AI · Infrastructure · Security · Software
Autonomous agents, multi-model orchestration and production AI systems — designed and deployed by engineers with three decades of infrastructure, security and software depth. No learning curve. No layers. Direct access to the people who architect and build your AI.
While others are still circling proof-of-concepts and vendor demos, we're deploying autonomous agents, orchestration layers and intelligent automation into production environments — built on three decades of infrastructure, security and software engineering. The gap between an AI demo and an AI system that survives contact with your business? It's the engineering underneath.
Every engagement led by senior engineers who understand AI, infrastructure, security and code as one system. Every deliverable governed, documented and measurable.
Production AI systems — not slides about AI. Autonomous agents, RAG architectures, multi-model orchestration and tool-use pipelines, deployed with evaluation frameworks, guardrails and governance built in from day one.
Resilient platform foundations across cloud, hybrid and on-premise environments. Designed for reliability, governed for cost, and built to evolve without re-platforming.
Defence in depth, from identity and posture management to detection engineering and incident response. Operating models that hold under real pressure — not bolt-on afterthoughts.
Fifteen years of building enterprise applications, API platforms and systems integration — rooted in the infrastructure and security knowledge that most development teams never have. Code that's secure by design because we understand every layer it sits on.
Most firms treat AI as a bolt-on. We treat it as an engineering discipline — with the infrastructure architecture, security posture and software rigour that production AI demands at scale.
Multi-step AI agents that reason, plan and execute — with tool use, persistent memory and human-in-the-loop oversight. We design agent architectures for enterprise environments where reliability, auditability and security are non-negotiable.
Retrieval-augmented generation built on proper data engineering. Vector stores, embedding pipelines, chunking strategies and reranking — architected for precision and grounded in your proprietary data, running on your infrastructure, under your control.
We connect AI to your operational systems through Model Context Protocol servers, function calling and structured outputs — so your models can query databases, invoke APIs and orchestrate workflows autonomously, with full observability at every step.
Multi-model architectures that route the right task to the right model at the right cost. Intelligent fallbacks, latency-aware inference, evaluation pipelines and prompt engineering that treats LLMs as production software components — not magic boxes.
Content filtering, output validation, PII detection, audit logging and regulatory compliance frameworks. We engineer AI systems that your legal, risk and compliance teams can sign off on — because we've spent decades building systems to standards that regulators actually inspect.
You can't optimise what you can't measure. We build eval harnesses, regression testing, latency monitoring, cost attribution and quality scoring into every AI deployment — so you know exactly how your models perform, what they cost and where they degrade.
The people who design your agent architecture are the same people who write your orchestration code, harden your infrastructure and pick up the phone. No account managers. No ticket queues. No layers between you and the engineers who understand your models, your data and your entire stack.
We didn't read about these shifts. We engineered through them — building, migrating and securing production systems while the technology was still maturing. Every era taught us something the next one depended on. AI is not our first revolution. It's our sixth.
Server rooms, Novell to NT migrations, the first resilient networks for organisations only just beginning to depend on them. We learned infrastructure at the hardware layer — where failure meant walking into a building and fixing it by hand. That hands-on instinct never left.
Physical estates consolidated into governed virtual platforms. Organisations were suddenly internet-facing at scale and completely exposed. We built the first serious perimeter defences and learned that security couldn't be bolted on — it had to be engineered in from the start.
While others rushed to lift-and-shift, we re-architected. Hybrid platforms designed for resilience and governed for cost from day one. The operational models we built here are the reason our clients' cloud estates actually deliver on the promise — not just move the problem somewhere more expensive.
A natural evolution. After fifteen years building the platforms, we started building what runs on them — enterprise applications, API layers, systems integration. The same engineers who understood every layer of the stack were now writing the code that depended on it. Infrastructure knowledge gave our software a foundation most development teams never have.
Identity-first security. Detection engineering. Infrastructure-as-code at enterprise scale. Pipelines that turned weeks into hours. Our software and infrastructure practices fused — security embedded into every layer, every deployment. The disciplines stopped being separate and became one engineering culture.
From GPT-3.5 through Claude, Gemini, open-source models and autonomous agents — we've been building production AI systems since the beginning of this wave, not watching from the sidelines. RAG pipelines, MCP integrations, multi-agent orchestration, tool-use architectures, evaluation frameworks. Every client conversation now starts with AI. We were ready for that — because every era before this one taught us how to engineer systems that actually hold up under real-world pressure.
AI doesn't exist in isolation — it sits on infrastructure, depends on data pipelines, and needs security at every layer. We've engineered all of them, together, for long enough to understand why most AI projects fail: they ignore the engineering underneath.
The platforms change. The failure modes don't. Decades of incident response, disaster recovery and post-mortems means we've seen every way systems fail — and we design AI deployments against all of them from the start.
We've never chased trends or diversified into things we don't understand. Thirty years of infrastructure. Fifteen years of software. And now AI — built on all of it. The depth of knowledge in this team is simply not available elsewhere.
Security-by-default, AI governance, automation, documentation and measurable SLAs at every stage — from first assessment through to ongoing operations and model management.
We map your environment, data landscape, dependencies, risks and AI readiness before writing a single proposal — because decades of post-mortems taught us that most problems start with assumptions nobody checked.
Platform, AI and application blueprints built by the same engineers who understand infrastructure, security, models and code as one system — not four separate conversations with four separate vendors.
Senior engineers build what they designed. No handoff to junior teams, no offshore delivery, no surprises. Minimal disruption, rigorous testing, security at every layer.
We stay with you. Proactive monitoring, model performance tracking, reliability engineering and continuous improvement — with the same engineers who built it, not a helpdesk reading from a runbook.
Every engagement is led by senior engineers — the same people from first AI strategy conversation to production deployment to ongoing model operations. Every team member is ours. No outsourcing, no subcontractors, no offshore delivery. When you call, you reach the engineers who designed your agent architecture and know your infrastructure inside out — not a service desk reading from a runbook.
When you engage Toltec, you get engineers who have already solved a version of your problem — whether that's deploying autonomous agents into a regulated environment, orchestrating multi-model inference across hybrid infrastructure, or integrating LLMs with legacy systems that weren't designed for AI. We've done it across multiple technology generations, in both infrastructure and code, under real operational pressure. That depth isn't something you can hire for. It's something that only comes from doing this, relentlessly, for decades.
This is what experience actually looks like. Not a timeline on a website. A team that already knows.
No pitch decks. No jargon. A direct conversation about what AI can do in your business — with the senior engineers who'll architect and build it.