AI & detection · Retail & eCommerce
This consumer brand ran its own storefront, its own platform and its own AI, with no internal platform or security engineering to support any of it. intSignal builds, hosts and defends the whole thing — one team across development, infrastructure, AI operations and layered security.
The problem
A customer-facing storefront and platform to build, run, and protect.
What we did
Full platform support and ongoing development.
The outcome
One partner across development, hosting, security, and telephony.
The challenge
A customer-facing storefront and platform to build, run, and protect. AI models needing training, management, and somewhere to actually run. Consumer and order data attracting the attention that comes with it. No internal platform or security engineering capacity.
The thinking
A consumer brand running its own storefront, platform and AI needed all three to hold up together under campaign spikes — and had no internal engineering to make that happen. We build, host and defend the whole stack as one, using a CDN for spiky front-end demand and infrastructure we control for the platform and models. One team across development, hosting, AI operations and security means the layers are chosen to fit each other, not assembled from whatever each vendor happened to sell.
The approach
Full platform support and ongoing development. Hosting and CDN, ERP, and Microsoft 365. AI training model development and management. Cybersecurity: XDR, EDR, SIEM, FWaaS — plus UCaaS.
Why it works
The quieter win is accountability. When a custom platform, its hosting, its security and its phone system come from four different vendors, the gaps between them are where things break — and no one owns the gap. Building and running it as one means a single team is answerable for the whole, the systems are unified by design rather than integrated after the fact, and there's one relationship to manage instead of a switchboard of suppliers. For a small team, that unification is the difference between technology being a tool and being a second job.
Outcome
The brand's AI models are trained and run without an ML-ops hire, the storefront sits behind FWaaS and a CDN, and endpoints and platform are covered by XDR, EDR and SIEM correlation. Build, run and defend live with one accountable team instead of three.
CDN kept delivery fast during campaign spikes without permanent overprovisioning. Managed AI infrastructure avoided building an internal ML ops function. Bundled XDR/EDR/SIEM cost less than assembling equivalent point products.
How these figures were derived
Effort and outcomes are reconstructed from production systems of record — ticket histories, call detail records, dispatch and SOC/NOC telemetry — read end to end rather than sampled. Cost comparisons are indexed to a baseline, never a fee or rate. Where a figure is modelled rather than measured it is labelled as such, and the model is documented and available to defend on request.
Customer
Shown on request. Some engagements remain confidential by contract.
Nonprofit
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