Deploy AI
into your
environment.
METECH forward-deploys senior engineers into your team to ship AI into your real environment — managed cloud, hybrid VPC, or fully on-prem — and stays until the system actually works in production.
AI strategy isn't the bottleneck. Your environment is.
Run a brainstorm. Buy another AI tool. Call the demo a success. Leave the hard parts for later.
Model providers ship the engines. OpenAI, Anthropic, Google, and open-weights keep getting stronger. The hard part has moved closer to the business.
Every business is a unique environment. Different data, tools, access rules, compliance constraints, and on-prem requirements. AI has to fit that environment — not the other way around.
That's where METECH fits. We forward-deploy senior engineers into your team, choose one use case worth shipping, and stay until the system works in your environment.
What makes AI usable. Beyond the demo.
We deploy AI wherever your data is allowed to live.
Managed cloud
Anthropic · OpenAI · AWS Bedrock · Vertex
Fastest path. Data leaves your network under contractual controls.
Hybrid VPC
Self-hosted models in your AWS or GCP account
Data stays in your VPC. You control keys, egress, and audit.
Fully on-prem
vLLM · sglang · local VLMs · embedding + reranker stacks
Air-gapped, regulated, or data that can't leave the building.
One use case. One embedded senior engineer. First deployment in 2-3 weeks.
Embed inside the business.
Forward-deployed from day one — your office, your Slack, your repo. We map the data, systems, and constraints involved, and choose one use case worth shipping first.
- Business context
- Data + system audit
- Use-case selection
- Success criteria
Build in your environment.
We ship the first version against real data and tools — agents, retrieval, computer vision, or custom software — on the surface your data is allowed to live on.
- Working system
- Real data, real evals
- Team checkpoints
- Architecture notes
Validate and hand over.
We demo the deployed use case with honest numbers, document what was built, and leave a clear path for production or the next use case.
- Live demo to leadership
- Evaluation results
- Handover package
- Scale recommendation
Make
It
Easy.
AI demos are easy. Your environment is hard. The model works in a sandbox, then stalls when it meets your data, access rules, edge cases, legacy systems, compliance posture, and real users.
We make deployment easier. Put a senior engineer inside the problem. Ship the first system in weeks. Measure it honestly. Then decide what deserves to scale — on whichever surface your data is allowed to live.
What we ship. AI built for your environment.
Forward-Deployed Sprint
A 2-3 week engagement with a senior engineer embedded in your team. One use case, working system at the end, honest evaluation, and a clear path to scale.
Agents, Copilots & RAG
LLM systems that search, reason, draft, decide, and act inside your tools and data. Tool-use, approvals, retrieval over your knowledge, and grounded answers your team can trust.
Computer Vision Systems
Production CV pipelines — object and person detection, semantic video search, event analytics — including fully on-prem VLM, embedding, and reranker stacks.
Private AI Deployment
The infrastructure layer behind everything we ship: managed cloud (Bedrock, Vertex), self-hosted models in your VPC, or fully on-prem with vLLM and sglang. AWS-certified, GCC 2.0 experience.
Proof we can ship. AI, computer vision, and the infra to run them.
Nex Gen Video Intelligence
AI agents for commercial cleaning ops
AI marketing dashboard
EventSense audience analytics
Modular ERP
GCC 2.0 government cloud
Why teams pick us. Close to the problem, serious about shipping.
Senior engineers from day one
Every engagement is led by engineers who have shipped production AI, software, and cloud systems. No junior delegation, no offshore handoff.
Embedded with your context
Forward-deployed means in your team, your Slack, your repo — close to the data, tools, constraints, and decisions that actually shape the system.
Deploys where your data lives
Managed cloud, hybrid VPC, or fully on-prem with vLLM and self-hosted models. We meet your compliance reality instead of pretending it doesn't exist.
Built for handover
Code, docs, evaluations, and architecture notes are part of the delivery. Your team can read, extend, and own everything we ship.
Clear answers. Before the intro call.
What is forward-deployed AI engineering?
Forward-deployed AI engineering means senior engineers embed inside your team — working from your repo, your Slack, your data, your constraints — until a real AI system is shipped in production. It is the opposite of arms-length consulting: we sit close enough to the problem to actually solve it.
Can METECH deploy AI on-prem?
Yes. We deploy fully on-prem AI systems using vLLM and sglang for inference, self-hosted VLMs and LLMs, and our own embedding and reranker pipelines. Our internal Nex Gen Video Intelligence product runs entirely on-prem, including all AI models. This is the right surface for air-gapped, regulated, or sensitive data that cannot leave the building.
What about regulated environments and government data?
We have shipped to Singapore government agencies on GCC 2.0 (Government on Commercial Cloud) in partnership with ST Engineering. Hardened controls, audited access, AWS-certified architects on the team. We can deploy AI into environments where most vendors cannot go.
How long does the first engagement take?
Most first deployments are scoped for 2-3 weeks. The goal is not to solve every AI opportunity at once; it is to choose one valuable use case and prove what should happen next.
What can you deploy first?
Common first use cases include internal copilots, RAG over company documents and databases, AI agents that act inside business tools, computer vision systems (object detection, semantic video search, event analytics), and operations software with AI built in.
What happens after the first deployment?
You get the working system, evaluation results, code, documentation, and a practical recommendation: scale it, refine it, pause it, or move to the next use case. Everything is built for your team to own.
Do you sell a product?
No. METECH is a forward-deployed engineering studio. We build the first useful version around your business context, then help you decide whether to scale, productize, or hand it over internally.
Is this the same as AI consulting?
It is more hands-on. We can advise, but the core offer is forward-deployed engineering: senior builders embedded with your team until there is working software to judge.
Have one use case in mind?
Tell us the operation, team, or customer experience you want to improve with AI. We'll respond with an honest fit-or-not take.