Off-the-shelf chatbots and generic SaaS add-ons rarely solve the workflow problems that actually slow your team down. Our custom AI application development practice builds bespoke LLM apps, internal copilots, and document-aware tools that fit the way your business already operates — and we stand behind them with the security, integration, and support discipline of a managed services provider. You walk away with software your staff will actually use, not a proof of concept that stalls after the demo.
TL;DR: We design, build, and operate custom AI software grounded in your data, your systems, and your compliance reality — not a one-size LLM wrapper.
A buyer scenario you'll probably recognize
A 120-person professional services firm in the South Hills wants to cut the time analysts spend digging through prior engagements, contracts, and email threads. They've tried a public chatbot and a couple of vendor "AI features" baked into existing tools. The chatbot hallucinates client names. The vendor features can't see across systems. Leadership is convinced AI should help here, but no one on staff has the bandwidth to design, secure, and ship a real internal application.
This is the situation we are built for. We come in as an advisory-grade engineering partner: we map the workflow, identify where a language model genuinely adds leverage (and where it doesn't), and then build a focused application — retrieval-augmented, permission-aware, and integrated with the systems your people already live in. The output is not a science project. It is a maintained piece of software with an owner, a runbook, and a roadmap.
What we build
Our engagements typically fall into a few patterns, though every build is scoped to the client's data and goals.
Internal copilots and assistants
Private, role-aware assistants that draw on your SharePoint, file shares, CRM, ticketing system, ERP, or knowledge base. They answer staff questions with citations to the underlying document, draft first-pass deliverables, and route work. Access is governed by your existing identity provider, so a junior associate sees only what a junior associate is allowed to see.
Document and data intelligence applications
Apps that ingest contracts, claims, intake forms, lab reports, or operational PDFs and turn them into structured output: summaries, extractions, comparisons, exception flags. We pair LLMs with deterministic validation so the result is auditable, not just plausible-sounding.
Customer-facing chat and intake tools
Branded chat experiences embedded in your website or portal, scoped to your products, policies, and service catalog. We build guardrails, escalation paths to human agents, and analytics so you can see what customers are actually asking — and where the model should not be answering at all.
Workflow automation with an AI core
Where appropriate, we wrap models inside larger automations: an inbox triage system, a quoting assistant, a back-office reconciliation tool. This is where our broader AI workflows and managed IT practices reinforce each other.
How we differ from typical providers
Most firms offering AI development right now fall into two camps: pure consultancies that hand off a prototype and disappear, or generalist dev shops bolting an API call onto a web form. Neither model holds up once an application has to live inside a regulated business with real users.
Production AI is 20% model and 80% integration, identity, evaluation, and operations — and that is exactly the work most prototypes skip.
We approach every build as software that has to be operated. That means we plan for evaluation harnesses to measure model output quality over time, observability so we can see when prompts drift or costs spike, and a clear ownership model after launch. Because we are also your potential MSP and security partner, the application lands inside a managed environment with patching, backup, identity hygiene, and incident response already handled. You are not stitching together four vendors to keep one app alive.
We also say no to AI when AI is the wrong answer. A surprising amount of "we need a chatbot" turns out to be "we need clean data and a better form." Telling you that up front is part of the job.
Our delivery approach
Engagements start with a paid discovery: a structured two-to-four-week assessment where we interview stakeholders, inventory data sources, evaluate feasibility, and produce a written architecture and cost model. From there, builds typically run in six-to-twelve-week increments, with a working application in users' hands before the end of the first phase.
Throughout, we focus on three things competitors tend to underweight: data readiness (your AI app is only as good as the corpus behind it), evaluation (how do we know the answers are right, and how will we know if that changes?), and change management (how do we get your people to adopt this without a mandate from the top).
Why PGH Networks
We are a Pittsburgh-based managed services provider with clients across the metro — from the Strip District and Downtown to Cranberry, Wexford, Robinson, Monroeville, the South Hills, and out into Washington, Butler, and Westmoreland counties. Our engineers live here. Discovery sessions can happen in your conference room.
Because we operate as a full-stack MSP, our custom AI application development work is informed by daily exposure to the security and compliance frameworks our mid-market clients answer to: HIPAA for healthcare and behavioral health, SOC 2 for SaaS and professional services, CMMC for defense suppliers, and PCI for retail and hospitality. We design AI applications that respect those boundaries from day one — data residency, logging, model selection, and access control are part of the architecture, not an afterthought.
We also maintain adjacent practices in AI workflows and AI strategy and advisory, so a custom build can plug into a broader roadmap rather than sitting on an island.
Book a discovery call
If you have an internal process that feels like it should be automatable with modern AI but isn't a fit for any product on the market, that is the conversation we want to have. A discovery call is 30 minutes, no slide deck, and ends with a clear sense of whether a custom build makes sense for you and what the next step would cost.
Frequently asked questions
How is this different from your AI Workflows service?
AI Workflows applies models inside existing tools and automation platforms — think a smarter Power Automate or Zapier flow. Custom AI application development produces a standalone application with its own UI, data model, and user base. Many clients eventually use both.
Do you host the application, or do we?
Either. We commonly deploy into the client's Azure, AWS, or Google Cloud tenant so data and billing stay under your control, but we can also host and operate the application for you under a managed agreement.
Which AI models do you use?
We are model-agnostic and select based on the use case, data sensitivity, and budget. That includes Azure OpenAI, Anthropic Claude, Google Gemini, and self-hosted open models when data residency or cost demands it.
What does a typical project cost?
Discovery engagements generally run in the low five figures. Build phases vary widely with scope, but most first-version applications land between $40,000 and $150,000, with ongoing operations billed monthly. We will give you a written estimate before any build work starts.
Will our data be used to train someone else's model?
No. We architect every engagement so your prompts, documents, and outputs are excluded from provider training, and we put that in writing as part of the contract.