Tenant data isolation
Per-customer data boundary, enforced at the runtime layer rather than per-feature. No cross-tenant leakage — ever — and the audit trail proves it. The same isolation guarantee scales from ten customers to ten thousand.
Capability · AI-Powered Products
Customer-facing AI features your engineering team can actually run.
The hard part of in-product AI isn't the prompt — it's the safety, the cost protection, the observability, and the rollout discipline. Agix builds customer-facing AI features that ship to production with the same operational rigor as the rest of your platform.
What an AI-powered product feature is
An AI-powered product feature lives in your customer-facing product, not your team's tools — and it ships with the per-tenant safety, brand-aligned voice, eval harness, and rollout machinery that customer-facing AI demands. Unlike a copilot, which serves your operators, this tier serves your customers. The discipline is higher because the audience is larger, the stakes are public, and the rollout has to be gradual.
- Brand-aligned conversational and generative features
- Per-tenant safety, evaluation, and cost protection
- Designed for the engineering team that has to operate it
- Production-grade rollout — not pilot-grade demo
- Provider-portable across Anthropic, OpenAI, and enterprise endpoints
The stack we install
Six layers — built around the per-tenant safety model that distinguishes customer-facing AI from internal AI — that turn a product UX into a place AI features can live safely.
Product context + brand
Your product's content, your brand voice, your tone parameters — versioned in the same way your codebase is. The AI feature reads from your brand surface, not from a generic model with your name pasted on top.
AI feature surface
Chat, search, recommendations, generation, personalization — embedded in your product's existing UX. The feature reads like a natural part of the product, not like a chat widget bolted onto the corner of the screen.
Per-tenant safety + cost protection
Brand-compliance guardrails per tenant, token ceilings per tenant, refusal posture defaults configured per tenant. A spike in usage for one customer can't break the economics for the rest — or for you.
Eval harness at scale
Regression suite running across diverse customer scenarios on every prompt or model change. Releases gated on green; the feature never ships an update that worsens its own score on canned cases. Eval at scale, not eval-at-pilot.
Rollout flags + experimentation
Gradual rollout per tenant — design partners first, then broader cohorts, then full availability. A/B test the prompt, the model, the surface treatment. Your eng team controls who gets the feature, when, and at what intensity.
What your team owns at handoff
The feature is yours from day one of rollout — code, configuration, evals, cost protection, rollout flags. Agix steps back; your eng team operates the feature the same way they operate everything else in production.
The feature code
Versioned in your repo, shipped through your release pipeline, deployed to your infrastructure. Your eng team owns it the same way they own every other production feature in the product.
Per-tenant config + safety set
The configuration surface that lets you onboard a new customer's brand rules, safety constraints, and feature scope. Reviewable, versionable, change-managed through your normal processes.
The eval set
Canned regressions across your customer base's actual scenarios. Your team extends it as new patterns emerge; CI runs the gate automatically on every prompt change.
Cost protection infrastructure
Per-tenant token budgets, per-customer cost dashboards, alerting on outlier spend. Operated by your team; the safety net is built in, not bolted on.
Rollout + experimentation system
The flags that gate per-tenant availability, the experimentation harness that measures impact, the dashboards that show how the feature performs across the customer base. Your eng team runs the rollout from day one.
When this tier fits
Four buyer situations where commissioning an AI-Powered Product feature is the right move.
"We want AI in our product, but our team can't run a research project."
The hard part of in-product AI isn't the prompt — it's the per-tenant safety, the cost protection, the eval discipline, and the rollout machinery. The feature has to ship with all of that built in, not bolted on after a pilot.
"We need to ship to thousands of tenants safely."
Multi-tenant economics. One tenant's prompt-injection attempt can't compromise another tenant's data. One tenant's usage spike can't drain your token budget. Per-tenant isolation is the architectural baseline, not an upsell.
"The AI feature is the differentiator vs our competitors."
The product moat is the feature, not the model. The model is a commodity; the brand-aligned, per-tenant-safe, observable, evaluable feature wrapping it is the work. That work is what we ship.
"We need rollout control, not a launch."
Pilot with design partners. Roll out per cohort. A/B test the prompt against the existing flow. Pull the feature back from a specific customer without redeploying. Rollout flags and experimentation are first-class, not afterthought.
When to pick something else
Customer-facing AI carries its own constraints. If the AI is for your team or operates on its own surface, a different tier is the right entry point.
If the AI is for your team, not your customers
Internal-facing AI is a different shape — embedded in your team's existing tools, governed by your team's policies, evaluated against your team's workflow. Start with AI Copilots for that work; AI-Powered Products is specifically the customer-facing tier.
Explore AI Copilots →If you need autonomous teammates with their own surface
When the AI needs to run on its own schedule, file its own findings, and have memory that compounds — and the surface lives outside your customer-facing product — that's Agix Agents, not an in-product feature.
Explore Agix Agents →If you don't have a knowledge layer for the feature to query
A customer-facing AI feature that reads from scattered, ungoverned content is a liability at scale. If your brand rules, product knowledge, and tenant context aren't queryable through one surface yet, start with a Second-Brain. The product feature will land on top of it.
Explore Second-Brain Knowledge Systems →Worked example: channel-specific descriptions in a mid-market PIM
An illustrative scenario showing how this tier shows up in practice. The shape mirrors how Agix ships customer-facing AI features; the client and specifics are composite.
The brief
“Our product information management platform serves a few thousand retail and DTC customers. Every customer maintains a master product record and ships variants of it to a dozen sales channels — Amazon, Shopify, their own catalog, wholesale portals — each with its own tone, length, and SEO rules. Today their teams hand-write the variants per channel. We want an AI feature inside our product that auto-generates the channel-specific variant from the master, in each customer's brand voice, with rollout we control per-tenant.”
The shape we'd build
A channel-variant generation feature embedded in the existing product-detail UX. When the merchandiser opens a product, the feature reads the master record and the customer's brand voice configuration, generates a draft variant per active channel, and runs each one through a per-tenant brand-compliance guardrail before surfacing. The merchandiser sees the drafts inline, accepts or edits, and publishes through the platform's existing channel push.
Per-tenant cost ceilings cap token spend per customer organization so one large account can't drain the budget for the rest. The eval harness runs hundreds of canned generation cases across diverse product categories on every prompt change; releases gate on green. Rollout flags let the product team enable the feature per customer — design partners first, then a broader cohort, then full availability — and pull it back per-customer without a redeploy.
What the platform owns when we step back
The feature code — the inline UX component and the variant-generation endpoint — in the platform's repo, shipped through their pipeline. Per-tenant config for onboarding each customer's brand rules and channel inventory. The eval set, extended by the platform's team as new categories emerge. The cost-protection system with per-customer dashboards. The rollout-flag infrastructure that gates per-tenant availability. The feature ships to the platform's customers from the platform's product, on the platform's rollout schedule, with the platform's eng team operating it from day one.
Talk through an AI feature for your product
Start with a Discovery conversation. We'll talk through where AI would live inside your product, the safety surface around it, and what shipping it to your customers looks like.