Your workflow, not a demo. AI that actually runs.

Who ends up on this page

Usually someone at a 20–80 person professional services firm — a law office, an insurance agency, an accounting practice, a real estate operation — who got handed an AI project by their owner or managing partner. The directive was vague: "we should be doing something with AI." The problem is specific: intake forms that take someone two hours to process, data that gets copy-pasted between your practice management system and your CRM every morning, email queues where the same questions get drafted from scratch a hundred times a month.

Sometimes you've already tried something. You enabled your software vendor's AI feature. It didn't change much. Or you looked at enterprise AI tools and they were priced for companies ten times your size. You're not skeptical of AI — you're skeptical that AI can be delivered at a scope that makes sense for your operation.

That's the right skepticism to bring to this conversation.

What integration actually means

An AI feature that lives inside one chat window is a toy. An AI process that picks up a real input — a client intake form, an email, a document, a record in Clio or QuickBooks or your practice management system — produces a real output, and is observable when it’s wrong: that’s integration.

This usually means connecting an LLM to your existing systems via their APIs, wrapping the output in validation logic, logging what the model produces so you can audit it, and giving your team a clear way to override or escalate. The deliverable is a working process, not a new dashboard to manage. Less magic, more plumbing.

The AI layer sits on top of your existing systems — we don’t propose replacing Clio, or QuickBooks, or whatever your team actually uses. We connect to it.

Common concerns — answered directly

Most AI conversations get derailed by the same four questions. Here’s where we stand on each:

You own the keys — and why that matters

Your AI integration runs against your accounts at the model providers — OpenAI, Anthropic, whoever fits. Your data goes through your contract with the vendor, not a sublease through ours. When this engagement ends, you have your keys, your data, your logs, and your code. Nothing gets stranded in our infrastructure. Your accounts, but TCG owns the operational layer — key rotation, spend monitoring, rate-limit alerting, and the audit logging that lives on top of the API. You hold the contract; we hold the operating burden.

The opposite pattern — a vendor reselling a model through their own portal — is convenient until you want to leave, or until they raise prices, or until they get acquired. We don’t build that.

This also applies at the model layer. The model landscape is moving fast. Code that hardcodes a single model provider ages badly. We build with a thin abstraction at that boundary so you can swap providers — or fail over to a second one — without rewriting your application.

When AI is the wrong tool

Not every problem benefits from a language model, and we’ll tell you when yours doesn’t.

Deterministic rules beat probabilistic models for anything safety-, compliance-, or contract-bound. If the answer has to be the same every time — a compliance checklist, a contract clause lookup, a price calculation — a lookup table or a rules engine is the right tool. Adding an LLM introduces variance where you don’t want it.

Automation beats AI when the workflow is already deterministic and the inputs are structured. If you can write down every step and every decision rule, you don’t need a language model — you need a script or a workflow tool. AI is useful when the input is unstructured, the judgment is contextual, or the output needs to vary by situation.

If the process is working, we leave it alone. Adding AI to a functional workflow because AI is a current priority creates overhead, not progress. The most useful thing we can do in a first conversation is rule out the cases where AI integration would create more problems than it solves.

Where AI integration fits with the rest of what we do

AI integration doesn’t exist in isolation. The integration has to run somewhere, connect to something, and be maintained over time. Because TCG’s managed IT practice already handles the infrastructure and systems access for clients, we understand what’s in your environment before we propose an integration. We’re not parachuting in to build something and leave — the integration lives in the same operational context we already manage.

For firms whose AI integration involves on-premises systems — a self-hosted document store, an on-site server that holds regulated data, infrastructure that can’t go into a public cloud — our Linux infrastructure practice handles the server layer that the integration runs on. Most AI integrators assume everything is cloud-accessible. Many professional services workflows aren’t.

If your firm is in one of the verticals we work specifically with — law, insurance, accounting, real estate, construction, and others — the Industries section covers how AI integration applies in your context. The workflow patterns differ by vertical, and the integrations we build reflect that.

For the founding decisions behind TCG’s BYOK-by-default posture — and the practitioner background behind this work — the About page is the shortest path.

What we don’t do

We don’t produce AI strategy documents. If you need a slide deck describing your firm’s AI readiness maturity, we’re the wrong vendor. That work has its place; it’s not ours.

We don’t implement vendor platform AI features. Getting Microsoft Copilot configured for your M365 tenant is a different engagement than building a workflow integration. TCG builds integrations; Copilot configuration belongs with a Microsoft partner.

We don’t certify or attest to AI compliance frameworks. We can support your firm in preparing for AI governance review — data handling documentation, access controls, audit logging — but we don’t certify. Nobody you should trust does that without an external assessor involved.

We don’t build for enterprise scale. If you need an AI integration supporting hundreds of concurrent users, handling millions of transactions, or running across multi-region cloud infrastructure, TCG is not the right fit. Our practice is sized for SMB operations — 10 to 100 people — and that’s where we build well.

How AI Integration engagements are scoped

AI Integration work is engagement-scoped: defined deliverable, defined timeline, defined scope. It is not an ongoing managed-services wrapper — if you need that, Managed IT is the right conversation. Each integration engagement starts with a specific workflow, produces a working integration, and closes when the deliverable is in your hands.

The BYOK posture described above is part of the scope structure: your firm holds the API contract with the model provider directly. TCG scopes and builds the integration work. When the engagement closes, you own the keys, the code, and the logs — nothing is stranded in TCG’s infrastructure.