SaaS & Technology Platform AI Engineering

Strict per-tenant isolation is designed in from the first line: retrieval, prompts, and model calls are scoped so one customer's data can never surface in another's results, aligned to SOC 2 controls. Cross-tenant leakage is an existential trust failure for SaaS, so we treat isolation as a hard requirement, not a configuration option.

We track cost per tenant as a first-class metric, cap heavy usage, cache aggressively, and route simple requests to smaller models. Ungoverned AI spend can quietly erase SaaS margin, so cost control is part of the architecture. You should know what each tenant's AI features cost, and be able to price accordingly.

Usually the one tied to a repetitive task your users already do in-product, such as search or a workflow copilot, not the flashiest option. We help you pick where adoption will be high and value is fast, then measure whether users actually keep it on, and expand from there rather than shipping ten shallow features.

Not if designed for it. We use streaming responses, cache, and route by difficulty so features stay fast across tenants and load spikes. Latency is a design problem; a feature that stalls under load costs more than it adds, so we load-test against your real usage patterns rather than assuming.

You do. Your product data, the trained models, and the code remain yours, with per-tenant isolation and zero-data-retention options for AI calls. We build into your product and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single an in-product copilot build is a modest, weeks-long project, while a wider rollout across your product and data platform is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from higher retention, expansion, and product differentiation, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as an in-product copilot usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are in-product copilots, semantic search, churn and usage models, and generative in-app tooling. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.