Gammaxon
Ruthless Prioritization
We assess where AI will pay off in your workflows, and just as importantly where it won't. You get a ranked roadmap tied to real cost and real risk, not a vendor's wish list.
AI Solutions via Forward-Deployed Engineers (FDE)
We solve your problems by leveraging AI to eliminate bottlenecks and ship end-to-end solutions.
Gammaxon provides AI focused Forward-Deployed Engineers (FDE) as members of your team, to help you decide what to build, what to buy, and what to run in-house. FDEs then build the production workloads side-by-side your engineers. This includes AI deployments that live entirely on your own hardware, disconnected from the internet.
Solving problems is our passion · We use what we recommend
Forward-DeployedEngineers who build with your team
100+AI enabled apps we have deployed
Data meshdesigned to be decentralized
Air-gappedon-prem AI, fully offline capable
Services
AI Solutions via Forward-Deployed Engineers.
Most organizations don't have an AI problem, they have business opportunities waiting to be unlocked. We work side-by-side with your team, in your systems, to design solutions that will solve your problems and make your business more profitable, more streamlined, and more awesome.
Design the Dream
Model selection and routing, retrieval and data pipelines, evaluation harnesses, guardrails, observability, cost controls and more. We outline these concretely, so that your engineers can effectively build against it.
Build Side-by-Side
Forward-Deployed means exactly that, our engineers sit with your team and ship production workloads together, so the capability stays in your organization and lives on forever.
Security & Governance built-in
Prompt-injection and data-exfiltration threat modeling, tenant isolation, audit trails, policy enforcement, and more are designed in from the start by a team whose day job used to be adversary tradecraft.
Cost Engineering
Token economics, caching strategy, model right-sizing, and the build-versus-buy math are included in our analysis. We know when moving a workload onto your own hardware pays for itself.
Agentic Systems That Last
Tool design, evaluation, and failure containment for agents that touch production. We build and operate these ourselves everyday, so the advice comes from running them, not theorizing about them.
Not sure where AI fits yet?
That's the normal starting point. You will find that a short conversation with our team, will usually clarify more than weeks of research.
On-premise AI
Cut the monthly bill. Work offline. Protect your intellectual property.
Metered API calls to someone else's models are a permanent operating expense and a permanent dependency. We design and stand up on-premise AI so your workloads run on hardware you control. This means there is no per-token meter, no data leaving your network, and full function when the internet is gone.
Predictable cost
Trade a variable per-token bill for capital hardware you already know how to budget for.
Fully disconnected
Air-gapped operation. Inference, retrieval, and agent workflows keep running with zero external calls.
Right-sized hardware
We size GPU and memory to your actual workload, from a single workstation to a rack.
Data never leaves
Regulated, classified, and contractually restricted data stays inside your boundary by construction.
DEPLOYMENT MODE
Cloud API
metered
Hybrid routing
mixed
On-premise
fixed
- ● egress to model providers none
- ● offline operation supported
- ● data residency your boundary
- ● model updates on your schedule
Illustrative comparison of deployment models. Actual savings depend on workload, utilization, and hardware. We model yours before you buy anything.
Flagship deception
RipTide: high-confidence deception for human and agent-driven attacks.
A lightweight managed sensor you install on a box you already own. It watches which ports actually get probed, rebinds itself to the hottest ones, and answers with infrastructure that looks real and is entirely fake. An anomaly score is a guess. A hit on a decoy credential is an answer.
Multi-cloud IMDS
AWS, GCP, and Azure instance-metadata endpoints. This is the first thing an SSRF or a compromised workload reaches for.
Kubernetes API server
A convincing API-server surface. Enumeration attempts against it are unambiguous, because nothing in your cluster should be knocking here.
Git smart-HTTP & CI API
A fake repo host seeded with zero-permission AWS canary keys. The keys grant nothing; using one tells us exactly who took the bait.
MCP JSON-RPC agent trap
Answers initialize and tools/list like a real MCP server,
exposes a get_ci_secrets leak point, and plants a per-session semantic
canary, purpose-built to catch agentic attackers probing your tooling.
SSRF bait chains
Decoy apps that chain into the IMDS persona and surface open-redirect bounce, DNS rebinding, and XXE attempts.
LLM-backed super-port
Model-driven responses on unclassified ports for deeper engagement. Kill-switched and cost-capped; ships disabled until you turn it on.
From install to alert
- 01Install
One command on macOS or Linux. Runs as a LaunchAgent or systemd unit.
- 02Heartbeat
The sensor checks in every 30 seconds and picks up runtime config from the web app.
- 03Choose personas
Toggle which decoys are live. Passive observation auto-rebinds to the ports actually being probed.
- 04Route alerts
Notifications, integrations, and OCSF 1.3.0 export straight into Splunk, Elastic, or Sentinel.
Canary credentials are non-privileged and exist only for detection. RipTide detects and alerts; it does not take automated destructive action against attacker infrastructure.
Included, not upsold
Internal network discovery, bundled with the sensor.
The same RipTide agent that runs your decoys will, when you opt in, walk your RFC1918 space and fingerprint what answers. You get a live internal asset inventory, the capability you'd normally buy a separate discovery platform for, as a side effect of a sensor you already deployed.
- RFC1918 asset discovery. Sweep the private ranges you authorize, nothing else.
- Service fingerprinting. What is actually listening, not just what replied to a ping.
- Network Inventory. Internal assets alongside your external attacker's perspective in one view.
- Opt-in and bounded. Off until you enable it, rate-limited, and scoped to ranges you declare.
Internal discovery is deliberately separate from external scanning. Turning it on never relaxes the safety controls that keep external scans off private and link-local targets.
$ riptide discover --range 10.20.0.0/22 ✓ scope authorized 10.20.0.0/22 (1,022 hosts) ✓ sweep complete 18.4s HOST SERVICE FINGERPRINT 10.20.1.14 ssh/22 OpenSSH 9.6 10.20.1.41 https/443 nginx 1.25 · TLS 1.3 10.20.2.8 postgres/5432 PostgreSQL 16 10.20.2.90 rdp/3389 MS-RDP · NLA off 10.20.3.5 k8s-api/6443 RipTide decoy ✓ 47 assets 12 new since last sweep ! 1 finding 3389 reachable, NLA disabled
Questions
Straight answers.
It starts with a scoping conversation, not a proposal. From there we typically run a focused assessment of where AI pays off in your workflows, deliver a reference design your engineers can build against, then embed to ship the first production workloads with your team. The goal is that the capability stays with you after we leave.
No. We work inside your environment and your boundary. For regulated, classified, or contractually restricted data, the on-premise deployment model exists specifically so nothing has to leave. Inference, retrieval, and agent workflows all run on hardware you control.
They're zero-permission by design. The credentials grant no access to anything. Their only function is to fire a high-confidence alert the moment someone tries to use one.
Yes. Self-hosted and co-managed deployment is supported, and our on-premise AI practice exists specifically to make disconnected operation practical. Talk to us about your environment.
Let's scope it.
Tell us where you are with AI and what you're trying to build. We read every message and get back to you personally, usually within one business day.