Hoplon InfoSec is your AI development partner. We design, build, and operate production LLM applications, custom ML systems, and GPU-accelerated inference infrastructure with security and evaluations woven through every layer from data pipeline to deployed model.
AI development at Hoplon is end-to-end engineering, not a workshop or a proof of concept. We work alongside your team to ship LLM applications, ML systems, and GPU infrastructure that go to production and stay healthy once they’re there.
Because our roots are in cybersecurity, every architectural choice is reviewed through a threat-model lens. The result is an AI product that performs in front of users and holds up in front of auditors.
Every AI product has its own shape, so we scope each engagement against what you’re actually trying to ship rather than a templated package. The eight tracks below cover the bulk of what teams ask us to build and we mix them freely on larger programs.
We build RAG pipelines on Pinecone, Qdrant, or pgvector with hybrid retrieval, reranking, and chunking tuned to your documents and your queries. The result is a grounded LLM feature that cites its sources and stops hallucinating its way through customer-facing answers.
We design multi-step agents with LangGraph, tool routing, and human-in-the-loop gates so high-impact actions are reviewable and retry-safe. Cost limits and structured outputs are wired into the loop, so an agent failure doesn't turn into an unbounded API bill or a malformed downstream call.
We run LoRA, QLoRA, and full fine-tunes on Llama 3, Mistral, and Qwen when hosted APIs can't match your domain language, output schema, or latency budget. You get a model that's smaller, faster, and demonstrably better at your task than a generic frontier model with a long prompt.
We train classification, ranking, forecasting, and recommendation models in PyTorch or JAX, with full experiment tracking so every metric in the deck traces back to a reproducible run. You get a model your data-science team can defend, retrain, and extend without reverse-engineering a vendor's black box.
We stand up feature stores, model registries, canary deployment, and drift detection so models ship to production safely and stay healthy without paging your team at 2am. Training-serving skew, stale features, and silent regressions get caught in CI rather than in customer complaints.
We design H100, A100, and L40S clusters with NVLink or InfiniBand and tune the inference stack vLLM, TensorRT-LLM, KV-cache reuse, speculative decoding for your throughput and latency targets. You serve more requests per dollar without renting a second cloud to keep the lights on.
We build vision and multimodal models for triage, search, OCR, and quality inspection, with the data pipeline and labelling workflow needed to keep accuracy steady over time. You ship a vision system that doesn't quietly degrade six months in as your imagery and lighting conditions drift.
We build versioned eval suites, prompt-injection defences, and automated red-team runs against your stack, so quality regressions and abuse paths surface in CI rather than in production. Every release ships with evidence that the model is at least as safe as the one it replaces.
Our delivery process keeps AI risk visible and decisions reversible. Every stage has a clear output, a clear owner on our side, and a clear point at which you can change direction including the option to decide AI isn’t the right tool, before the GPU bill arrives.
We define the business problem, the data you have, and the metric that will tell us whether the system is working in writing, before any model is selected. The output is a clear scope, a baseline number, and a model-or-not decision.
We audit your data for quality, leakage, PII, and bias, then build the evaluation set the model will be measured against. Eval design comes before model selection so we're optimising for a target you can defend, not whichever benchmark looks best.
We try the cheapest credible option first hosted LLM, small classical model, prompt-only and escalate to fine-tuning or training from scratch only when the evals demand it. You see real candidate outputs early, not slide-deck promises.
Engineers work in short sprints with code reviews, automated evals, and prompt-injection tests running on every change. Working features land at the end of each sprint, and regressions are caught the day they're introduced rather than the day a customer reports them.
We size the GPU footprint, tune the inference runtime vLLM, TensorRT-LLM, or Triton and apply quantisation, batching, and KV-cache strategies so tokens-per-dollar lands inside your budget rather than on the next quarterly cost review. Capacity is sized for real traffic, not a worst-case launch-day spike.
We move to production with a planned cutover, live dashboards for latency, cost, and quality from minute one, and a rollback path. Guardrails, audit logging, and red-team checks ship with the launch, not on a future sprint.
After launch we monitor drift, log interactions with appropriate privacy controls, and retrain or update prompts based on real usage. Your AI system gets sharper over time instead of quietly decaying as the world moves on.
We use the same frameworks the rest of the field uses, plus the in-house tooling we’ve built across engagements. Standards get you 80% of the way; the last 20% is where the model either ships or sits in a Jira ticket.
Three short examples of the kind of AI system that shows up in our engagements anonymised, but representative of the build, eval, and security work behind each delivery.
A regional bank needed sub-100ms fraud decisions on card transactions, with model risk documentation a federal regulator would actually accept. We built the gradient-boosted scorer, the feature store, the canary deployment pipeline, and the model-card workflow the risk committee signs off on every release.
A multi-site care network wanted a triage copilot grounded in their own protocols, not the public internet. We delivered a RAG system on a private vector store, fine-tuned a 8B open model for clinical phrasing, and wired full PHI-aware audit logging so every suggestion is traceable back to source documents.
A SaaS platform wanted an internal knowledge copilot without sending proprietary documents to a third party. We stood up a vLLM cluster on owned A100 nodes, built the indexing pipeline against Confluence and Notion, and shipped a Slack-native interface costing roughly 12% of the hosted-API alternative.
Both have a place. Wiring a hosted LLM into a product can be the right move when the task is generic and the data isn’t sensitive. AI development is what you need when the task is specific to you, the data is yours, and “good enough” has to be measurable.
An AI development partner is an engineering team that ships and operates AI systems on your behalf. The job is to build safely, evaluate honestly, and stay on the line after launch the things consulting decks and one-off prototypes usually skip.
Our engineers come from ML, infrastructure, and offensive-security backgrounds. Most specialise in two or three of the disciplines below and cross over freely.
Wire LLMs, fine-tuned open models, and custom ML into your data, your auth, and your services. Ship with evals and observability so quality is measurable from the first commit.
Design GPU clusters, tune vLLM and TensorRT-LLM, apply quantisation, and monitor utilisation so your AI features serve more requests per dollar without renting a second cloud.
Red-team prompts, monitor drift, log interactions with appropriate privacy controls, and patch guardrails as new jailbreak research lands so the platform stays inside its security posture.
We don’t deliver a thick deck and a hand-off. We deliver running systems, evals you can rerun next quarter, a partner who answers when the model drifts, and the documentation a regulator or board reviewer will actually accept.
Prompt injection, model extraction, and training-data poisoning are threats we design against from kickoff. Your AI ships with OWASP LLM Top 10 considered, not patched in.
Data, training, eval, deployment, and monitoring under one roof with one engineering lead. No vendor chain pointing fingers when accuracy slips after launch.
OpenAI, Anthropic, open models on your GPUs, classical ML we choose based on your cost, latency, privacy, and accuracy needs, and document the trade-offs in plain English.
Every model and prompt change runs against versioned eval sets. You see whether last week's clever tweak actually moved the metric or just made the demo look better.
We treat tokens-per-dollar and GPU utilisation as first-class metrics, so the inference bill stays predictable as traffic grows instead of becoming a quarterly surprise.
No mystical AI vocabulary, no inflated capability claims. Clear scope, clear metrics, clear next steps the same language your engineers and your board both speak.
Short, honest answers. If your question isn’t here, send it to our team and we’ll add it.
We build production AI systems end-to-end: LLM applications (RAG, agents, fine-tunes), custom ML models (classification, ranking, forecasting, computer vision), MLOps platforms, and GPU inference infrastructure. We don't sell workshops or "AI strategy" decks the deliverable is always a running system with evals, observability, and a handover your team can operate.
We try the cheapest credible option first. If a hosted model with a good prompt hits your eval target, that's the answer often it does. We escalate to fine-tuning when domain language, output format, latency, privacy, or per-request cost makes a hosted API the wrong fit. The decision lives in a written trade-off document, not a vibe call.
You do. Fine-tuned weights, training pipelines, prompts, evals, and infrastructure code are all delivered to your repos and registries. We don't keep models locked behind a Hoplon service. If you stop working with us tomorrow, everything we've built continues to run inside your environment.
Most engagements run six to sixteen weeks end-to-end. A focused RAG application can ship in six to eight; a custom-trained ML model with feature store and serving infrastructure often runs ten to twelve; a fine-tuned model with GPU cluster work and red-teaming can push toward sixteen. We give you a firm timeline as part of scoping, with a clear go/no-go review at week one.
Yes that's usually how it works. We integrate with Snowflake, BigQuery, Databricks, Redshift, S3, and on-prem warehouses, and we use whatever orchestration you already have (Airflow, dbt, Dagster). We bring opinions about what works, but we're stack-agnostic by default and won't ask you to rebuild infrastructure that's already serving your team.
Yes for on-prem and hybrid clusters, we help with H100, A100, and L40S sizing, interconnect (NVLink, InfiniBand), and the K8s or SLURM orchestration on top. For cloud, we design across AWS, Azure, GCP, and specialised providers like Lambda or CoreWeave. The decision between cloud and on-prem usually comes down to traffic, data sensitivity, and total cost of ownership at your projected scale.
It means prompt injection, model extraction, training-data poisoning, and PII leakage are designed against from the first sprint, not patched in after a public incident. Practically: input/output filtering, structured output enforcement, audit logging, scoped tool access for agents, automated red-team runs against OWASP LLM Top 10, and model cards documenting data lineage and known limits.
A 30-minute call is enough to scope the right engagement. No sales deck, no checklist questionnaire just a conversation about what you’re trying to ship, the data you have, and where the risk sits if it goes wrong.