoices, and forms — extracting structured data, classifying documents, and routing outputs into your systems automatically.
We build retrieval-augmented generation (RAG) systems that let your team query internal documents, SOPs, product documentation, or any proprietary knowledge base with accurate, source-cited answers.
We connect AI capabilities to your existing tools — CRMs, ticketing systems, databases, communication platforms — creating intelligent workflows that route, enrich, summarise, and act on data without manual intervention.
We build models and dashboards that surface patterns in your operational data — forecasting demand, identifying churn risk, flagging anomalies, or scoring leads — to support faster, better-informed decisions.
We build AI-assisted review workflows where automation handles the volume and humans handle the edge cases — including confidence scoring, escalation routing, and audit trails for compliance-sensitive processes.
We start by identifying the specific problems worth solving with AI — mapping workflows, understanding data availability, assessing feasibility, and setting clear success criteria before any development begins.
We review your existing data sources, documents, knowledge bases, and systems to design the right AI architecture — choosing models, retrieval strategies, tools, and integrations based on your actual constraints.
We build a focused prototype to validate the core AI behaviour — testing accuracy, latency, edge cases, and integration points — before committing to a full build. This keeps risk low and surfaces real issues early.
We build the complete solution — AI logic, backend services, integrations with your existing tools, and any user-facing interface — with proper error handling, logging, and fallback behaviour built in from the start.
We run structured evaluations against real data, test failure modes, tune prompts and retrieval pipelines, and refine the solution until it meets the accuracy and reliability thresholds agreed at the start.
We deploy to production, set up monitoring for AI quality metrics (not just uptime), and stay involved for the first iteration cycle — adjusting the system as it encounters real-world usage patterns.
Tell us about your idea. Our engineers, designers and AI strategists will map a clear path from concept to launch — usually within a single discovery call.
