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, Resilient to Outages, & On Your Terms
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+problems solved with AI in production
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
Which models to use, how your data feeds them, how you prove the results are right, and how you keep spend under control. We put all of it in a concrete design your engineers can build against without guessing.
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.
What we've built
A few of the 100+ problems we've solved with AI.
We use what we recommend. Each example below is a real problem we solved and put into production, packaged the way we'd brief your board: outcome first. The machinery is one tap away when your technical team wants to go deeper.
AI that runs entirely on hardware you control.
The AI pilot works. That turns out to be the expensive part. Every month the invoice climbs, because success is priced per token on someone else's hardware. Legal keeps asking where the data actually goes, and nobody has a crisp answer. Then one afternoon the provider has an outage, and every workflow built on top of it stops at the same time.
This is the moment we move it home. We size the hardware to the real workload, from a single workstation to a rack, stand the models up inside your own walls, and point your workflows at machines you own. The meter stops. The data stays. The internet becomes optional.
From then on, the bill is a number you chose once, not a curve you watch nervously. The data question gets a one-word answer: here. And on the day the rest of the world's AI goes down, yours keeps working.
- Predictable cost. Hardware you budget for once, instead of a per-token meter that never stops running.
- Your data stays home. Regulated, classified, and contractually restricted data never leaves your boundary.
- Works fully offline. Inference, retrieval, and agent workflows keep running with zero external calls.
Technical Deep Dive
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.
RipTide: an alert that actually means something.
It is 2 a.m. and someone is inside a network that looks a lot like yours. They know what they are hunting for: cloud credentials, admin consoles, a code repository full of secrets. The security tools are all working as designed, and that is the problem. The intruder's first quiet steps are buried under the day's thousand maybe-alerts, and nobody chases maybes at 2 a.m.
RipTide changes the ending. A small sensor on a machine you already own presents convincing fakes of exactly those prizes. No employee has any reason to touch them, so they wait in perfect silence until the intruder reaches for one. Then a single alert fires. Not an anomaly score. Not a maybe. An answer.
So the story ends quietly: the intrusion is caught the night it begins instead of months later, the incident never becomes a headline, and your team gets back what alert fatigue took from it: trust in its own alarms.
- High-confidence alerts. Decoys have no legitimate users, so a touch is a finding, not noise.
- Built for AI-driven attacks. Traps designed for agentic attackers as well as human intruders.
- Fits your stack. One-command install, and alerts flow straight into Splunk, Elastic, or Sentinel.
Technical Deep Dive
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.
A live inventory of what's actually on your network.
It starts with a simple question in a tense room: "whose machine is 10.20.2.90?" Silence. The official inventory is a spreadsheet that was stale the day it was saved, and the network has grown quietly ever since: a demo server nobody retired, a vendor box nobody documented, a forgotten machine with remote desktop open to anyone who finds it. You cannot protect what you do not know you have.
Discovery changes the answer. The same RipTide sensor that runs your decoys will, when you opt in, quietly walk your network and take attendance: every address that answers, every service actually listening. The first sweep tells the truth. Forty-seven assets, twelve that were on nobody's list, and one of them wide open.
Now the question has an answer before anyone needs to ask it. The forgotten machine gets fixed before someone else finds it, the auditor gets a live inventory instead of a shrug, and the list stays current on its own, because the sensor never stops taking attendance.
- Always current. A live inventory instead of a spreadsheet that was stale the day it was saved.
- What's really listening. Fingerprints the services that answer, not just the hosts that reply to a ping.
- Opt-in and bounded. Off until you enable it, rate-limited, and scoped to ranges you declare.
Technical Deep Dive
- 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
Your problem is the next example.
Every deployment above started as a conversation about a bottleneck. Tell us about yours, and we will tell you exactly what we would build.
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.
Governance is designed in from the start, not bolted on afterward. Threat modeling for prompt injection and data exfiltration, tenant isolation, audit trails, and policy enforcement are part of every reference design, built by a team whose day job used to be adversary tradecraft. Your auditors get evidence, not assurances.
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.