Web Business Development, based in Las Vegas, builds AI that answers questions from your own content, reads and sorts incoming documents, and carries data from one business app to the next without retyping. Projects usually start as one small pilot with a clear yardstick, and you decide which actions need a person's sign-off, such as emailing customers, spending money, or deleting records.
Each build targets a defined task with a named owner on your side. Start with one, prove it, then add the next.
Web Business Development's AI solutions are practical systems for small and midsize businesses: website and internal assistants that answer from approved content, automations that move data between existing apps, document extraction, and AI agents that pause for human approval. WBDI starts with one measurable pilot, tests accuracy on real examples, and keeps people responsible for decisions that matter.
A chat assistant for your site, built and tested to answer only from the pages, FAQs, and policies you approve. It uses retrieval-augmented generation (RAG), so it looks up your content before replying instead of relying on memory. When a question falls outside that material, or a visitor asks for a person, it collects contact details and hands off to your team with a summary.
Staff ask questions in plain English and get answers drawn from your handbooks, SOPs, price sheets, and product specs, each with a link to the source document. Access follows the roles you already use, so a new hire and a manager can share one assistant without seeing the same files.
We connect forms, inboxes, spreadsheets, and business apps using Zapier, Make, Microsoft Power Automate, or n8n. Fixed rules handle the predictable parts. AI steps handle the messy parts: reading a free-text request, tagging it, drafting a reply, or deciding which queue it belongs in.
Invoices, applications, contracts, and scanned forms rarely share a layout. An extraction workflow pulls the fields you need into structured data, writes a short summary for the reviewer, and flags missing or unreadable values instead of guessing at them.
Agents work through multi-step jobs, such as researching an inbound lead, assembling a draft quote, or updating several records after a sales call. They run with narrow permissions, log each step, and stop for a named person to approve anything that sends a message, spends money, or deletes data.
AI helps only when it can reach the right data. We integrate with CRMs such as HubSpot and Salesforce, with Microsoft 365 and Google Workspace, and with accounting tools like QuickBooks, using official APIs and connectors so nobody retypes information between systems.
Strong candidates share three traits: the task happens often, it follows a recognizable pattern, and a mistake can be caught before it reaches a customer.
Faster follow-up on new leads and less time staring at blank drafts.
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Quicker answers for customers, with people still owning the difficult cases.
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Fewer requests lost between email, spreadsheets, and scheduling tools.
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Less retyping and a clearer view of contracts and spending.
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Support for busy hiring managers, without handing hiring decisions to software. Several states and cities regulate AI in hiring, so we flag those rules for your attorney first.
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A clearer read on the business, with less time spent assembling it.
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AI projects stall when they start too big. WBDI begins with one process that happens often, follows a pattern, and costs real hours. We map each step as it runs today, mark where a person must stay involved, and agree on how success will be measured, such as minutes per task or error rate, before any build begins.
The pilot is checked against an evaluation set of real examples from your business, with a human-in-the-loop review step and per-task cost tracking from day one. If the numbers hold up, we widen it. If they do not, you get a plain explanation of what fell short and what we would try instead.
What a Pilot Delivers
Describe it in a sentence or two. We will tell you whether it makes a good pilot and what we would need to test it.
An assistant that leaks a file or obeys a hidden instruction costs more than it saves. These controls are part of every build, not an upgrade you add later.
We use business and API plans whose terms keep your inputs out of model training by default, and we limit data retention where the provider and your project allow it. Health, payment, and other regulated data stays out unless the service supports the agreements those rules require.
An assistant should never become a shortcut around your permissions. Answers respect the same roles and groups your systems already use, and public website assistants never draw from internal documents.
Questions, retrieved sources, tool actions, and approvals are logged with timestamps and kept for a retention period you choose. When someone asks why the assistant said something or who approved a change, the record has the answer.
Emails, web pages, and uploaded files can hide instructions aimed at an AI. We treat that content as untrusted, limit the tools an agent may call, keep sensitive actions behind approval, and test with deliberately hostile inputs before go-live.
Every system is scored against real questions and documents with known correct answers. The same test set runs again after each change to prompts, models, or source data, so a drop in quality shows up quickly.
Customers should know when they are talking to AI and how to reach a person. We write the disclosure and handoff wording with you and flag the rules your attorney should confirm for your industry and state.
WBDI is not tied to one AI vendor. We choose a model for each task and can mix several in one workflow, so a simple sorting step does not pay for a large model's reasoning.
Because the integration layer stays separate from the model, switching providers later is usually a configuration change plus a fresh round of testing, not a rebuild.
Public benchmarks rarely look like your documents. We run candidate models against your evaluation set and compare how often each one gets the answer right.
Sorting and extraction often run well on smaller, cheaper models. The most capable models are saved for steps that need careful reasoning, which helps keep monthly usage costs predictable.
Some work can go straight to a provider's API. Some belongs inside your own Microsoft Azure, AWS, or Google Cloud account. Open-weight options, including Llama and Mistral models, can even run on servers you control.
A website chat needs replies in seconds; an overnight batch of documents does not. Latency targets help decide both the model and where it runs.
Direct answers about scope, data, accuracy, and the AI tools you may already own. Different situation? Describe it and we will answer plainly.
Ask About Your ProcessA first AI project with WBDI is a single pilot aimed at one repetitive process, such as sorting inbound leads or pulling data from invoices. WBDI maps the current steps, agrees on success measures with you, builds the pilot against your real tools, and tests it on your own examples. With the results in hand, you decide whether to scale it, adjust it, or stop.
WBDI designs AI to remove repetitive steps from jobs, not to remove the people doing them. Typical targets are retyping, sorting, first drafts, and hunting for documents. Staff review the output, handle exceptions, and make the judgment calls, and higher-stakes actions, such as sending a quote or changing financial records, wait for a named employee's approval. The aim is to give skilled people more time for work that needs them.
Not through WBDI's work. WBDI builds on paid business and API plans whose published terms exclude customer inputs from model training by default, and it turns on shorter data-retention options where a provider offers them and the project allows. WBDI does not use your data to train any model without your written permission, and it documents the provider settings in use so your team can verify them.
WBDI is model-agnostic and chooses per task. A project might use models from OpenAI, Anthropic's Claude, or Google's Gemini family, or an open-weight model like Meta's Llama or one from Mistral AI when data must stay on infrastructure you control. WBDI compares candidates on accuracy with your examples, cost per task, privacy needs, and response speed, then records why each model was picked so it can be swapped later.
WBDI reduces made-up answers, often called hallucinations, by grounding each assistant in your approved content, requiring it to cite the source behind every answer, and instructing it to say it does not know when the sources are silent. Each build is tested against an evaluation set of real questions before launch and after every change. No AI system is perfect, so higher-risk outputs keep a human review step.
Yes. WBDI starts from the AI licenses a company already owns. With Microsoft 365 Copilot, that can mean connecting company data, building agents in Copilot Studio, and tightening SharePoint permissions so Copilot only surfaces what each person should see. With ChatGPT Enterprise or another company ChatGPT plan, it can mean custom GPTs, connections to approved data, and usage guidelines. WBDI writes custom code only where those tools fall short.
Tell us which process costs your team the most hours. WBDI will review the steps, the data involved, and the tools you already pay for, then outline a pilot you can measure.