ARTIFICIAL INTELLIGENCE / CONNECTED TO YOUR BUSINESS
Give customers and teams a useful way to work with your knowledge. We build AI assistants and agents connected to approved documents, systems and workflows—with source references, clear permissions and human handover.
For customer experiences, internal teams and the work in between. Built around your business, wherever you operate.
Answers shaped by the sources you approve.
The right information for the right person.
Clear escalation and approval paths.
THE POSSIBILITIES, IN PRACTICE.
See how the experience changes with the job. Open an example, try the questions and prepare a sample handover.
Product and policy guidance, with a clear support handover.
Procedures and ownership brought into one readable answer.
A clear project handover, prepared for a person to approve.
Original interface concepts with fictional brands and sample data. These previews demonstrate the experience; your assistant is designed around your business, sources and permissions.
01 / FIND YOUR STARTING POINT
Start with a job that matters to your business. We define the knowledge, decisions and next steps the assistant needs before choosing the model or interface.
SERVICE / FROM QUESTION TO NEXT STEP
Bring product guidance, policies and service information into one considered experience. When a question needs a person, carry the context into the handover.
Your proposal confirms the systems, deliverables, access, third-party costs and handover for your project.
TEAMS / LESS SEARCHING, MORE CONTEXT
Give your team a practical starting point for policies, procedures and project information. Shape access around the way people work and the information they are allowed to see.
Your proposal confirms the systems, deliverables, access, third-party costs and handover for your project.
WORKFLOWS / A CONSIDERED NEXT ACTION
An agent can prepare a request, look up a record or suggest the next action. We define its tools and permissions carefully, with human approval where the task requires it.
Your proposal confirms the systems, deliverables, access, third-party costs and handover for your project.
02 / HOW THE KNOWLEDGE BECOMES USEFUL
We connect the question to the right information, decide what the assistant is allowed to use and design a useful way to continue.
Knowledge access, source selection and the task are considered together.
Source references, a clear next step and a human handover when it is needed.
Illustrative architecture. The data sources, access controls and model provider are confirmed for the project.
03 / THE ENGINEERING BEHIND THE SERVICE
A model is one part of the work. Your data, permissions, interface and operational process decide whether the result is useful to your business.
We map the sources, organise the content and agree how it stays current. The assistant should help people understand the information and make the next step clear.
Source access, tool permissions, escalation and approval paths are defined before release.
We use agreed test questions to check usefulness, source support, refusal behaviour, latency and usage costs.
Model providers, retrieval and application tools are selected for the agreed project.
Anthropic
Python
PostgreSQL
FastAPIMODEL CHOICE / BUILT AROUND THE JOB
We evaluate model options against your tasks, knowledge, data requirements and expected use. The recommendation follows the work.
HOSTED APIAPI-based assistants, tool use and structured responses, evaluated against the tasks your business needs to complete.
Task quality, response format and the tools involved.
HOSTED APIDocument-led assistance and tool-connected workflows, assessed using your own knowledge, questions and working examples.
Source support, document handling and useful answers.
HOSTED APIText, document and multimodal use cases, chosen around the inputs and experience your project needs.
Input formats, response quality and the target experience.
OPEN-MODEL OPTIONSOpen-model options with managed or self-hosted deployment, where the model licence, infrastructure and operating requirements fit.
Hosting, model terms and the resources needed to operate it.
Quality, latency, data handling, usage costs and hosting are reviewed together. Llama and other open-model options have their own licences and operating requirements; the selected model and terms are confirmed in your proposal.
WHERE THE EXPERIENCE LIVES
Build for the places your customers and teams use. The experience, identity checks and knowledge access are planned for each audience.
Help at the point of decision.
Branded assistance inside your website or customer portal, with useful answers, guided next steps and a clear route to your team.
Public or authenticated customer journeys.
Continue the conversation.
Customer service through your business messaging setup, with agreed message flows, routing and a handover to the right person.
Customer-specific service journeys. Meta setup and messaging rules apply.
Useful knowledge, where the team works.
Approved team knowledge and internal requests, available within the workspace your people already use to collaborate.
Workspace app, agreed channels and scoped permissions.
Bring assistance into the working day.
An internal assistant for employee questions, guided requests and team handovers in Microsoft Teams.
Tenant installation and access matched to your organisation.
Each channel is planned as part of your project scope. Account setup, access permissions, platform approvals and any messaging or service fees are confirmed before implementation.
WHAT THE BUILD INCLUDES
Compare the work behind a tailored assistant with a typical out-of-the-box chatbot setup.
| What matters | UMW AI Agents | Generic Chatbots |
|---|---|---|
| Business knowledge | Sources, access rules and update routines mapped to your business. | Often starts with a FAQ, uploaded content or a general prompt; source controls vary. |
| Evidence and answer quality | Source references where supported, agreed test questions and a defined escalation path. | Evidence, testing and handover depend on the product and its configuration. |
| Actions in your systems | Scoped API tools, permissions and approval steps designed around the process. | Frequently focused on answering or routing; deeper actions may need additional integrations. |
| Deployment channels | Website Widget, WhatsApp Business API, Slack or Microsoft Teams, as agreed in the project. | Commonly starts with a website widget; other channels depend on platform support and plan. |
| Model and operating choices | OpenAI, Anthropic, Gemini or appropriate open-model options evaluated for the job. | Model choice, hosting and usage rules are usually defined by the platform. |
| Handover and continuity | Agreed custom source files, documentation, evaluation cases and support options. | Portability, access and ongoing changes depend on the provider, plan and licence. |
Capabilities vary by vendor and configuration. Your proposal defines the exact knowledge sources, channels, tools, deliverables and operating responsibilities for the UMW implementation.
Let’s shape your AI project04 / HOW WE WORK TOGETHER
Work is organised around an agreed scope, practical review points and the people who will use and operate the result.
Choose the audience, use case and decisions the assistant should support.
Agree sources, permissions, content preparation and update routines.
Review answers, tool use, escalation and the customer-facing interface.
Receive the agreed source files, documentation and operating guidance.
We collaborate remotely with businesses across industries and markets. Written updates, shared review windows and a defined support scope keep the work clear across time zones.
A clear way to maintain the approved sources and identify who owns the content.
Model, hosting and service charges explained alongside the implementation scope.
The agreed source files, configuration, test scenarios and guidance for your team.
Support and review options shaped around how the assistant is used after launch.
05 / SHAPE YOUR PROJECT
Tell us who the assistant should help, what it should know and what you want it to do. We will shape the scope before recommending an implementation.
UMW will review the brief and follow up using the contact details you provided. The next step is to agree the scope and prepare a proposal.
hello@umwrecordingsinc.com06 / BEFORE WE START
The practical details of planning an AI project with UMW.
Let’s shape your projectYes, through the sources and access rules agreed for the project. We define who can see each source, how updates are handled and what data reaches the selected model provider. Provider retention, hosting and contractual requirements are reviewed as part of the architecture.
We design retrieval around approved sources, ask the assistant to identify its evidence, and define when it should ask a question or hand over to a person. We test agreed scenarios before launch. AI output still needs appropriate review, especially for decisions with meaningful consequences.
It can, when the required APIs and permissions are available and included in the scope. We define each tool, its allowed actions, approval steps and failure behaviour. The proposal distinguishes information lookup from actions that change a record or start a business process.
We review your current environment first. The choice depends on your APIs, content, privacy requirements, channels and expected usage. We explain the practical options and their costs rather than requiring a particular provider for every project.
The agreed custom source files, configuration documentation, knowledge update guidance, evaluation cases and operating instructions. Third-party platforms and models keep their own terms. Training, monitoring and ongoing improvements can be included in the support scope.