Applications, conversations, knowledge and automation
Have a product idea, buried documents, repetitive work or too many routine questions? Identify where AI can help, choose the right owner — including RAG and knowledge systems under Custom AI Solutions — and define a build you can evaluate.
Prefer a specialist page? Explore AI Applications, AI Chatbots, Voice Agents, Custom AI Solutions, or Business Automation.
Looking for a spoken conversation agent? Explore Voice Agent.
01Starting points
Starting points
Start with the person and the task — not the model name. Match the situation below to a service, then discuss the simplest useful scope.
01Our product needs an AI feature
Define the user task, expected output and integrations before choosing architecture. Broader software builds may also involve Development.
Relevant serviceAI Applications
Discuss this situation02Customers or staff ask the same text questions
Help people find answers through a text conversation, with a path to a person when needed. Spoken conversations belong on Voice Agents.
Relevant serviceAI Chatbots
Discuss this situation03Routine calls interrupt the team
Handle routine spoken questions and collect useful context. Browser demo and production telephony are different scopes — see the Voice Agent page.
Relevant serviceAI Voice Agents
Discuss this situation04Useful knowledge is buried in documents
RAG (retrieval-augmented generation) uses your approved documents and systems to answer or draft — it is not automatic model training and not a guarantee of accuracy. Custom AI Solutions owns source design, permissions, citations, freshness and evaluation for that job.
Relevant serviceCustom AI Solutions
Discuss this situation05Repeated data entry and handoffs between tools
Connect steps across tools. Use AI only where interpretation helps — predictable rules often do not need a model.
Relevant serviceBusiness Automation
Discuss this situation06Not sure where to start
Describe the users, the task and what “good” looks like. Discovery helps pick the simplest useful service without forcing every AI category.
Relevant serviceNot Sure Yet
Discuss this situationScroll to explore
06 situations
Websites, apps and business systems without an AI feature remain under Development. We will say when AI is not the right starting point.
02Selected work
Portfolio
Projects where Buildaze contributed AI product features, retrieval or operational tooling. Read each case study for measurement notes — outcome figures are published when evidence is approved.
Relevant to AI Applications, internal copilots and LLM integrations with RAG.
A SaaS support team needed drafted replies with account context. Buildaze delivered a production web application feature that uses retrieval and live integrations — agents review before send.
RAG grounded drafts + human review (see case study)
Relevant to Custom AI Solutions, document intelligence and decision-support interfaces.
Underwriters spent hours summarizing claims. Buildaze built operational software that summarizes claims and surfaces risk signals for day-to-day use — with human review in the loop.
Docs document intelligence + review (see case study)
Relevant to AI Applications and knowledge retrieval inside a shopping experience.
Shoppers could not find products when search ignored attributes. Buildaze cleaned catalogue data, built a retrieval layer and shipped a shopping experience integration.
Search attribute-aware retrieval (see case study)
01Application / copilot
Relevant to AI Applications, internal copilots and LLM integrations with RAG.
A SaaS support team needed drafted replies with account context. Buildaze delivered a production web application feature that uses retrieval and live integrations — agents review before send.
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03AI services
AI services
Five focused destinations. Capabilities such as RAG, LLM integrations, document intelligence, internal copilots and workflow agents are covered under the owners below — not as thin extra pages.
Start with the user task, expected output and integration needs before choosing the model or architecture. LLM integrations and product copilots often live here when the deliverable is a usable application interface.
Best fit: Teams shipping a new AI product experience or adding search, summarization or assistant features inside an existing app.
Detailed knowledge/RAG systems are owned by Custom AI Solutions. Generic website or app builds without AI remain under Development.
Map conversation goals, prepare knowledge, implement the chatbot interface, define handoff and test representative conversations. Website chat does not automatically include every messaging channel.
Best fit: Customer support, lead intake or staff knowledge questions that should stay in a text channel.
Spoken conversations belong on Voice Agents. Deep RAG methodology lives on Custom AI Solutions — chatbots can use approved documents when scoped.
Configure business information, test realistic conversations and implement the agreed channel. Production handoff and telephony are project-scoped — not assumed from the public demo.
Best fit: Businesses that need routine calls answered with approved information and clear escalation.
The browser demo is not a telephone deployment. Appointment requests are not confirmed calendar bookings unless that integration is scoped.
Define the output, inventory approved sources, design retrieval with permissions and citations where useful, then evaluate on representative hard cases before treating a demo as production-ready.
Best fit: Teams whose core problem is grounded retrieval, document workflows, internal copilots or LLM systems tied to a specific business task — not only a chat skin.
RAG is not model training and not guaranteed accuracy. Chat UI alone may sit on AI Chatbots or Applications. Fine-tuning, computer vision and compliance certifications are not assumed. Dedicated evaluation-only consulting is a future specialist only when capability is confirmed.
Map the workflow, choose a bounded starting scope, implement triggers and actions, and define exception handling. AI agents that carry out workflow steps belong here when the buyer intent is process automation.
Best fit: Teams repeating data entry, routing, follow-ups or record updates across systems.
Not every automation needs AI. Tool names such as n8n, Make or Zapier are only claimed when confirmed for a project. Autonomous agents are not an unlimited promise.
LLM integrations, RAG, document intelligence, internal copilots and scoped AI workflow agents appear under the owner that matches the deliverable — with brief mentions and links from related pages.
04How we work
How we work
Discover the task → define scope and evaluation → build and test → hand over with ownership and operating considerations. Fixed delivery dates are not promised before requirements are clear.
We clarify who uses the system, which inputs and outputs matter, and what integrations or permissions exist.
What we review
Outcome
A shared definition of the problem to solve.
Useful AI work defines how quality will be judged. We do not guarantee zero errors, automatic growth or full replacement of human oversight.
05Why Buildaze
Working practices
Preserve evaluation, ownership and honest limits — without inventing certifications or uptime guarantees.
A single impressive answer is not an evaluation set.
Representative examples and acceptable performance belong in scope so demos are not mistaken for production readiness.
Share the users, the task and the systems involved. We will suggest a practical starting service.
AI only where it adds value.
06Common questions
AI FAQs
Short answers about service choice, integrations and when AI is unnecessary.
Start with the problem and the person using the solution. An application, chatbot, voice agent, knowledge/RAG system or workflow may be the right fit; discovery helps define the simplest useful scope.
Include the goal in the enquiry form and we will answer in the context of your systems and data.
07Related services
08Contact
Share who will use the solution, the task they need to complete and which systems or documents are involved. Do not send confidential credentials through the form.
