You have a use case in mind — a customer-support copilot, a contract summarizer, an internal knowledge assistant, a sales-email drafter — and you need a partner who can build it, secure it, and keep it running on your Pittsburgh team's terms. This page walks through how PGH Networks, an LLM app development agency in Pittsburgh, takes a business problem from whiteboard sketch to a working production application your staff actually uses.
Most generative AI projects don't fail at the model — they fail at the integration, the data plumbing, and the change management around them. Our process is built to head off those failure points before a single token is generated.
The hardest part of an LLM app is rarely the prompt — it's the data access, the guardrails, and the workflow it has to fit into.
Step 1: Discovery and use-case scoping
We start with a working session at your office in Pittsburgh, the South Hills, Cranberry, Monroeville, or wherever your team is based — or remotely if that's faster. The goal is to separate the AI ideas that will pay back from the ones that look impressive in a demo and stall in production. We map the proposed use case against your existing systems (Microsoft 365, Salesforce, ERPs, line-of-business apps), the data it would need to read or write, and the regulatory exposure involved (HIPAA for healthcare clients in Allegheny and Westmoreland counties, CMMC for defense suppliers around the Mon Valley, GLBA for financial firms downtown).
By the end of discovery you have a one-page scope: the problem, the user, the data sources, the success metric, and a fixed-fee build estimate.
Step 2: Architecture, model selection, and guardrails
TL;DR: As an LLM app development agency in Pittsburgh, we design each build around your data boundary first and pick the model second — not the other way around.
Model choice is a downstream decision. Before we pick between Azure OpenAI, Anthropic Claude, AWS Bedrock, or a self-hosted open-weight model like Llama or Mistral, we settle the architecture: where your data lives, what crosses a tenant boundary, how retrieval-augmented generation (RAG) is structured, and what the audit trail looks like. For regulated clients we default to Azure OpenAI inside your own tenant so prompts and completions never train a third-party model.
Guardrails we build in by default:
- Prompt-injection filtering on any user-facing input
- PII and PHI redaction before retrieval
- Role-based access so the LLM can only see what the user could see
- Full prompt/response logging for compliance review
Step 3: Build, integrate, and ground in your data
This is where most of the engineering happens. We build the application — typically a web app, a Teams or Slack bot, or an embedded panel inside an existing tool — and connect it to your real data through a vector store and connectors to SharePoint, network shares, SQL databases, ticketing systems, or industry-specific platforms. Grounding is what turns a generic chatbot into something that answers correctly about your policies, your contracts, your patients, your parts catalog.
We work in two-week iterations with a working demo at the end of each one. You see progress on real data, not slideware.
Step 4: Pilot with a real user group
A model that scores well on a benchmark but confuses your billing clerk is a failed project.
Before any company-wide rollout, we run a structured pilot with five to fifteen users — usually the team that originated the request. We instrument the app to capture thumbs-up/thumbs-down feedback, time-to-answer, and where users had to fall back to the old workflow. Two to four weeks of pilot data tells us whether to expand, refine the retrieval, or change the UX.
Step 5: Production rollout and managed operations
Because PGH Networks is a managed services provider first, an LLM app we build doesn't get tossed over a wall. We host it, patch it, monitor it, rotate keys, watch token spend, and update prompts and embeddings as your documents change. Your help desk calls the same number for an Outlook problem and an AI assistant problem.
Ongoing operations include:
- 24/7 monitoring and uptime SLAs
- Monthly usage and cost reports with optimization recommendations
- Quarterly model reviews as new versions ship
- Security patching and dependency updates
Who this is for
Small and mid-market organizations within roughly 75 miles of Pittsburgh — manufacturers in Beaver and Butler counties, healthcare practices in Allegheny County, professional services firms downtown and in the Strip, and defense and supply-chain firms working toward CMMC. If you have between 25 and 750 employees and a specific workflow you want to make faster or smarter with generative AI, you're in the right place.
Why PGH Networks
We are a Pittsburgh MSP with a dedicated custom AI applications practice, not a national agency parachuting in for a project. That means the same team that understands your network, your Microsoft 365 tenant, and your compliance posture is the team building your LLM app. When something breaks at 7 a.m. on a Tuesday, you call a local number and reach an engineer who already has context.
Next steps
If you have a use case in mind — even a rough one — the fastest way to start is a 30-minute scoping call. Bring the problem; we'll bring questions about data, users, and constraints, and you'll leave with a candid read on whether an LLM is the right tool and what a build would look like. Call PGH Networks or request a discovery session through pghnetworks.com to get on the calendar this week.