Services / AI & Agentic

AI Consulting and Development.

Practical AI for organizations that need software to start working harder without handing customer data and proprietary context to a third party. We deploy on-prem GPU clusters running open-weight Hugging Face models, wire modern agent harnesses into developer workflows, build managed AWS Bedrock infrastructure for cloud-hosted inference, and run AI audits and safety guardrails for regulated deployments.

01 Hugging Face models on your hardware, behind your VPN

Local AI infrastructure

For organizations that can't or won't ship sensitive context to a third-party API, we deploy local LLM infrastructure on hardware you control. The latest open-weight models from Hugging Face (Llama, Mistral, Qwen, DeepSeek, gpt-oss, and code-specific models like Qwen Coder and Codestral) run on a GPU host and serve inference over your LAN or VPN with no traffic leaving your network. Editorial content, customer records, source code, internal documents, and regulated data stay onsite.

The deployment work covers GPU sizing for the parameter count and quantization you need, model serving (vLLM, llama.cpp, Ollama, Text Generation Inference) with OpenAI-compatible endpoints, RAG pipelines wired to vector databases (pgvector, Milvus, Qdrant), monitoring and observability, and the operational layer that keeps a multi-GPU host healthy under load. Provider abstraction stays consistent with cloud-hosted deployments so applications can route between local and cloud inference without rewriting code.

Experience: we run our own production local LLM stack on Proxmox with GPU passthrough, load-balanced across multiple inference nodes for high availability. The cluster serves agents and harnesses (pi.dev, Hermes, OpenClaw, OpenCode) over LAN and VPN to the team, fast and secure, with no inference traffic leaving the network. The same architecture pattern is what we deploy for clients who need onsite AI without the third-party data exposure.

02 Agent harnesses wired into developer workflows

Agentic development

Agentic development tooling has matured fast. Coding agents, autonomous task runners, and harness frameworks (Claude Code, OpenCode, Hermes, pi.dev, Aider, and similar) are now production-grade for the right kinds of work: refactors at scale, multi-file feature development, code review, test generation, and operations work like log triage and infrastructure provisioning. The wins come from picking the right harness for the task and integrating it with the team's actual workflow rather than treating it as a standalone tool.

What we do here: evaluate agent harnesses against your codebase and team conventions, integrate them with local or cloud inference backends, configure tool access and sandboxing, build custom MCP servers and tools when the agent needs to talk to your internal systems, and set up the review and observability layer so agent output is auditable. We also write the in-house automation (build pipelines, code review bots, ops agents) that the harnesses run inside.

03 Managed AWS Bedrock infrastructure with reproducible IaC

Cloud AI on AWS Bedrock

For workloads where local inference doesn't fit (frontier model access, burst scaling, geographic distribution), we build cloud AI infrastructure on AWS Bedrock with the same engineering discipline we apply to the rest of the stack: provider abstraction so model swaps don't require application changes, guardrails and content filters configured at the service layer, IAM scoped tightly to the application, observability into prompt/response traffic, and the cost monitoring that keeps token spend visible.

All of it ships as infrastructure-as-code (Terraform or CDK) so the deployment is reproducible across environments, version-controlled, and reviewable. The same IaC pattern covers Knowledge Bases for managed RAG, Agents for orchestrated tool use, and Bedrock Guardrails for content policy enforcement. Multi-cloud and hybrid deployments (Bedrock plus on-prem inference, with routing between them) are common shapes when latency, cost, or data-residency requirements pull in different directions.

04 Audits, safety guardrails, and governed deployments

AI audit and safety guardrails

Before adding AI to a regulated or customer-facing system, we run an audit covering data flow (what the model sees, where it goes, how long it persists), prompt-injection and jailbreak surfaces, output validation gaps, model and provider supply-chain risk, license and IP concerns for training data, and the operational controls that need to be in place before production traffic. The deliverable is a written report with prioritized findings and remediation paths, suitable for sharing with stakeholders or auditors.

For governed deployments (HIPAA, FERPA, GDPR, SOC 2, internal policy regimes), we implement the guardrails: input/output filtering, PII redaction, content-policy enforcement, structured-output validation, retrieval grounding with auditable context sources, rate limiting and abuse detection, and the logging and review workflows that make AI behavior reproducible after the fact. The Drupal AI Context Control Center module is one pattern we deploy where editorial workflows own the context library; equivalent patterns exist for non-Drupal stacks.