AI Copilot Development Services

An AI copilot is an assistant embedded in a product or workflow that answers from your own data and takes actions in your systems on the user's behalf. A chatbot answers questions; a copilot completes work, drafting the record, updating the ticket, running the query, and reporting what it did. The action layer is the commercially meaningful difference, because that is where the time is actually saved.

Use theirs when your work already lives inside their suite. Per-seat platform copilots are good at what sits in their own product's data, and no custom build competes with them on price for that. Build custom when the copilot needs your proprietary data, your permission model, or the ability to act inside your own product. We'll tell you which case you're in before you commit to a budget.

Every answer is generated from your own retrieved content and includes a source the user can open. The copilot is instructed to refuse when the material isn't there. The model isn't asked to recall facts about your business; it's asked to answer from material we put in front of it. We then measure how often it's right against an evaluation set built from your real questions, so accuracy is a number you can see.

Yes, and we treat that as a design requirement. The copilot authenticates through your existing SSO and inherits your role-based access rules, so retrieval is filtered per user before anything reaches the model. Someone asking a question can never be shown a document they weren't already allowed to open.

It acts. We wire the copilot to your APIs and internal services through individually scoped tools, so it can create records, update tickets, run queries, and trigger workflows. Consequential actions require explicit human confirmation, and every call is logged for audit. Our own assistant runs on 16 live tools rather than a static index, which is why its answers are never stale.

We build an evaluation set from your real questions and expected answers, then score the copilot on accuracy, citation quality, and refusal behavior before go-live. The same measurements run continuously once it's live, so quality drift shows up on a dashboard first.

Per industry averages, a focused single-use-case copilot typically runs from around $30,000–$80,000, while an enterprise copilot spanning several systems with SSO, audit, and compliance requirements usually lands between roughly $100,000 and $300,000 or more, depending on the number of integrations, data volume, and governance scope.

A working proof of concept typically takes about a week, and a production-ready first version about a month. Enterprise rollouts take longer as the number of integrations, security reviews, and change management tasks stack up. The first step is always a Scope & Design Document that turns the goal into a phased plan with milestones.

Yes, completely. You own the application code, the prompts, the retrieval pipeline, the tool definitions, and the evaluation set, with full IP rights transferred and everything running in your own accounts. The evaluation set matters: it's the asset that lets any future team prove the copilot still works after they change something.

We're model-agnostic and choose per use case, weighing accuracy, latency, and cost against each other. The copilot runs behind a provider-neutral layer, so moving from one provider to another is a configuration change rather than a rebuild. If your data can't leave your environment, we'll run an open model you host instead, as we did on a self-hosted AI platform where credentials and memory never left the user's own hardware.

Retrieval quality degrades as your content changes, and someone has to own that. We monitor accuracy, latency, and cost, alert on drift, and retune retrieval and prompts as part of ongoing support. With 80% of our clients staying 7+ years, this is the normal shape of our engagements.