Custom Business AI Platform
Your business already has the knowledge a custom business AI platform needs. It lives in your emails, SharePoint, Teams chats, SOPs, spreadsheets, and CRM records.
Fusion Computing builds a governed AI platform over that data, so your team can ask better questions, automate repetitive work, and turn company knowledge into repeatable systems.
CISSP-led security · Canadian-owned since 2012 · Built for Microsoft 365 businesses · OpenAI technology partner · Practical AI adoption
What a free AI strategy call covers
Business ai platform? A business AI platform answers questions from your own documents rather than the open web. Before building one, check whether Microsoft 365 Copilot already covers it: Copilot surfaces only content a user has at least view permission for, so the real work in either route is fixing permissions first, not choosing a model.
A free 30-minute discovery call covers the business task, current systems and users. We confirm whether a paid assessment or custom build is a sensible next step. Technical findings, pricing and delivery dates follow a scoped review.
- ✓ An honest look at your data sources and systems
- ✓ The access and approval questions to cover in a paid scope
- ✓ The next step for a suitable business workflow
Recent engagements
- AI Rollout for a 40-Person Firm: Hype to Results
Measured productivity gains and a tested governance pattern. - Marketing Agency Cyber Recovery
Stabilized in 72 hours after a ransomware breach; gap closed in week one. - Scaling a Design Studio: 35 to 205 users
Zero unplanned downtime through a 4-month phased deployment.
When Does a Custom AI Platform Make Sense?
Use Microsoft 365 Copilot when the licensed product and available agents cover the task. Consider a custom platform when the workflow needs source-specific integration, retrieval checks or approval steps that the selected product does not provide. Compare a real workflow before commissioning a build.
On small screens, swipe sideways or use the arrow keys to compare both routes.
| Buyer question | Microsoft 365 Copilot or a configured agent | Fusion Computing custom platform |
|---|---|---|
| Which sources can it use? | Microsoft 365 content and the sources supported by the selected agent or connector, subject to licensing and access. | The approved SharePoint, CRM, accounting or other sources named in the integration scope. |
| How is access controlled? | Microsoft applies the controls for the selected product. Permissions and connector configuration still need review. | We map each source’s access rules to retrieval and test allowed and denied requests. |
| Can users check an answer? | Grounded responses can provide references. Verify the behaviour against your actual sources and tasks. | The pilot tests source references, answer quality and what happens when evidence is missing. |
| Can it take action? | Agents and connectors can support actions within their configured permissions and capabilities. | The scope names the actions, approval points and exception handling needed by the workflow. |
| What are the ongoing costs? | Applicable Microsoft licences, agent usage and the time required to manage the configuration. | Build work, model and platform usage, connectors, support and maintenance are scoped separately. |
| When is this route a fit? | The available product meets the task and its controls can be configured for your users. | A valuable workflow needs integration or control behaviour beyond the selected product. |
Microsoft documents work-data access through uploaded content, supported in-app context and configured agents. Compare the actual licensed product before assuming a custom build is needed. Microsoft Copilot Chat privacy and work-data access.
Project guide and written scope
The published custom-platform project guide is CAD $25,000 to $60,000, depending on scope. A paid readiness assessment defines the proposed sources, workflows and controls. Your written proposal separates build cost from licences, usage and ongoing support before you commit.
A Managed AI Platform Built Around Your Company Knowledge
A custom business AI platform connects your company data and indexes it securely. It retrieves the right information at the right time. Your team gets a simple interface to ask questions, generate work, and trigger approved automations. Built around your business context, your users, your permissions, and your workflows.
Fusion Computing’s custom business AI platform is not a generic chatbot dropped onto your website. It is a governed AI system built on retrieval-augmented generation (RAG), a secure knowledge index, and a permission-aware retrieval layer. The same platform extends our existing Microsoft Copilot deployments and Power Automate consulting into something more durable: a productized custom AI integration platform.
What the Platform Includes
Bought the licences and nothing changed? That is a training and governance problem rather than a tooling one. See AI enablement and training for how we take a team from licences to a measured change in how work gets done.
Six modular layers. The reusable foundation stays the same across clients. The custom work happens in the data ingestion layer where every business is different.
Data Ingestion & Connectors
We connect to where your knowledge already lives. That includes Microsoft 365, SharePoint, OneDrive, Teams, Outlook, CRM, accounting, PSA or ticketing, line-of-business apps, PDFs, spreadsheets, internal documents, websites, portals, and structured databases.
This is the custom layer. Fusion Computing defines the right ingestion plan before anything is indexed.
Secure Knowledge Index
Document parsing, metadata extraction, embeddings, vector search, structured document indexing, page-level indexing for long documents, source tracking, and permission-aware retrieval.
When someone asks a question, the system finds the right business context instead of guessing.
Retrieval-Augmented Generation
RAG grounds AI answers in your company data. Users ask natural questions and receive answers based on approved internal sources, with references back to the documents, records, or systems used.
Ground answers in approved sources, show the evidence and test unsupported questions.
New to the pattern? Start with our plain-English RAG explainer for Canadian SMBs.
Business Memory Layer
Persistent context across interactions: company terminology, preferred response style, standard operating procedures, past decisions, customer-specific context, known exceptions, reusable workflows, and department-specific instructions.
Better continuity without uncontrolled access to everything.
Retrieval Tuning & Quality Review
Ongoing review of what your team asks, what the system retrieves, and where answers fall short, with chunking, ranking, and prompt tuning adjusted against real usage.
From “interesting demo” to “useful business tool.”
Frontend & Workflow Automation
Delivered through a web portal, Microsoft Teams, internal apps, workflow dashboards, department-specific assistants, Power Automate workflows, Azure Functions, and custom business process automations.
Ask the right question. Get a useful answer. Move work forward.
What Can a Custom Business AI Platform Do?
Six categories where Fusion Computing clients are getting the highest day-one value.
Company Knowledge Assistant
A secure place to ask questions about policies, procedures, client history, product information, internal documentation, and technical material.
Sales & Customer Response
Help sales and service teams draft replies, find past quotes, summarize customer history, compare product information, and prepare better responses faster.
Document Intelligence
Extract and compare information from PDFs, contracts, forms, invoices, reports, supplier documents, and spreadsheets.
Operations Automation
Turn repeatable manual work into AI-assisted workflows: report generation, intake processing, approval routing, ticket triage, and status updates.
Technical Support Assistant
Help staff search internal documentation, past tickets, vendor notes, troubleshooting steps, and known fixes.
Executive Q&A
Owners and managers ask business questions across multiple systems without manually pulling data from five places first.
Custom AI agents, not just chat
Who builds custom AI agents for Canadian small and medium businesses? Fusion Computing designs, builds, and manages custom AI agents for Canadian SMBs from Toronto, Hamilton, and Metro Vancouver. Agent builds start with your permissions model and data boundaries, then automate the workflow end to end, whether that is a Copilot Studio agent inside Microsoft 365 or a fully custom build on your own systems.
An AI agent goes one step beyond a chat assistant: it completes multi-step work such as reading a request, checking your systems, drafting the response, and filing the result. We scope agent and workflow automation builds against the same security baseline as the platform above, so agentic workflows never outrun the permissions that govern your data.
Questions the first workflow can answer
“What did we quote this client last time?”
“Which supplier has the best price this week?”
“What does our policy say about this situation?”
“Which tickets point to a recurring operational issue?”
“What changed between these two contracts?”
“What should the next step be for this customer request?”
Built With Security Before Automation
The biggest AI risk for an SMB is not that AI will fail. It is that AI will be connected to too much data too quickly. Fusion Computing starts with access, governance, and security controls before production rollout. The same controls already underpin our file labels, access checks, and data loss controls. We define least-privilege access, audit events and human approval steps in the written scope.
Role-based access
Permissions per user, group, and department.
Least privilege
The AI sees only what it needs to answer.
M365 identity
Entra ID, sensitivity labels, conditional access.
DLP alignment
Existing data-loss policies extend into AI.
Audit logging
Every query, retrieval, and action logged.
Human approvals
Sensitive workflows pause for sign-off.
Source citations
Source references are checked against the pilot acceptance tests.
Data handling review
Document the selected services and their data-handling terms.
The pattern is the same on every AI project I’ve scoped this year. The best business knowledge is locked inside email threads, SharePoint folders nobody curates, and the heads of three or four people. The job isn’t installing a chatbot. It’s getting that knowledge into a system the AI can reach without breaking governance, and stopping there until the controls are right.
AI should make the business faster without making the data messier.
What our custom AI platform stack looks like
RAG architecture and Microsoft stack
- Azure OpenAI Service, with the model and deployment selected for the agreed requirements
- Azure AI Search with hybrid keyword + vector embeddings retrieval
- Document-chunking pipelines with overlap, semantic split, and metadata tagging
- Embedding pipeline selected for the approved source corpus
- Retrieval grounding and source-reference evaluation against agreed test questions
- Identity-aware access via Entra ID On-Behalf-Of (OBO) and ACL trimming
- Microsoft Purview sensitivity labels honoured at index and retrieval time
- Conditional Access policies enforce MFA, device compliance, and location
- Azure AI Foundry for evaluation, content-safety, and prompt-flow orchestration
Governance and evidence in scope
- Data-flow and access documentation for the services selected in scope
- Privacy and consent requirements identified with the client’s responsible owner
- A model and integration inventory with named owners and review tasks
- NIST AI RMF findings mapped to the controls reviewed in scope
- ISO/IEC 42001 mapping where included in the agreed assessment
- Coverage gaps and unresolved decisions recorded for the client
From Assessment to a Maintained Platform
Four phases. The first version focuses on high-value workflows. The fourth keeps the platform secure, current, and useful as your business changes.
Assess
We review your systems, data sources, workflows, permissions, and business priorities. You get a clear recommendation on where AI saves time, where automation makes sense, and what data should or should not be connected first.
Build
We configure the core AI platform, connect the first approved data sources, build the retrieval layer, and deliver the first working use cases. The first version focuses on high-value workflows, not every possible idea at once.
Optimize
We review usage, tune retrieval, add new data sources, improve prompts, and expand into additional workflows as your team gets comfortable. Every rollout becomes more useful over time.
Maintain
Monthly retrieval-quality reviews, source-index updates as your documents change, permission audits when staff join or leave, and quarterly roadmap sessions. The platform stays accurate as your business knowledge evolves instead of decaying after launch.
Optional planning resources
FREE DOWNLOAD
AI Implementation Partner RFP Kit and Scorecard (2026)
Section 7 is the one to read first: when a custom build is the wrong answer, what it costs to RUN rather than to build, and who owns the code and the prompts afterwards.
Written by Mike Pearlstein, CISSP. No sales call required.
Score your AI readiness across governance, your IT estate and Microsoft 365, with your ranked gaps and a 30/60/90-day plan. Twelve quick questions.
Take the AI readiness assessment →Free. No booking required.
Common Questions Before Getting Started
How is this different from Microsoft 365 Copilot?
Microsoft 365 Copilot and configured agents can already work with company content. A custom platform is a fit when the workflow needs integrations, retrieval checks or approval steps that the selected product does not provide. Fusion Computing reviews that gap before proposing a build. See our Microsoft 365 Copilot consulting page for the product-led route.
How long does it take to get a working version?
The first usable version typically lands in 4 to 8 weeks. Phase 1 (Assess) takes 1–2 weeks and produces the data and workflow plan. Phase 2 (Build) takes 3–6 weeks and delivers the first working use cases against the first approved data sources. Phase 3 (Optimize) tunes retrieval, expands data sources, and rolls out additional workflows. Phase 4 (Maintain) is the ongoing managed-services cadence covering model and connector updates, governance and DLP reviews, audit-log monitoring, security posture, and incident response.
What does this cost?
The published custom-platform project guide is CAD $25,000 to $60,000, depending on scope. A paid readiness assessment defines the proposed sources, workflows and controls. Your written proposal separates the build cost from licences, usage and ongoing support before you commit. Request a free strategy call to discuss the business need.
Will our data be used to train someone else’s AI model?
Microsoft states that Azure OpenAI customer prompts, completions and embeddings are not used to train foundation models without permission. The application still needs its own access checks and data-handling review. We document the selected services and test the source permissions included in the scope.
Do we need an internal AI or data team to use this?
No. The platform is delivered as a managed service. Fusion Computing handles the architecture, the data connectors, the retrieval tuning, and the governance reviews. Your team uses the system through a web portal, Microsoft Teams, or a department-specific assistant, the same way they already use Outlook or SharePoint.
Can the platform trigger actions, not just answer questions?
Yes. The frontend layer can call Power Automate workflows, Azure Functions, or custom business process automations. Sensitive actions pause for human approval.
What if our data is messy or our SOPs aren’t written down?
That is the normal starting point for an SMB. Phase 1 surfaces the gaps before any data is indexed. The Build phase usually starts with the cleanest, highest-value sources first, current SOPs, recent contracts, current product documentation, and expands as your team writes down or cleans up the rest.
The platform is designed to grow with your knowledge, not to require a perfect data lake on day one.
Ready to Turn Your Business Knowledge Into Working AI?
Tell us where your data lives and which workflows slow your team down. Fusion Computing will help identify the highest-value AI opportunities, the safest place to start, and the right path from idea to production.
Start the conversation
Share the business problem, team size and timing. Managed services usually suit 10 to 150 employees; smaller project and AI enquiries are welcome.
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- ✔CISSP-led Canadian team
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