Quick Answer: Custom AI Copilot Development
i3solutions builds custom AI copilots on the Microsoft stack: Azure OpenAI and Azure AI Search for retrieval-augmented generation inside your tenant, Microsoft Copilot Studio for agents that act in your systems, Teams and Microsoft 365 as the surface, and SharePoint or Dataverse as governed knowledge sources that honor the signed-in user’s Entra ID permissions. Senior, U.S.-based teams; Microsoft partner since 1997; 600+ Microsoft platform implementations, including AI platforms delivered inside Department of Defense environments.
Plenty of firms can build you a chatbot. The failure mode arrives later, on audit: retrieval that ignores your permission model, prompts and responses that no log captured, and an agent nobody can promote from a developer’s sandbox to a governed production environment. If your enterprise runs on Microsoft, the copilot should run on the controls you already own, and that is the entire premise of how we build.
What We Build
Custom copilots and agents. Agent logic, custom skills, prompt design, and knowledge integration in Microsoft Copilot Studio, taken from prototype to production through dev, test, and production environments promoted via Managed Environments rather than living in a maker’s sandbox.
Retrieval-augmented generation in your tenant. RAG built on Azure OpenAI with Azure AI Search, with your data kept inside your tenant, retrieval filtered by Entra ID group membership, citations returned with every answer, and answers that refuse when no grounded source exists.
Domain-specific agents. Personas, knowledge ingestion from documents and SOPs, and guardrails tuned to the workflows the agent serves, with human-in-the-loop checkpoints on any step that writes or sends.
Systems integration. Connectors into CRM, ERP, HRIS, and ITSM platforms with on-behalf-of authentication through Entra ID, so the copilot acts as the signed-in user and never as an over-privileged service account.
Governed by Construction, Not by Promise
The governance is the differentiator, because it is the part a generic AI shop cannot retrofit. SharePoint and Dataverse knowledge sources honor the signed-in user’s Entra ID permissions on the underlying records, while a public website source honors nothing, and an architecture has to respect that difference. Connectors run least-privilege, scoped by role. A Power Platform DLP policy is in place before the first agent ships, not after the first incident. Every prompt and response is captured in Microsoft Purview, with alerting through Microsoft Sentinel. Sensitivity labels on knowledge sources are honored at retrieval time. Azure AI Content Safety filters the generated side, and output inherits Purview labels and DLP. For customers who need it, customer-managed keys and Customer Lockbox close the loop. The control set maps to CMMC 2.0, NIST 800-171, the HIPAA Security Rule, and SOC 2 Type II, and the build is documented so your assessors can verify it rather than take it on faith.
Build a Custom Copilot or Buy Microsoft 365 Copilot?
These are different purchases solving different problems. Microsoft 365 Copilot, a $30 per user per month add-on at Microsoft’s published mid-2026 pricing, reasons over what the Microsoft 365 index already holds: mail, files, meetings, chats. Build custom when the knowledge lives outside that index, in a line-of-business database, a Dataverse table, or an external API, or when the copilot must take actions rather than summarize. Copilot Studio consumption is prepaid in packs of 25,000 Copilot Credits at $200 per pack per month under Microsoft’s mid-2026 pricing, which changes the cost model from per-seat to per-usage. If you are earlier in the journey, start with Microsoft 365 Copilot readiness or the Copilot licensing guide; if the platform question is still open, our custom AI consulting and integration services team evaluates fit across Microsoft Copilot, Copilot Studio, Azure AI, Power Platform, Fabric, and custom integration before anything is built.
Proof From Regulated Delivery
This is not our first AI platform; it is the same discipline applied with newer models. For a U.S. defense command, i3solutions built a custom AI data-fusion platform unifying 300TB of structured and unstructured data from over 250 global sources into a single pane of glass, with AI and machine learning handling data mining, pattern recognition, and metadata auto-tagging. For a U.S. Army geospatial organization, we developed and trained over 20 machine learning models and applied generative AI, large language models, and retrieval-augmented generation across hundreds of thousands of news articles, files, and documents. And we operate governed platforms at enterprise scale: a Power Platform estate for a federal defense agency supporting roughly 10,000 personnel across about 180 locations. Read the data fusion case study or the machine learning case study.
How an Engagement Runs
Engagements follow the Expert Delivery Model, a four-phase methodology of discovery, architecture, build, and knowledge transfer. A small-scale prototype can be delivered in 4 to 6 weeks; full-scale production deployments may take 3 to 6 months depending on integration depth and governance requirements. Senior specialists typically embed with your team within two to four weeks of engagement start, and delivery runs on 100% US-based senior-level delivery teams, which matters when the knowledge sources are export-controlled or the tenant is a government cloud. And because an agent is a system you operate rather than a project you finish, the model also answers who maintains and supports these agents after they go live.
If a copilot is on your roadmap, the scoping conversation settles the three questions that decide the build: where the knowledge lives, what the agent is allowed to do, and what your auditors will need to see. You leave with a platform recommendation across Copilot, Copilot Studio, and Azure OpenAI, and a build estimate against the engagement ranges published on this page. If the decision needs to survive a governance or budget committee, that documented recommendation is built to go into the room with you.
