Cybernip is an AI and machine learning consultancy. We design, build and operate generative AI and agentic systems on AWS and Azure, and we engineer inference on AWS Silicon.
The same engineers take a system from the first model call to audited production. Nothing is handed to a different team halfway through.
Retrieval, generation and orchestration built around your data and your constraints. We choose models by running them against your evaluation set, and we put cost controls and observability in place before the first release rather than after the first incident.
Agents that act inside your ERP, CRM and ticketing systems with explicit permissions and a full trace of every step. Where a managed harness fits, we use it. Where it does not, we write the orchestration ourselves.
Training and inference engineering on AWS Trainium and Inferentia with the Neuron SDK, next to the GPU estates that still make sense. We measure price-performance on your workload instead of quoting a vendor slide.
Cybernip started in digital identity and certification for high-assurance sectors. We bring that to AI: identities for agents and tools, signed and attributable actions, and audit trails a regulator will accept.
This is how we look at an agent in production: each tool call, each model call, how long it took and what it cost. If we cannot show you this view, we have not finished the work.
The platforms moved a long way in twelve months. These are the pieces we are deploying for clients now, not ideas we are watching from a distance.
Orchestration loops with isolated sessions, persistent state and exportable code. A prototype becomes a system without a rewrite.
Bedrock AgentCore harness · Strands Agent Harness SDKTrace-driven evaluation, batch scoring and A/B tests against your own data, in whichever framework your team already uses.
AgentCore Evaluations · custom judges and golden setsAgents that run multi-day jobs on dedicated compute, registered centrally, with permissions scoped per tool.
AgentCore Runtime instances · Agent RegistryTools exposed over the Model Context Protocol and agents talking over A2A, so you are never locked to one vendor's loop.
MCP · A2A · gateway and identityClaude, Amazon Nova, OpenAI and open-weight models tested side by side on Bedrock or Azure AI Foundry, with routing and fallback designed in.
Amazon Bedrock · Azure AI FoundryFine-tuning, distillation and custom pre-training where general models fall short, served on the silicon that makes the economics work.
Nova Forge · Neuron on TrainiumDrag to rotate. Hover or tap to separate the layers.
Inference is now the largest line in most AI budgets. AWS designs the accelerator, the interconnect and the compiler together. We specialise in moving real workloads onto that stack, and in telling you plainly when a workload should stay where it is.
Not every model belongs on Neuron. Unsupported operators, very small batches or a weekly retraining cycle can still favour GPUs. We benchmark first and show you the numbers either way.
Before anyone migrates anything, we run the same prompts and the same traffic shape on both platforms and write the result down. Sometimes the candidate wins on cost and loses on latency, as in this sample. That trade-off is yours to make, with real numbers in front of you.
Cost per million tokens, p95 latency and utilisation on the current estate. This baseline is what every later decision refers to.
Port with Neuron, fix unsupported operators, shard with NeuronX Distributed. Most problems appear here, which is where we want them.
Identical prompts and traffic shape, side by side. Throughput, cost per token and quality parity against your evaluation suite.
Canary a slice of traffic on SageMaker or EKS, watch the traces, then move the rest. GPU capacity goes back to the workloads that need it.
Our engineers work in your environment, under your access policies, next to your people, until the system runs and your team can operate it without us.
A rigorous review of one candidate workload or an existing AI system: architecture, data readiness, cost model, risk, and a plan in priority order.
Fixed-scope delivery of a production system: the agent or generative workflow, its evaluation suite, guardrails, identity and observability, deployed in your cloud account.
A standing engineering team for organisations running several AI systems: continuous evaluation, model and silicon migrations, cost management and incident response.
These appear in every statement of work we sign. They are the reason clients in regulated sectors are comfortable letting us into their systems.
We work inside your AWS or Azure account, in your region, under IAM roles you grant and can revoke. We do not copy data to our own systems to work on it.
Model, platform and silicon choices are backed by benchmarks you can rerun yourself. If we cannot measure something on your workload, we say so.
Assessment and Build engagements are fixed in scope and price before they start. Changes are agreed in writing, never discovered on an invoice.
Code, infrastructure definitions, evaluation sets and runbooks are handed over in full. We train your engineers to operate the system, and to replace us if they choose to.
NDAs as standard, background-checked engineers, least-privilege access and a written security review in every build. Our PKI background means we take identity and attribution seriously.
We do our best work in regulated and operationally complex environments, where an agent's mistake has a real cost and the inference bill is reviewed by someone in finance.
Cybernip is an independent consultancy with engineering in Guadalajara, headquarters in Mexico City and a US office in Houston. The firm began in digital identity and certification for high-assurance sectors, moved into data engineering, and over the last two years has concentrated on applied AI: generative systems, agents and the silicon they run on.
We are platform-literate and vendor-neutral. We are a member of the AWS Partner Network and a Microsoft AI Cloud Partner. Recommendations come from evidence on your workloads, and we will tell you when a platform, a model or a chip is the wrong choice for you.
Two legal entities serve clients across the Americas. Both work to the same engineering standards and the same confidentiality terms, with the same people.
Mexico City and Guadalajara. Serves clients in Mexico and Latin America.
Houston, Texas. Serves clients in the United States and international clients who prefer a US counterparty.
Tell us about the workload, the platform you are on and what a good outcome looks like. A senior engineer, not a salesperson, replies within two working days.
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