Two practices
Enterprise architecture + agentic implementations
One practice designs the system the agents live on — data, integrations, ops, observability. The other builds the agents themselves. Both senior-engineered, both inside Agix.
Scale your AI organically.
Agentic Implementations
Architect-led delivery, built on tested AI patterns.
We install the enterprise AI foundation — and ship the agentic systems that prove it works. Agix Agents, copilots, second-brain knowledge, full-stack deployments, AI-powered products. The features show what's possible; the foundation is what your team owns and grows.
What Agix builds
Five categories of agentic systems,
built to the same bar.
Each capability page carries a working demo of the pattern in action. Pick a capability to see the live surface, the architecture behind it, and what shipping it into your stack looks like.
Agents
Agix Agents.
Autonomous teammates with defined roles — researcher, analyst, ops coordinator, account assistant — that take work off your team, learn from chat feedback, and get sharper for your team over time.
Copilots
AI Copilots.
In-app AI assistants that help your team draft, review, and decide — inside the tools they already use.
Second-Brain
Knowledge Systems.
AI that reads everything your company knows — docs, threads, data — and answers like the deepest expert on your team.
Full-Stack
Full-Stack Deployments.
Bring an enterprise AI idea to life end-to-end — architecture, data layer, agentic logic, interface, deployment, and ops. Built on tested patterns and handed off to your team.
Products
AI-Powered Products.
Customer-facing AI features built into your product — chat, search, recommendations, generation — with the safety and observability your team can run on its own.
How Agix is built
Two practices, one firm.
Enterprise architecture and the agentic systems that live on it — architect-led, framework-driven, held to the same production bar. Each practice makes the other stronger.
Two engagements
Ship-to-Own or Managed Service
Own the foundation outright after a fixed-scope build, or keep Agix operating and tuning it on a recurring engagement. Same architecture, same agents — different commercial fit.
One bar
Production-grade or it doesn't ship
Every system gets its own eval harness, observability, cost protection, and rollout controls — and, where it earns its place, a recursive-learning loop. Demo-grade isn't a release we ship.
Three claims, engineered
Lower cost. Higher quality.
Real scale.
Every Agix system is built and evaluated against all three. Not the one that's easy to demo — the three that decide whether it survives the second quarter in production.
01
Lower token cost
Engineered for production economics. Smarter routing, leaner prompts, tighter context — the difference between a demo budget and an enterprise bill.
02
Higher AI quality
Built and evaluated against your real cases, not generic benchmarks. Reliability you can stake your name on, with the eval harness to prove it on every change.
03
Real scale
Systems that work past the demo, past the pilot, past the first hundred users. Built for enterprise volume from day one — observability, safety, and cost protection included.
Two ways to work
Same engineering bar.
Different commercial fit.
Own the system outright, or partner long-term to operate and evolve it. The work is the same; the commercial fit is yours to pick.
01 · Fixed-scope build
Ship-to-Own
A fixed-scope build with full handoff. Your team owns the code, the prompts, the playbooks, and the agents — run them, evolve them, take them anywhere.
Best for
Teams that want a foundation they control and an engineering org capable of running it.
02 · Recurring engagement
Managed Service
Agix builds and continues to operate, evolve, and tune the system on a recurring engagement. A long-term partner for teams that want the lift without the operational overhead.
Best for
Teams that want a specialist AI architecture practice as a function — without building or staffing it in-house.
Principles
What governs every build.
- Production over pilots
- Cost, quality, and scale — all three
- Architect-led builds on tested patterns
- Eval before you ship; observability before you scale
- Buyer-language, not insider jargon
- Agentic implementations, not stitched-together demos
Start with Discovery
Two weeks of structured discovery. A production-grade plan, not another deck.
Best suited for teams with a working product or operation and AI ambitions who want a production-grade agentic system — not a pilot that leaves technical debt when the engagement ends.