An AI initiative usually starts as a thesis: 'this should be possible.' Full-stack deployments are the engagement that turns that thesis into a live, observable, owned system. Architecture, data layer, agentic logic, interface, deployment, and operations — built on tested patterns and handed off to your team.
A full-stack deployment is the engagement that delivers a complete AI-native platform — not a slice. Where the other four capability tiers each ship one piece of the architecture, this one ships the whole thing, coherent, in one engagement, on the same foundation. The buyer who picks this tier has a thesis but no AI capability yet, and wants a working production system at the end.
Six layers — the Agix reference architecture, instantiated for one client. Every other capability tier is a slice of this same stack; full-stack is when you commission all of it at once.
Multi-tenant data isolation by default. Encryption at rest. AI-shaped schema — embeddings, context, telemetry — built into the model from day one. Connectors to whatever you already run on, so the AI doesn't start from a cold knowledge base.
The Second-Brain that powers everything above it. Embeddings + governed index + citation surface, queryable through the same API by humans and agents. Versioned, access-controlled, freshness-tracked.
Role definitions, memory store, tool gateway, critic loop, eval harness — the Agents runtime, instantiated for as many roles as your engagement requires. Often more than one: a drafting agent + a triage agent + an account assistant, all on the same substrate.
Copilots embedded in your team's tools — admin panels, Slack, the CRM, your internal review UI. Where work already happens, intelligence shows up there instead of in a separate app.
AI-Powered product features — chat, search, generation, personalization — built into your product with per-tenant safety, brand-aligned voice, and rollout flags. Ships to your customers, not just your team.
Infra-as-code, observability, per-tenant cost protection, CI with eval gates on every change. Your platform runs the same way every other production service in your org runs — your team doesn't have to learn new ops to operate it.
A full-stack engagement ships a platform your team operates. Five concrete artifacts come with it — and the foundation is the thing that keeps compounding after we step back.
Code, infra-as-code, configurations, secrets handling, deployment pipelines. Not a sandbox you can't replicate — a production system your team operates from day one of go-live.
Regression suites for the data pipeline, the knowledge layer, each agent, each copilot, and each customer-facing feature. Integrated into CI so any change runs the gates automatically.
Documented operational procedures for each layer — incident response, cost-spike triage, prompt updates, eval-set extension, tenant onboarding. Written for the team that has to use them, not for an audit.
Per-tier traces, per-tenant cost views, refusal logs, audit trails. The same dashboards Agix uses to run the system during Build are the dashboards your ops team uses to run it forever.
Every meaningful trade-off documented — why we picked this vector store, why memory lives here and not there, what would have to change to swap providers. Your team can extend any layer without needing us to be in the room.
Four buyer situations where commissioning a full-stack deployment is the right move.
Greenfield. The leadership team believes AI changes the shape of the business; the engineering team hasn't built anything AI-native yet; you want a working production system out of a single engagement, not a phased multi-year build.
You can see that you need all four other tiers. You also know that commissioning them separately would produce four federated, half-compatible systems. Full-stack is the engagement that delivers them as one architecture with one direction.
Strategic buyer. You want to own the architecture, not rent capability. The Agix internal platform — second-brain + agent runtime + critic loop + eval harness — is the shape you want, tuned to your domain.
You're early enough to do it right the first time. Full-stack engagement up front is materially cheaper than retrofitting later, when every layer has to be opened up and re-architected against shipped traffic.
Full-stack is the umbrella; most engagements are smaller. Here's how to tell which slice you actually need.
Pick the specific tier that addresses your gap. Copilots, Second-Brain, AI-Powered Products, and Agix Agents are each engagements you can commission on their own. Full-stack is for when you need them all at once.
Explore AI Copilots →Most companies have institutional knowledge scattered across drives, wikis, threads, and tickets. If the gap is making that queryable — not building a full platform — start with Second-Brain. Other tiers can layer on later.
Explore Second-Brain Knowledge Systems →Customer-facing AI feature with per-tenant safety and brand-aligned voice is its own tier. If you have a working product and want to add a chat/search/generation feature to it, start with AI-Powered Products.
Explore AI-Powered Products →You have a knowledge layer, you have evals, you have ops. What you need is autonomous teammates with roles. Start with Agix Agents — same runtime, same handoff bar, scoped to the roles you commission.
Explore Agix Agents →An illustrative scenario showing how a full-stack engagement shows up in practice. The shape mirrors how Agix builds end-to-end platforms; the client and specifics are composite.
“We're building a trial-operations platform for sponsors and CROs. Multi-tenant by design, regulatory-grade audit trail, and AI-native at the core. We want every surface — the trial data layer, the protocol + regulatory knowledge layer, the autonomous monitoring agents, the in-product analyst copilot, the sponsor-facing dashboard — built coherent, in one engagement, by one architecture team. We'd rather get the foundation right once than retrofit AI onto a non-AI codebase a year from now.”
A complete reference-architecture deployment tuned to clinical trial operations. Multi-tenant trial data with encryption at rest, per-sponsor cost protection, and an audit trail engineered against the regulatory bar. A knowledge layer holding protocols, regulatory guidance, prior amendments, and historical query/answer history — queried through one API by humans, by monitoring agents, and by the sponsor-facing analytics surface.
Autonomous monitoring agents that watch trial event streams, surface anomalies, and file findings into the existing review queue. An in-product copilot for the operations team that drafts protocol-deviation reports and amendment correspondence. A customer-facing sponsor dashboard with brand-aligned voice and per-tenant rollout flags. Every layer built coherent — the knowledge layer the agents query is the same one the dashboard reads from; per-tenant cost protection is enforced once at the runtime layer, not bolted onto each feature.
A complete AI-native platform that scales without re-architecting. Every layer is documented, evaluable, observable, and operable by the platform's engineering team — code in their repo, infra in their cloud, dashboards in their environment, runbooks for every surface. The architecture is the foundation; the features are the proof points. When the team wants to add a new agent role or a new sponsor-facing feature a year from now, the substrate to do it is already there.
Start with a Discovery conversation. We'll talk through the thesis you want to prove out, what a complete platform would look like, and what installing it into your team looks like.