Free 30-min discovery call CT · NY · MA · RI · nationwide
+1 (888) 555-0142 hello@skyviewlabs.ai book 30-min call →
~/services/system $ cat integrate.md

AI is only as good as the data underneath it.

Most enterprises run on systems that do not talk to each other. CRM, ERP, M365, EHR, helpdesk, custom line-of-business — each holds part of the truth, none holds it cleanly. We build the integration layer and unified data foundation that makes AI, analytics, and operational reporting actually work.

~/services $ cat reference-pattern.md

Reference architecture

~/services $ cat the-problem.md

The problem

Data is fragmented across platforms. The customer record lives in the CRM, the order in the ERP, the support history in the helpdesk, the documents in M365, and the operational data in a custom application. AI built on top of that produces fragmented results.

Reporting is manual and stale. Analysts spend hours assembling cross-system reports because no system holds the full picture. The "single pane of glass" promised by every SaaS vendor never materializes when systems do not share a data model.

Every AI project hits the same wall. The AI vendor asks where the data lives. The answer is twelve places, with twelve schemas, owned by twelve teams. The integration work that should have been done years ago becomes the bottleneck on every AI initiative that follows.

~/services $ cat what-we-do.md

What we do

We connect the systems, normalize the schemas, and build the unified data layer that AI runs on — making your reporting, analytics, and operations work better even before AI lands.

System integration

Connect the SaaS and on-premises platforms your team already runs — Salesforce, HubSpot, Dynamics, NetSuite, Epic, Magento, Shopify, custom line-of-business systems, twenty-year-old on-prem ERPs. Modern APIs and legacy integration surfaces both in scope.

Data unification

A normalized data layer across the connected systems — customer, transaction, document, operational. Not a generic data warehouse imposed on top, but a unified model designed for your actual business operations.

M365 + Microsoft Graph

For organizations standardized on Microsoft 365, we build the Graph-based integration layer that exposes M365 data to AI and analytics workloads cleanly — SharePoint, OneDrive, Exchange, Teams, Dynamics, Purview governance.

Master data management

Resolving duplicate records, normalizing identifiers, and establishing the master data definitions that downstream AI will rely on. The work that prevents an AI agent from mistaking three records for three different customers when they are actually one.

Reporting + BI enablement

Once the data is unified, reporting and BI improve immediately. Power BI, Tableau, Looker — connected to a clean data layer instead of fighting cross-system extracts. Faster reports, more accurate dashboards, better decisions.

Real-time event integration

For workflows that need real-time data flow — agent-driven decisioning, customer-facing AI, operational alerts — we build the event-driven integration layer that supports it.

~/services $ cat outcomes.md

Business outcomes

A data foundation that supports AI

AI workloads built on a unified data layer produce coherent results — not the contradictory answers that come from AI fed fragmented data. Every future AI initiative gets faster and more accurate as a result of integration done first.

Better reporting and BI without AI

Integration and unification produce immediate value even before AI lands. Reporting times drop, dashboards become trustworthy, analysts get freed from spreadsheet assembly.

Reduced cost of every future integration

The integration plumbing built once is reused by every system added later. Cost per integration decreases over time as the unified layer absorbs more of the surface.

Faster operational decisions

When the data needed for a decision lives in one place, decisions ship in minutes instead of hours. The compounding value across thousands of micro-decisions is substantial.

Reduced redundancy

Integration work surfaces the overlapping systems your team has accumulated. Many operators discover they are paying for three tools that should be one — license consolidation often pays for the engagement.

~/services $ cat pricing.md

How it is priced

Engagement model
System Integration & Data Enablement

Integration Assessment: fixed-price, $15,000 to $40,000 depending on system count and complexity. 2 to 4 weeks. Output is a documented integration architecture and phased build plan with fixed-price scope per phase.

Integration builds are scoped and priced per phase, with each phase shipping working integration into production before the next phase begins. Most programs run 6 to 16 weeks across 2 to 3 phases.

~/faq $ cat system-integration-data-enablement-faq.md

System Integration & Data Enablement — frequently asked questions

The questions we get most often, answered. If yours isn't here, ask it on a 30-minute call — we answer the awkward ones too.

Do we need Microsoft 365 Copilot licenses already, or do you handle that?
Either works. Spectrum Virtual is a Microsoft Cloud Solution Provider (CSP) Indirect Reseller, so we can procure your Copilot + M365 + Power Platform + Azure licenses, manage your tenant, and bundle license costs into a single monthly engagement. If you already have licenses through another reseller or a Microsoft direct agreement, we deploy against those — no need to switch unless it makes commercial sense. We're transparent about Copilot economics: it's per-user/per-month and only pays back for the user populations that will actually use it. License sizing happens during Discovery, not after.
How long does a Copilot deployment take?
Most deployments ship in 4–8 weeks end-to-end: 1–2 weeks Discovery + readiness, 2–4 weeks technical deployment + governance, 2–6 weeks adoption program + first set of custom Copilot agents. Faster for smaller tenants with clean data hygiene; longer for larger or messier environments where the SharePoint permissions audit + remediation is the long pole.
What's the difference between Copilot and a custom AI application?
Copilot for M365 is licensed productivity AI inside Microsoft's products — Word, Excel, Outlook, Teams, SharePoint. It excels at M365 workflows. Custom AI applications are bespoke systems we design and build for workflows that live outside M365, or for workloads where per-user Copilot licensing breaks the economics, or for use cases requiring data hosting controls Copilot does not offer. Many engagements use both — Copilot for the M365 surface, custom AI for the rest. We tell you which is which.
Can you build custom Copilot agents for our specific workflows?
Yes — that's the part of Copilot deployments that most consulting firms skip. We build custom agents in Microsoft Copilot Studio grounded in your business data, integrated with your line-of-business systems (CRM, ticketing, HRIS), and tied to specific role-based workflows. Copilot Studio is the right tool for many M365-adjacent workflows; custom AI on our private cloud is the right tool for others. We pick deliberately.
How do you handle Copilot data governance and the "Copilot saw something it shouldn't" problem?
This is the single biggest reason Copilot rollouts go sideways and one of the first things we audit. The fix: SharePoint + OneDrive + Exchange permissions review, sensitivity labeling, DLP policies, and tenant-level governance configuration — before activation, not after the leak. We document the controls so your security and compliance teams can sign off. This is part of Phase 1, not an upsell.
How long does a Skyview Labs AI engagement take?
Most production AI engagements ship in 4 to 12 weeks from kickoff. Discovery Engagements run two weeks. The build phase runs two to eight weeks depending on scope. Integration and testing run one to two weeks. We don't run multi-year transformation engagements — we ship working software early and iterate against real usage.
How much does AI consulting cost?
Discovery Engagements are flat-fee, typically $10,000–$25,000 depending on organizational complexity, and credited against a build engagement that starts within 90 days. Custom build engagements are fixed-price for the scope defined in Discovery — most fall in the $50k–$500k+ range depending on scope. Private AI cloud hosting and Managed AI Operations are monthly engagements priced to workload, typically bundled with the build for a single predictable monthly cost. No per-token surprises.
Where will my data be hosted?
By default, in our private AI cloud — Tier III TierPoint colocation facilities in Marlborough, MA (MRL-01) and Chicago, IL (CHI-01), with regional capacity at our Connecticut office (CT-01). For workloads with strict data sovereignty, ITAR, or air-gapped requirements, we install the entire Skyview stack on-premises in your own data center. Every engagement includes a written data flow document covering every component, every integration, and every external API call.
~/contact $ open

Build the data foundation AI runs on

Most AI projects fail because the data was not ready. We make sure yours is — before the AI build starts.

Book an Assessment