AI features, multi-tenant scaling and DevOps for SaaS companies

Enforce tenant boundaries in the retrieval and tool layers, not in the prompt. Embeddings are stored with tenant and permission metadata, and those filters run inside the database query, so the model only receives records the signed-in user could already open. Tools the AI can call reuse your existing authorisation checks. Caches and conversation history are keyed by tenant, customer data stays out of shared fine-tuning, and cross-tenant access attempts are part of the test suite that runs before every release.

We score it against an evaluation set built from realistic cases in your product before any customer sees it. The set covers typical requests, awkward edge cases and requests the feature must refuse, each with an agreed expected outcome. Every prompt, model or retrieval change is scored automatically for accuracy, grounding in sources and safety. The feature then goes to a few design-partner tenants behind a flag, and we compare their edits and feedback with the offline scores before widening access.

Monitor quality, cost and behaviour per tenant, not only uptime. We track latency and errors per model, token spend by tenant and plan, how often users edit or reject outputs, retrieval misses where no relevant source was found, and requests stopped by guardrails. Samples of real interactions, with personal data masked, are reviewed on a schedule and turned into new evaluation cases. Alerts fire when cost per request or failure rates move outside the limits you set.

Make AI a tenant-level setting from the start, with processing location as part of it. Each tenant record carries flags for which AI features are enabled, which model providers may process its data and how long prompts and outputs are kept. The LLM gateway reads those settings on every request and either routes to a model endpoint in the agreed region, where the provider offers one, or skips the feature. Your sub-processor list and customer documentation can then describe exactly what happens to each tenant's data.

Build the features that sit on your own data and define your product, and buy the generic ones. An assistant that answers from each customer's records, or automates a workflow only your product runs, is usually worth building because you control tenant isolation, quality and cost. Generic capabilities such as transcription, translation or a help-centre widget are often cheaper to embed. Every embedded vendor becomes another sub-processor your customers must approve, so we compare data handling and per-seat cost before recommending a route.

We separate the work large customers generate from the requests everyone else depends on. Bulk imports, exports and scheduled reports move to dedicated queues and worker pools, often split by plan tier. Public APIs get per-tenant rate limits and fair scheduling. Reporting queries can run on read replicas, and for the largest accounts a dedicated database becomes an option. Tenant-level metrics show who is consuming what, so these decisions rest on data rather than guesswork.

Link infrastructure and model spend to tenant IDs, then compare it with what each account pays. We tag cloud resources consistently, record tokens and model choice for every AI request against the account that made it, and meter heavy operations such as storage, exports and background jobs. A pipeline joins this with plan and billing data, and a dashboard shows gross margin per tenant, plan and feature. Plan limits, AI allowances and price changes can then rest on measured cost rather than averages.

Yes, if the environment is defined as code and deployed by the same pipeline as everything else. We package the platform as Terraform modules and container images, then create a separate stack for that customer with its own database, encryption keys and, where required, a specific cloud region. Each release goes to the shared platform and every dedicated stack from one pipeline, in the order you choose, so nobody maintains a special branch. Monitoring and hosting costs are reported per stack.

We close the technical gaps the review exposes and give you accurate material to answer it. Typical work includes single sign-on, role-based access, audit logging, encryption settings, secrets moved into a managed vault, dependency and container scanning in CI, tested backup restores and an incident runbook. We also produce architecture and data-flow diagrams and help draft technical answers to standard questionnaires. We do not certify you or sign attestations, and audit decisions stay with you and your auditors.