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MIZANIC

AI

AI-native engineering, from the foundation up.

AI-enabled product features, AI-assisted delivery, and custom agents, engineered for production. Our own agents already run in customer environments.

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Four modes of AI work

Four shapes AI work takes.

Features inside your product

Embedding LLM-powered capability inside an existing product: search, summarization, copilot UX, structured extraction. Wired up with the right evals, guardrails, and cost controls.

Delivery workflows that compress timelines

We use AI in our own delivery pipeline (design, code, review, ops) and bring those workflows into your team so you ship faster on the engagements you already won.

Custom agents for the job-specific work

When the off-the-shelf agent doesn't fit, we build the one that does. Tool design, evaluation harnesses, deployment, observability. Engineered to hold up under real workload.

Web and mobile, AI-native end to end

When the right move is to ship a product, we ship one. Modern web and mobile, engineered AI-native end to end.

What good looks like

Production agents run on this discipline.

The gap between a demo and a production agent is engineering work: eval harnesses, guardrails, observability, cost controls. It compounds across the engagement.

  • Eval harnesses tied to your actual workflows
  • Tool and capability design with explicit failure modes
  • Observability: traces, costs, latencies, error budgets
  • Guardrails: input filtering, output validation, human-in-the-loop where it matters
  • Versioned prompts, model swaps, and rollback paths
  • Cost controls per tenant, per workflow, per model
  • Memory and context architecture that survives real conversations
  • Security: prompt-injection, data exfiltration, and privilege containment

In production

We ship our own agents.

Consensus is in private beta with select customers; Naqid is in preview, running inside live engagements. Both are products of the same engineering practice we apply to client work.

Agentic DevOps

An autonomous DevOps agent that watches infrastructure, drafts changes, and ships fixes through the right approval gates.

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Agentic pentesting

Continuous offensive testing across web, API, and cloud surfaces, feeding findings back as remediation tickets.

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AI engineering FAQ

Common questions about AI engagements.

What's the difference between an AI-enabled feature and an AI agent?
An AI-enabled feature wraps an LLM call inside an existing product: summarisation, search, structured extraction, copilot UX. The user is still in the loop. An AI agent takes goal-shaped instructions and acts: it picks tools, makes decisions, and runs multi-step work on its own. Both need the same production discipline (evals, guardrails, observability, cost controls), but agents add tool-design and failure-mode work that features don't.
How do you keep AI features and agents from going off the rails in production?
Eval harnesses tied to the actual workflow, not toy benchmarks. Guardrails that filter inputs and validate outputs. Observability with traces, costs, latencies, and per-tenant budgets. Versioned prompts and model swaps with rollback paths. Human-in-the-loop gates on the change classes that matter. None of this is glamorous, but the gap between a demo and a production agent is exactly this work.
Do you build models, or just use existing ones?
We use existing models, closed-source frontier (OpenAI, Anthropic, Google) and open-source (Llama, Mistral, Mixtral via vLLM/Ollama), and engineer around them. Most production wins are 80% engineering and 20% model selection, not the other way around. When sovereignty or compliance requires it, we deploy open-source models inside the customer VPC via our Marketplace Private AI image.
Can you build inside our existing product or do we need a separate codebase?
Both work. We can extend your existing product directly, embedding LLM-powered capability inside your stack, or stand up a separate service with its own evals and observability that talks to your product through APIs. The right split depends on your team's appetite for prompt churn, the security posture around the model boundary, and how AI-specific the operational tooling needs to be.
How fast can AI-assisted delivery actually compress timelines?
It varies by surface, but on greenfield UI and infrastructure work we routinely see 30–50% compression on the engineering hours that historically went into scaffolding and refactor. The compression is real but not free: it shows up only when the team has the discipline (specs, evals, tests, review) to capture the speed without dropping quality. We bring our own AI-assisted delivery playbook into the engagement.

Further reading

AI engineering, when production is the bar.

Send the workload, the constraints, and the timeline. We come back within 48 hours with a delivery shape and the engineers who would build it.

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