# Bryant Technologies, Inc. > Bryant Technologies, Inc. is a Washington, DC company providing AI solutions, software services and IT talent. It builds AI assistants and chatbots, company-wide knowledge bases with retrieval-augmented generation (RAG), custom Model Context Protocol (MCP) servers, private AI environments in clients' own AWS, Azure or Google Cloud accounts, private and local AI on open models (Ollama, vLLM, llama.cpp), custom AI and cloud infrastructure on Microsoft Azure, AWS and Google Cloud, and enterprise AI integrations, and it places screened IT consultants on client projects. - Address: 1717 K St. NW, Suite 900, Washington, DC 20006, United States - Phone: +1 202-747-3355 · Email: info@bryant-technologies.com - Brands: Digital Arts Media Services (https://digitalartsmediaservices.com/) and Sectorly.ai (https://www.sectorly.ai/en/) ## AI services ### AI Assistants & Chatbots Assistants for your website, by voice and inside your systems, that answer from your approved information and live data — and hand over to people when they should. More: https://bryant-technologies.com/ai-solutions/ai-assistants/ ### Company Knowledge Base & RAG One AI knowledge base for the whole company — documents, wikis and records indexed with their permissions, so every assistant and AI tool answers from approved sources, with citations. More: https://bryant-technologies.com/ai-solutions/knowledge-base-rag/ ### Custom MCP Servers Secure Model Context Protocol servers that let Claude, ChatGPT, Gemini and your own agents work with your business systems — with permissions, redaction and audit logs. More: https://bryant-technologies.com/ai-solutions/mcp-servers/ ### Private & Local AI Open models running on your own servers — Ollama, vLLM and llama.cpp — for image recognition, bulk document parsing and audio transcription, with no data leaving your network. More: https://bryant-technologies.com/ai-solutions/private-local-ai/ ### Private AI Cloud on AWS, Azure & Google Cloud A private AI environment in your own AWS, Azure or Google Cloud account — isolated networking, your encryption keys, approved models and separate knowledge bases per department. More: https://bryant-technologies.com/ai-solutions/private-ai-cloud/ ### Custom AI Solutions & Cloud Infrastructure Custom AI applications and the cloud infrastructure to run them, on Microsoft Azure, Google Cloud and Amazon Web Services. More: https://bryant-technologies.com/ai-solutions/custom-ai-and-cloud/ ### Enterprise AI Integration Bringing AI into the systems your organization already runs — documents, CRM, collaboration tools and line-of-business applications — under enterprise governance. More: https://bryant-technologies.com/ai-solutions/enterprise-ai-integration/ ## Case studies ### AI Chatbot for Tessitura Brand: Digital Arts Media Services. Sector: Performing arts. A 24/7 box office assistant that works from live Tessitura data — shows, seats, bookings and memberships — with checkout on the venue’s own TNEW site. More: https://bryant-technologies.com/ai-solutions/case-studies/tessitura-ai-chatbot/ ### MCP Server for Tessitura Brand: Digital Arts Media Services. Sector: Performing arts. A governed bridge that lets box office, marketing, development and leadership teams ask Claude, ChatGPT and Gemini questions of live Tessitura data. More: https://bryant-technologies.com/ai-solutions/case-studies/tessitura-mcp-server/ ### Sectorly.ai Platform Brand: Sectorly.ai. Sector: Hospitality, healthcare, arts and services. A multi-industry AI platform: governed chatbots, a hotel concierge, appointment scheduling and AI-assisted practice management. More: https://bryant-technologies.com/ai-solutions/case-studies/sectorly-platform/ ## Brands ### Digital Arts Media Services "Your Tessitura, everywhere". Our Washington, DC brand for arts organizations and businesses: AI on Tessitura, websites and custom software. Website: https://digitalartsmediaservices.com/ ### Sectorly.ai "Intelligent tools for modern businesses". Our AI product brand: platforms for customer conversations, hospitality, appointments and medical practices. Website: https://www.sectorly.ai/en/ ## IT staffing Bryant Technologies augments client project teams with specialized technology consultants. Because BTI is also an application development company, senior technical staff screen every consultant against the project requirements, reducing turnover for hiring managers. Practice areas: quality assurance and business analysis, application development, information systems, and systems and telecommunications. ## Open positions (0) ## Blog posts ### Grounded AI Assistants: Chatbots That Don't Make Things Up Published 2026-10-19 · https://bryant-technologies.com/blog/grounded-ai-assistants/ Almost everyone has met a bad website chatbot. Some are rigid decision trees that can't understand a simple question. Others are general-purpose AI that answers confidently and wrongly, quoting a price that doesn't exist or a policy that was changed last year. Neither is acceptable when a customer asks *"are there seats left on Saturday?"*, *"what's your cancellation policy?"* or *"can I check in late?"*. The fix isn't a smarter model. It's a **grounded** assistant. ## What "grounded" means A grounded assistant answers only from sources you control: - **approved knowledge**: FAQs, policies, product and venue information that your team has reviewed; - **live lookups** in your own systems, such as availability, bookings or order status, through tools you allow; - **context**: which page the visitor is on, the current date and time, and (when they sign in) who they are. When none of those sources has the answer, a grounded assistant says so honestly and offers a way to reach a person. It never invents prices, availability, opening hours or policies. ## Five things a production assistant needs ### 1. A review process for what it knows Everything the assistant can say should pass through review before visitors see it, with automated checks for conflicting statements, gaps and duplicates. Every approved version should be kept, so you can compare changes or roll back. ### 2. Clear rules about actions Looking things up is low risk. Changing things, like adding seats to a cart, booking an appointment or updating a record, needs rules. A good assistant only uses the tools you've switched on, and asks the customer to confirm before anything changes. For payments, it hands over to your own checkout rather than taking card details in chat. ### 3. Honest handover to people Some questions need a human. The assistant should recognize them, say so plainly, and route the conversation to the right team, by email, Teams, Slack or your help desk, with the context attached so the customer doesn't have to repeat themselves. ### 4. Access control Not every assistant should be open to everyone. Hotel concierges should only work for current guests; member services only for members. Look for open, recognition and invitation-only modes, and access that expires with a stay, term or season. ### 5. Privacy by default Conversations should be kept only as long as you decide, data should be separated per organization, and the AI services underneath should be configured so your conversations aren't used to train their models. ## Beyond the website chat bubble The same grounded assistant can work across channels: a website widget, a full-page chat, a QR code in a hotel room or on a theater seat, a home-screen app, and **voice**, with hands-free calls that can switch language mid-conversation. Internal versions live in Microsoft Teams or Slack and answer staff questions from your own documentation. ## Two real examples - The [AI Chatbot for Tessitura](/ai-solutions/case-studies/tessitura-ai-chatbot/) gives performing arts patrons a box office that never closes. It answers from live Tessitura data, adds seats to the patron's cart and sends them to the venue's own TNEW checkout. - The [Sectorly.ai platform](/ai-solutions/case-studies/sectorly-platform/) runs grounded assistants for hotels, clinics, appointments and more on one governed core. ## Platform or custom build? For most customer-facing assistants, a configurable platform is the fastest route: you shape the assistant's knowledge and behavior without writing code. When the workflow is unusual, the systems are internal, or hosting rules require everything to run in your own cloud, a custom build makes more sense. Bryant Technologies does both. Read more on our [AI Assistants & Chatbots](/ai-solutions/ai-assistants/) page, or [tell us who your assistant should help](/contact/). ### Private AI with Local Models: Documents, Images and Audio Published 2026-10-19 · https://bryant-technologies.com/blog/private-ai-with-local-models/ Cloud AI services are remarkable, but they come with a condition many organizations can't accept: your data has to leave the building. For personnel files, medical and legal records, contracts, recorded calls or security footage, that's often a non-starter. And for very large workloads, like a backlog of scanned documents or years of recorded meetings, paying per call adds up quickly. The good news is that **open-weight AI models now run well on hardware you control.** With the right setup, you can get much of the value of AI without sending a single file to a third party. ## What "local AI" actually means A local AI system runs the models themselves on your own infrastructure: - **your own servers or workstations**, in your office or data center; - **GPU virtual machines in your own cloud account**, inside your network boundary; - or **air-gapped networks** with no internet connection at all. The models are open-weight models such as Llama, Mistral, Qwen and Gemma for text, plus dedicated vision models and Whisper-family models for speech. Because you run them, you choose the version, keep it as long as you need, and decide exactly where the data goes. ## The tools that make it practical Three open-source runtimes cover most needs: - **Ollama.** The quickest way to run open models on a server or workstation, with a simple local API. It's ideal for pilots, internal tools and modest workloads. - **vLLM.** High-throughput serving on GPU servers, built for many concurrent users or large batch jobs. - **llama.cpp.** Efficient inference on modest hardware, including CPU-only machines. We choose the runtime per workload and keep the models swappable, so you can move to a better model when one is released without rebuilding the system. ## Four workloads that run well locally ### 1. Bulk document parsing Large backlogs of PDFs, scans, forms and contracts can be turned into structured data: named fields, tables, classifications and summaries. A queue processes documents in the background, and results are written straight into your database or exported as JSON or CSV. ### 2. Image recognition Vision models can classify and tag images, detect objects, check inspection photos and read text from photos and scans (OCR). Typical uses include sorting incoming documents, cataloguing image libraries and flagging items that need a person's attention. ### 3. Audio and video transcription Whisper-family speech models produce accurate transcripts locally. On top of the transcript, a language model can identify speakers' turns, summarize meetings and calls, and build a searchable archive, all without recordings leaving your network. ### 4. Private assistants and search Retrieval-augmented generation (RAG) works fully on-premise: documents are indexed with embeddings computed on your servers, and a local language model answers questions with references back to the source documents. ## Keep a person in the loop Local models are very good at focused tasks, but no model is right every time. The systems we build report a confidence level for each result. Anything below a threshold goes to a **review queue**, where a person confirms or corrects it. That keeps automation fast on the easy majority and careful on the hard cases. The corrections also show you exactly where the model needs tuning. ## Local, cloud, or both? Local models aren't a replacement for every cloud AI use. For open-ended reasoning, the largest cloud models are still ahead. A common design uses both, behind one interface: - **local models** for sensitive data and high-volume, repetitive work; - **cloud models** for tasks where the data is cleared to leave and the extra capability matters. Your applications don't need to know which one answered. ## What you need to get started - **A defined workload.** For example, "extract these twelve fields from our supplier contracts" or "transcribe and summarize our weekly calls". - **A sample set.** A few hundred documents, images or recordings, plus examples of the correct output. - **Hardware sized to the job.** Small models run on a workstation; bulk jobs and many users need GPU servers. We measure your workload during a pilot and recommend hardware from the results rather than guessing up front. Bryant Technologies designs, installs and runs private AI systems on your hardware or private cloud. Learn more on our [Private & Local AI](/ai-solutions/private-local-ai/) page, or [contact us](/contact/) with a sample of what you need to process. ### What Is an MCP Server? Connecting AI Tools to Your Business Systems Published 2026-10-19 · https://bryant-technologies.com/blog/what-is-an-mcp-server/ Most teams already use an AI assistant every day. They draft emails in ChatGPT, summarize documents in Claude, or ask Gemini to explain a spreadsheet. What those assistants usually can't do is work with the systems that hold the organization's real data: the CRM, the ticketing system, the finance application, the document library. The **Model Context Protocol (MCP)** is how that gap gets closed, and an **MCP server** is the piece you build to close it safely. ## MCP in one paragraph MCP is an open standard, introduced by Anthropic in 2024 and now supported by OpenAI, Google, Microsoft and others, that lets AI assistants use external tools and data. An MCP server sits next to one of your systems and publishes a short list of named *tools*, such as "check availability", "find this customer" or "summarize this ticket". Any MCP-capable assistant can discover those tools and call them. The server does the actual work against your system and returns only the result. The important detail: **the AI never talks to your system directly, and never sees its credentials.** It only sees the tools you chose to publish. ## Why not just give the AI an API key? It's tempting to point an AI assistant straight at an API. It's also why most security teams say no. Raw API access has three problems: 1. **It's all or nothing.** The assistant gets every field and every operation the account can reach, including data it doesn't need and actions it should never take. 2. **There's no record of who asked for what.** Calls happen under one service account, so you can't tell which person's question led to which change. 3. **Sensitive data leaks into prompts.** Whatever the API returns, payment details and private notes included, is sent to the AI provider. An MCP server fixes each of these by putting a governed layer in between. ## What a well-built MCP server does A production MCP server is more than a thin wrapper. The ones we build at Bryant Technologies include: - **Identity.** Staff sign in with single sign-on (Microsoft Entra ID, Google Workspace or Okta); systems use API keys; public tools need no sign-in but only see public information. - **Permissions per tool, role and field.** The box office sees orders, marketing sees sales trends, leadership sees summaries. Each role only gets the tools it needs. - **Redaction.** Payment details, dates of birth and private notes are removed on the server, before anything reaches an AI model. - **Human approval for changes.** Read tools run immediately; anything that writes, such as a note, a contact update or a reservation, waits for a person to approve it. - **Audit logs and limits.** Every call is logged with who asked, what ran and what changed. Rate limits and query caps protect the underlying system. ## Tools, not endpoints The biggest design decision is *which tools to publish*. A good tool is shaped around a question people actually ask, not around the structure of your API. Compare a raw endpoint like `GET /constituents?filter=...` with a tool called `brief_me_on_donor(name)`. The second one returns exactly what a development officer needs before a meeting: giving history, recent contact, open asks. It hides everything else. It's also far easier for an AI to use correctly. We usually start with a handful of read-only tools for one team, measure how they're used, and add more, including write tools with approval, once people trust the results. ## Where the server runs An MCP server is a small service, so it can live wherever your systems do: - in your own **Azure, AWS or Google Cloud** account, next to the system it connects to; - on your own servers, including networks with no internet access, paired with [local AI models](/ai-solutions/private-local-ai/); - or as a service managed by us. Either way, your system's credentials stay on the server. ## One integration, every AI tool Because MCP is a standard, one server works with Claude, Claude Code, ChatGPT (through custom connectors), Gemini-based tools, IDEs such as Cursor and VS Code, and the agents and apps you build yourself. Instead of a different plugin for every AI product, you maintain one governed integration. The same server can also power customer-facing assistants. Our [MCP Server for Tessitura](/ai-solutions/case-studies/tessitura-mcp-server/) gives arts-organization staff governed access to live ticketing and donor data, and also feeds the [AI Chatbot for Tessitura](/ai-solutions/case-studies/tessitura-ai-chatbot/) that answers patrons around the clock. ## How to get started 1. **Pick one team and one system.** Write down the ten questions they'd most like to ask. 2. **Design the tools.** Decide what each tool returns, who can use it and what must stay private. 3. **Build against a test environment.** Prove the tools on safe data first. 4. **Connect the AI tools your team already uses**, then train people and watch real usage. 5. **Expand** to more tools, more teams and more systems on the same foundation. If you'd like help planning your first MCP server, see our [Custom MCP Servers](/ai-solutions/mcp-servers/) service or [get in touch](/contact/).